From d4db3104e6818baee9ef109124b095ede8ffeefc Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 18 Aug 2025 09:28:43 +0200 Subject: [PATCH] typo --- .../.ipynb_checkpoints/E1-checkpoint.ipynb | 2 +- doc/LectureNotes/E1.ipynb | 2 +- doc/LectureNotes/_build/.doctrees/E1.doctree | Bin 38644 -> 38646 bytes .../_build/.doctrees/environment.pickle | Bin 198867 -> 199672 bytes doc/LectureNotes/_build/html/E1.html | 4 ++-- .../_build/html/_sources/E1.ipynb | 2 +- doc/LectureNotes/_build/html/genindex.html | 2 +- doc/LectureNotes/_build/html/intro.html | 2 +- doc/LectureNotes/_build/html/search.html | 2 +- doc/LectureNotes/_build/html/searchindex.js | 2 +- .../_build/jupyter_execute/E1.ipynb | 2 +- 11 files changed, 10 insertions(+), 10 deletions(-) diff --git a/doc/LectureNotes/.ipynb_checkpoints/E1-checkpoint.ipynb b/doc/LectureNotes/.ipynb_checkpoints/E1-checkpoint.ipynb index 1fa343a6e..5a9b9a23b 100644 --- a/doc/LectureNotes/.ipynb_checkpoints/E1-checkpoint.ipynb +++ b/doc/LectureNotes/.ipynb_checkpoints/E1-checkpoint.ipynb @@ -19,7 +19,7 @@ "\n", "In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants. \n", "\n", - "If you are unable to complete this weekss exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." + "If you are unable to complete this week's exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." ] }, { diff --git a/doc/LectureNotes/E1.ipynb b/doc/LectureNotes/E1.ipynb index 1fa343a6e..5a9b9a23b 100644 --- a/doc/LectureNotes/E1.ipynb +++ b/doc/LectureNotes/E1.ipynb @@ -19,7 +19,7 @@ "\n", "In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants. \n", "\n", - "If you are unable to complete this weekss exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." + "If you are unable to complete this week's exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." ] }, { diff --git a/doc/LectureNotes/_build/.doctrees/E1.doctree b/doc/LectureNotes/_build/.doctrees/E1.doctree index 59453c2aec7ea3f3243c2a86aa13c5d1f816f69d..8b7d3d33f1c3c12cc9cb640efe67f10e96300dc0 100644 GIT binary patch delta 57 zcmV-90LK6HtpfI~0tA2smFuwt&j?to zYayP&k(_}*7~sN3LLej%2n4urArJ_J1W0ZYZn!*iAwUA*6$p@v1BA!-uR7=SIX%-W zS!cF&qn|z7eY(54y6RM&I#qS*Jb&&h2WQNjLH`=d{7N=gIy>N%PUouSQqe2bnvI@) z^;{uyz-W3!v;PIn^Uc1-Y`0Qr9&2=G%IR9w^D?<=v#~wC32f24P|1~hZ!lHYs;pRim!nW~j1CJNqIy;dvZ3wmX1 zx?Gu5ZC%Alzm_V22#fOd%4E%}rpC(UQ#e&1vfh}7PY#|f66NuA>K*jzYRN6&Wgx*S zao+%12XZA`5$D0jq%xkLn=p`}x20-XuZWkdkuQ;$@TG!V^UgLKYvrY4y->^HO{r47 zIObJ}_+Ddv!7WYH-3fr0MXXv_D5u?mmjaE69*r(>?l)b0N4Z{2dnvD!E@zl^3+lD; z zh>kS~dS=U|oddTF`nj5ythnh@K++$S-=aT#kO>gco-P+F<&q%y|FaiA*7+A#CNpl$ zm4p@Smfsw8#rSHk(N(FI^N=e@?{RVDl-#1{l*%;+{U6fg)-FueDL)II2!N!&Gv%V2 zLw8t@XNswDa1@=+OI5vb5ATE)@gdS4II@Tz6#bAmw4^dQ!D=$)*Cv58G(Hbr&Bn4U zI$(i1VbE>B2>F3f(fV?^BKiY5%~GX?Qe)Lz25RnDWAQ1^tEA3|@rPd7>(?t4?kGVc zG~Xgp?-+gPJ4pR4V?^a0soG?PJJc!=ELX~SXCWfRBGD9|h)x6m-RMFouaYa2C-7E2 zLyr_^%a|-Qw4sBg#tZHQQF}=R#B!^X=$?XlvnrG5ghI2T_o;CT-?gadmMWwXQ}t>A z;OCcewMs61YLZAOk5d>esCxmI72ziEVFE(~F3iJVKqKab#&U6#^1U>UFxXTusE^ES zESBxi(?z$&($R%`h{E{BQE}wlNK6+(4W=B-O$>0;+?qQ^Oq_p`{&G_{Mh`DJhH(W$ zW%rSsU&B~JSL%)RMfYs3h?mni=aM;)D&$J|@k$dw=2&AXFv!aBi_`!ykDI(lL3VUA zM)6oq)s1xT()aRNFfg-v>isc(`>R}+T z!qU|oYZxMiXoQ^!iH2o}!4!?XeCL@ew0LQPbSmI=W1!7FhWp$CozV9SHf6Uomg-Yw zEkWG{w-{XOgBb?XIh{?7xz&^yl9rEhi&M+ye1|NffdO>!B?T9H9`~^cEBfIwQo?LI zv4J^GD!9?b!($E>=;AT@)M^$zi?j*`?IqHlG@9lDYnZhc$pR?2!JQasyb9}h@o2rV zh$aazZE3Le8Ccb;lwr=5q2cJJ#(mjZt>WK0I7n7YB|GWo(*D3iu7CJoRbh63Y7V=xfXS@2bi{7p#CSr`dCB%%M}8+)?lA`RIS#A!Cg zFEkdprBWH>L-&UgAnV12ojzFuf-KO>C(30^4^lyEibP{yV;#h_?&qNEj7w6ReM1edEUhZrIdD4$7sW+P6CT1t--gE);w3_$~IYWVRSf9=ieLeJ$J?0RJQ%7xR}kIA$M^!3N8%@MRaP?Q&uj_NmE{aX2ps9{TpH^Cf_z`a zgLxqO{e{K~zEd})xK}o2CSY)hUWo2rle+(+Y$N`r#B2dj1qdEHL>s|I`zrA~Pb#P> zB_|f>@&Z4SN{m%-j*uo7I*61#4SMS4WAc?IfMfD`ex;s&U1+QkZzL03jt+jkNR}k9 zkzd2tge492#`n{pMROW*`K;1im*!2yN=*zd!1 zX9lb_ZmN1km=5v|;*R+Ox{s`$+9aEPba|8UEWyJIV6l|yJn6%n%B{i-6RLc@acdS6 z4H|l2$rf@&I1yOcuQ2Xl>mcQyo*)h^H!k>?zouy@Z}etzrw4+Gg#)_=6IA0)VR7<| zs`-rPbkCTf=2i}@1h?Xy@FotNt@L3M2}7XYIoIvrUlp#WYRM6e1JLJW+Vu@SG_d8S zr#DtT3+z-`R! zL1#ZELSyicp+~@j<2uwm9WmNF7_rj@Obfltfb&4EP;kaPr$WvU^au1IeswMD)|@k- zgS@jFUj_|RFYrAjXQJwk@!i~m$N-RC)~Vz?=vQJoUt&Pw$#fIAFCyuffOZ_u!gTUBax|c=v%52a?cObsmg2K8+y}WRQ_i zCM1Eyh7C~|n>XJr(B^y1PY7^JS3L}{@(V~-p{r=R4FyN4pRGLfM0h@EZceXb@FWR} z5sDJj(*-5)x{`MWiicI+=4bV7c5aH~CYm?*_YG|6-`vk&p-aF~sT?l@FQit9p{-q| zzYV(hYET|}L3BhSCUuL49?{$rUw#lqe+E~$kLimO<(k2O_KTX}#x?OqBHp2LP4q?M zozx}z^esw=h{q6v0iUdozReGpHusy9<~{}}1a*K<`ULR>C5d>%Byrnwp5vH^i&KfJ z36!1+f4+b@K+dT?dXI)BbX zJ01yxZtFJmEd6VHzWoz~UKO`HAu37QdZmp200txed}4~J9r~S3rc81tR-U)wD4$eJ2t7##xZ@6I z^X4msE~GoqI)}+PV6eU%=rII|o&!}0?K95Ohk>c&$T`X}lQWSJ|gFK3v)u5^Liwpl6&7_!zytzHM8b z*8xJhREBeFp>VFGKv)>Jb7&Tz8_BT%&y?v%St9RO ze#Y!)8{G;7IdBdruSG`=9H2;cI!^?%`|5IDC75JH8|QMgk>UU}SE4t_htu|g zoD&M?HKLnp8r7IJ;J`C0=10PDrhKvR?TYD`oH7}%QSk~jl}f{j&XWf@v7-QKMmiEf zTAK2S3Dp>aUea-Ptwa)&R`PW7WCkpdL5=JO}s)>hB&z-$)jVg=CsAA^(x^o)a+R zOP8n@NVg~XqGvtahiLG_{Rj`U!<`<<a;&RQ#HlVtLKT=$Jj>1Xa%ID9K{Q)5>x9R(hr79XII|7){ zqnapY&G88^Mzz(+d3~|5Or4YH$|Qpejdek*jJ#1yo@YYrB3rF417FOGWyrA%5n6lj zz9UDRV?##|IJX}-I&}ERT|>jeCk~8`-U%;-eoBPcF{5^i=!{2(jvcyh=nx(`a^$W; zZU6yr+`fpz_2}&f{2LU93Vk;0=3rAKxjry6vNYwT--$3UDRdjma+T_5nP zTq7DG@(7!O(kadZfbX_mEP)_vMPsd8DR`&70)>BwbkJd7<_dAK-Jc@b*$;sZ4<61c zL3b=Sn#m=Dm<)&XL^I|4RLBgrAju$>zN2+qU!QkgFB&=GWl($+)BVLA24xjNa~G8C-Gf8+LZ^%9&w z2u@M|(|6p6S@2m>|Hz)ejGJaV7`G6A2;TWHTv4_1!(>E*bVzxbfL6+$dl-W4ogwEM zCQauahCGmI=TmI;{5d%q`RDbE9?l}DSmnf&a{+9kN+mO{k}mF`e!De%6?%YN+Cig+ zc%yRw=Bzz2n|@=JV4mS@3qmu!;u44Wq+%oWFrNYpXhg4AfNoZS3-9`ObPS2ied~d(Epbh2YCVMAT@l<%%8@Q*-1xv-M25dWynyBXiAGibIsq3U{3b zD;tfEob{{?hz^puBfmd<3k5kjdeYyxR^L;FWMhnhn=7SII&@d{BODYo?_;v0>LvPo z3P*Vvpp!7*zy_!WF#ME@xIwji3YkF&w4ob|Fh7g@#l|{nD7vTMvlL2$vW9Sk_zr1W z=$c$oGf0+0J_=seD^y^Plro3_z)-=^tD7_4X>zx7 z!o^&#l&qr%tkIX{Q_4nf%-oLPufEK24cG>{<&+58hTcJQbf^%rUXL|a>w9@NfLNwb zT`(8)B_!{`qNx^jdd4b!k)MMZF6`E&gLS_;i0JZQHb)7hP4g8rpbW+eyu-c>xIWC`KF*ol~7S842dMY~!&AsjQ`P z$vq8=&1^P@u9ap$5sE>D4b~5c3tOE5Hv>uGY_5S!wOq+?dPQ&OIb_B{A{Xx`M=i}Q zsb?e1&~{HMOF0(CjpBS==thbwr8p0XpkBx?f_atQmtTxO7xPQ-dm+D+e|z}17r(f? zO#E3c{;a^C{7U*+LqBWr(^zRtEk)o&&es|%I1g5+1jL+9;q3()02Xum#CTCPtl|cw z`6>PxX}1f>QYfX!&cP=l^SDrODVYb6`!3=eCF&qotUiY6bq!sJl8@%sT>li!|H!yt z*s62*lSs4R{HE31j$w~xV5j_`+W^07_)@qz1-%c;e+-v<_%Z|{CDLGo4n`UQLL#tr zF?p^bnP@K0$bf*jowAU`7)FUi;^q8JG?I$Z5ut_VZ$bbVyc{*Cr>A(L$fJUwD4Uy< zKOqbf>f-ziox&Uqgc4aSg0kv@Ux3P_kO@#&A|8Qgq>30H$@X5uO==7k(;4od7^7D5 zH6C(Outigl@p{c`j(oDQjIWpsV3SJF9nCt-2JJNS9R5Gwd|bUua!}@MGRg5WL4pi% zS>|*ip9HR8ToNB-UK06&Q4eL{AYvDQ_%QKMrd+39CTF!6I2e5qSjG^+lWM$2P*k1= zd7}A!)Z;_ll}*vH@>vYqNUJD~rP6Rvl_|54dMT4)DO!ofoUV(3n{rN&LP$xXE9e0a zoiKDg|1)~cVq6m*02=KvfG3l6%pq#YB+MIJgC$wQysSEL7DURy!@w_FhAZA_3?rnz zXmp^lf6cQVa+8k@B_B*ZA`iADZ%I9}oy0qj+F#=oP{)_Apg}F?g}DO1~y_MLc><=utQaDV$$Q@zW%wq07Qd zA%$X~q8bIf9z0O=5O>8SiYC2;0kz3qQGqxkl@^I@)E$H~9TpfghR-Pmgm23Q;VV&H z6^0SiX-YRP$L$P2IA7VQM`s(wxPyQVqLI{BopWTNKwq@pEk>P{cVjS$xSLQ>wy;R6 zhPboTtizQe=1Z`MbC`P|!cQ55$jZgle*R#AA*g`)7x^c}KuGkIq+nwkW-feVjRhoJ zNkqeQLQdo@%mtVv;vVE?(>j70WRSYCyhn^*hEr;dz6O^S<((QAsi(*XX?UX%TxFUz zkjzS!Ftt;n?pO*hL~bEGKYj`>xAU;t4;ntZyicL%dIkx#4!ZDr(SiqmkfxuQ zKEYErxMN`Fz^!}UE6P97fI z{oo=0nWeFtirelf+*=$up0DkB#)G%qbI+-vJK#Rz`H*m`gN80FoXKJt7G=G0!a%g$ zxp;Oj5> z%4UAsESMkL@l&riNaIO1s179c4X4E!J|W@fqEUp*_=sgMa0|l18{z8jf+m?gj%!dabEM_}zrM|opkYx>s}q-eo;H84rO z|7$~ll~bjd^L=svA}O5nL}m6|5mq=gLno{@+r^N;-Uo7dgq~5l`&eVW`G_>s$UieK zCPI(E(1YOL3WpBdA1i!he?e~DGoDY`ne1r-U8r4-yn!{vti&_W%E-!(RH$% zK1mZ_4Hd7!umLB5gn1B|WQWm)TL1wgHm&MtzPR~fXg)17Z80>W00@AMJyAzFA zJ|t-Z%{DZK-phW^G?Y4)_B7^U7(nVcs5>9?V3C}C&#&+gUI%uCz_kQ1s=}#^`{}rw z4O*XP2N&vN`5X`t3l8%I91`-3TMxS`VCN$lSgftlI4;6q;ua>1b}v6kAGGg;cRGjc z1Nb`OL9WrNg0ze>p74AZ&Jpt9p7he$QjXkwoZ@fVyKOgc6W^n*sD(WGYt)sUQ$C{>`RHfX%J*xX{gQDC!F^{j1EUuy4l!Bg$sF1Td&JNb1hhmpn~WOgg6 zclK%X545AVD71!6cklTJOnfC$H#~p!PJf(G4+XvkkaeG*; zwLQ`w@rc%VZMP@eZ?8AzVl8jE*375TA#tL-dW*#B0pX8=<(VH#k}cOHJx33a-AVT3 zQeh_JEQp)J6m6`8D}Yw{lGzF63jY(b|IRkE-<)D;c>XwCLp55)XiSy+vbSb$Ys|(P zez^X!w`cFj--$24;hk6_F0zeqa#!|g`61Ch1lO~?WxqZg)`tg_h7CkpPir zT~KYQ;ON=Gk((V;m(u|Tnc&FFj;n8<$Y#}@xoln??#}K}H=H`Ajtar^#cWC4SqT~+ z%~sW&et>2zII0K8oz9+7PwzUG-3Kb2%}(;)$AY`hUF3%6WY6>8=VqVBf1e)!dBMm` z1>}X<7pcQW0N^D7UM~&WelvLbW!aajM}8}4d_{0$GdOx>aP?K$SF1<_F}O=1@n?&Dpo`-#?Up-(j^3Vqhid%e>^tRQ{s9`0o`D|*_20AaQTM!eWVSl|9{~~Hr+eG`73H#jGBQKm z`GM>QjSnHe2}kgn7a017B%&XdfB!7|5pMrzc8ltHf1drAI{b_5U#i1@$$nEF>UBt* zUqEXNgKODu1zr8y+3%=lzY8@W^~Aff?^cKZn*E+S{C@V|)Zve_PpZTJ2w?pr`%~5U zv+RGW!=GPJN52S;ewqE1YW!2xn5XJF7iDvPCQgDYv&@s(I0-P#&k3E)#hF4UKhHer z!bt#czIn31Jn6h>TCdKN=WmAI0;C!4JY~S^doWILB~6dhFx^L+i19%j&CsISS@1Y+F zXoQZB7!611_!xeeW_$6v{LM6^5Cxx(W>%{C6Li<8y6YqkSf{F?mxK$MF&X59@PqdS z58jU>(e?n2^3T8z-|-CT@WbtsIOyl2$8Z!RGjYo~w1{V(qmB>`Kr)h82MSLTEE-k2Mq4y+ z$;zQ2R0hX^-zQp{$3=L!!hL{$1x}>Aky{N7(=Z8XwE0+v0Fl#nU4}!0>kG4ul)1z!}z{{ z&R(d_UW5ZdoL{5%7pwNiaUj0x*MmDQ&>a`)M~c7`bo?7eLj%WZ5XrxUF1*yZ`kUs} zm(hin8&`jej$dIkH0k)2M#JymI8Y|9rwhMpTzvx_ztL#;Jvx4q(eV2?4xax5oQMy6 zb8z$)^M*g9X7#0SHBa7V-u6evmBvb)c_Q;{rZh4TO;MRVFhKKfXP8^SXPmqPC;307 zAKCCuI)0bY@NOI*lUL2;A0+F*G$$7mvc=jgn`Bb~{O`u|`S+M7@5RYQb=&_it~89) zBLuT6BGU|Zw!qO$8R9fCjM%}orNOr%;gAaktc{edyOCm}aBN(oAEGcLC;cG&hs8Ar zXayf{q&5Y2i3&Mz#z^wL51)~LKmEvtKcV9f7!4o9vHI#p4^pT^hSjC4L@Fl4w^8Ck zZ;(!;Ez1?)M0k?KlPc2we~Q-y!_$Y1lg3h+Jfy)y7^rH<{IKz4{?Bld{|Noa0rjJF z{O3l)$LRPkjE29YTN{2(4Zkq%_$3|x%4nEDla-nDBZ1DM*X$Lm`^|Qjs>lEbfaP9I(2zr>*Ymg2eAM$J?7P3^JH1;t;@}; zD{vA_#PTc6ORLP2)#k|>oaEQikNnbgbbO7`a4n95Pg#$Xes$}0bl3I9tqvV;Fd8<} zal&Z0fsSu98aC1KW}~5xj{A* zARQl~ANl=v<2V3zm@eF7Tphu&v@MU|P?+RL>4sy*UB_`8knHJn;e>H@6vx3EPSS<@ zjH~zKSP6O^{xvt1)EsXeZX_ z9N0lWNDo$xI9#BjOSq<*Z`Oym;4p}L1UKx_H|#|y7$z)&vIZ=`RmBw*On^5cB1Y^b z0J_Tn0Bn~>1UliRAtH5(ATO+jF0J9Cqi9l2HWsM$AFqQ?W*os3GzF|0cKSnmW z;g0=tI#=HCz(f5P)Y|Uu{(Pu8xchHH&4I)EFGJ0NNBYy$tlk*h{C8$+aPLRW)*x2$ z_hxGl%=nzy8bmAp%4`k75r1vA2JwcEo2@});1kq(snE;+rW+#S5TB*?`N#;VmZ_d* z{%`5LC6x6)scoT3Uf|pQ7lYGXKn6tfpW$0oKjm9L$q<8-25$Qlw}m2)`7fXil{;zr zOMOD5e<1A`eIrjDBL9Erhq^-kOY}qR&wrVIh~xQxq#t5({wwrDe9eEAeu$O%uh9>& zFaJ;ULoCbxGyM>o@?WPPVom-V^h4~({|o&P3-aHjA0m7HTl7Pu&VQSJh^G1P&<{~D z|6Tea`sM$Xeu!%M@6ittEB}4^A@bz^jedwK`5({^(IWpt`XLJBe}o^>WkDfVl8qjz znq)@vK)N|HTU8oXl`#h(rX>{F^RNhNrb>m;7jq=8h>hc#szMz=Ly_r`1xV=;#i>=Z z@H^suS+_WVrPp{68nT9RaFud+naM6o?UqUS=8mT)?pUay_q*;s-Hcy^dNOK~Y88m$zgj3$~h2Ccnn{TOoS#yL?R zw{~Y;)UD-3sgzZ=idS`@6f2Ut(4p`iT1G(2*Or@QH^oGivy3?om7RV0610nzH=mPY zG0DY317vj8QmW`@y)mCw5a4SBD`_BoqHEVDe)oyrxqV(Rer-qFjeq!i+!l>?gY3*7qL1 zY!22>bbaK!Xq9Hd6J786JJFzwi6^>FzvjK-U1|uxyY{_Zw3%kW6J3M9^*(W@8Ub+U z1Ai~t45QO z9-YaW!&qWn;{-dZ!R=$))4OjO z_jc@Zx2N}P-?Mk`EqnH4cJAG|E3<9;uJrbuH}ASRy?eX6$J>6(uJqo$dvD(9jc0ac z#&+-6o7u5vOq5le3VLRI@7UfwyT*3!+L_+t?b_~nuDjdwc4W3^_INk%_HG&9lis%b zmh|4VJ9f+1&hhkKcgOh7n=^Ye<72mM^U@6cWjtp6W|Id%{Grhge`uJ)9~zzThlVEn zp|J&jXc)sE8pH61h9mr;5fXoBn8F_#5%GtHApD_m3V&!w#2*?r@rMRE{GpKxe`uh? z9~vw1hX!8!q0#wLFu|S?Y?l3j#p)WhEDwDYt>6`8)AL3~nq&%?4USDKdv? zQO~Lr)nKOT5TyB#!A4buvmRy4t`S8Y=tBF&`D$H=e8uf}MPrWaU!pT@KogD0-C{k6 zU-8mpqoAjX$_%^^1ZMXDW*AtqX4LhcCx^hs9O1g`66ZUSiL5mrIjvFwcqGgr7X zj}Fa2wRT>{L<_R(_54PrWd22hp!pZ$@6nInn1399N4C}dd?Mm{ex7=5?tRJaeZKcczzMCCnK(}Gw3rAalJ><2L)^MgHhL43HaCI z`nHJc?rd>=4z6#Hxc<#K;`%&X-w|>BwUYh|aeZg|^^sSK$7ka5uJ+4EKPx}42iJE; zTz}In@%(ID-xG2D4Ku~{S-4K42Fcth@wZjLR~+XREzVAqVyjFFWzhc=@TAqJ0agwR+s;4S}DoU zrh$^54^eU|b$N8Ip)R}XSa>s!jdiws^E3*vSx>9D{Q;^Ui8OJ_kPX%%EU3C?gine* zUNBm*j}O-EAjq2k7Ck-ZBt`==n|cqZ)Lm5n@=iHBck^NdjFt1aW&m>BzJmw+F}Kle zR81fz&(3j41X;QqC}8!-T;cP=(R>t!*C({R9}^Y2r{3tQxu~E(b#!DNFf#gSJjj0= zKQaOMUvUV>^Y`!vIR5~D8ofuvN&%fVFHz0^7?<;N<}sT2Cvife`xEi#&%~dfi$A{< ze`erb;J6^DA_2TcX1n$LEL_OX#t%@UA?PwFZQ|{lFlTgJ=ODJ;aSoQN*qkEc9772z zluJA2)i3~_3gNl@it)_%GP)y}yoL~@G4L`7?iei>P7{8^xpCg~qlV0F=KyLduo>-$ z5^$k(^ANHdtdU*Xf-DWlmqB*@ek>P4NhCsziD%MKtcN+@6wDs9#;d0VFERu!gV#+b zuw=Ii@u{Ks9OHyyU&L4cR_9(=VN^ewXiRHgLe#s%8q3}mEXf?X43_Io(2_^7qy_~> z%Et1N5K<%7NX=^ zIE6%$%)85A(?7+69~9L!`jnf)k}_JEp?ZGZ8pru9IFgBZ862HpB@nNEy9{bCvPNw| z3u!PGv9bQ!BI-`)qg2!u@XkFD&w=^jhGO=tNKo|%m&>XdVX{&P-o;_Yw zcTb%k#Ok%EuKIH8uj_94Ia+>bg#D6I{B z6l@96cYOa)f2gCp-WuP9E%?%0;WGFxl>?qqj3G?kZjH&}7EEYXav4m9?lsorZ568} zK`9!IX+`^SF#(4sj@zQ#Yl#gDa{oxgW7Go#D+8-jK z6U6VKY88vhnW9D1;&SBV?V!iITZPr@*RL2?Wu47xAt=pYFN5`x{k)EswgIFNMToOM zx?)7aM57t#0a*-^h=h3em^+3NyI4(mGV7sy920C*MS3AW!{#P5A!L7b#mFkE z&1|6>P24Yo_KK5o*)$_~s49XsW`|qcb){2*Fzv`Ya2X^P&;&}a(^jMNx(?q=euvAv zd5G3wIy-mW>KvxXtz7p?BOMACpcV+N4BzS~Q%7|B%aawv?zuc!4D5)@(^#D6VU)%~ zcCO`VWMMa2o|HH{%<`lu*)^6YJ;YA1JShV9cjZaS*t3q*ZaEJ~)T_Cs zaH@2R8hh-VKoM;2QH=!}Pw~nJh`v|!N(m`~Kmy{^8Df3}ehTf+V1hd?YI3EiDQJ-8$` zxFF<`26D;rI0}C;a;K3LQLZLU1ra*~?@}g$v)xHL2a&u+;~1I4 znqW_znJci3Z#>L=TEdgjmgM#%>gf}jNuX))^$OYA3tT%K>Xz7oh@Gul1Geq}TZfgw zLVVSIowxP6_sD2y#M~Pj3vbda+#%Sec&JfIBF`>K^^n`7aF;EuwG?i*1ra;Do*A%f zC)l+gSvhP@jte&n)eV9sARyFn8)#M=jB^cVlDHR?Vc{N%kNRPD#QEr`QOFa7rqi+8FlRw$#=Pd#x>qR)$#^w_k@* zCG2D21F!(0u{dIDp%E4;31ccJ#FS=tpRL_}nS_onOjDPm)Dk4GlxX(^>DgiLbAB28 zX>I-Kv$mZ<_oq)Yh^y#4SY&#FLt^_ManZ1`H~}wO;C^mOe}3P{?2(yX8&Usn=S2OX zEeI_h-xqwD%6(wnkJmmxoA+Xu-`1ew(?YzsX2JB#E86;VV{PILFi7Tn7%7yB;*p1p z-8v>;X0`KW4N&TE`mmBgT*c15c?f}i+9e9!O1LLc&7fMwSJMQZ*2}=abPJPIz}bv) z+#Vb4`x0()T;r2e_|Tl4Yi;i3C2pP2ip|O~L z-PtFyI&U21*&tWo5&L;d51Z&Ba%vb7=9h6t5fQC<*sTG9J{>_aGe#Hr>D;MYO=!aq zh}X!zMxie(G&64#K_3P8N?e~*&K$c&-cvbHhe%rX)zC^rQ6|Pp;a-J)FJ#00K z;$-)kI!=(~O1R96f7>Eq?c>XigwIS$()YR5Bz8KHOcIhL=f7bxilkLC2xhkrJTI}8 z3oTkt2s&J`xoWGymfu^Qa7($zXu?GEB~tF8KJ`i2r>-CYIWiL2PX84NtQ?i^PSU)H z-))R1kw({Fc246zw*{dU$Di4P(CSNY`bs*sG(a^L)33gS_Szuxj&x4?RtJlKk-8V` ztZ0hpPh#sL8)*N?7TIJ(k^v${f0@%Rkw3QOla|OIF^H>T3d+G_&Pn)!LqfQ=n~ZF^ z$bpxmt2Gg!wFL7XhMoMZ9ic}z@}n-NLp0P4Vl>bF7}GNfiYIX>p_})hB@2 zbOemr75|<@;I%9zG3Vh`8x$g<>Kh)%8L&Ls_mU-n9XY89yAVesEZci+*`Yal#1=#= zN3W!t%YLPY2BC5+Kxiy3Gc?$m3tLCA+llaeleI8RQ_~#nsNv>lUvG|soWxGY?RLZq zW9v@I1on@YZg6ErBy*)CvZEO(PPxIJ4z)G|V{ZF^@jTlOp!?8sY(d1%z3XXU*@q2I zun9^QML&sSWON}YMcdejU3^s1=O4tzFo)Ik7~_$YcnGh8!o!gCdF?#TZKEGSeSkg^+6g>)2Sc3W*sa>_`2Aqxcl9Fja3?J3~HUOHIv?zpw=nJ40^c zU3f@TC`2zdprWBPL(@f(P6JPwhvGcpg+y)Z=Dr^r*KSK-FZy~KI5Nisjx?Z39O+Qv zm^7AQB^gCS*5A~0-v?E{;a@6F)q-4F4l$go-G06~S1|t`?Gx7mj zE@(!cv;`46BX0{Bx#L!(IGrLL$>|i>pjO4BGL*_xz;>^=7V?prs`0t8ar8FL(H)o| z5{e?b0w}5w6_lhxF&RS9VC|xI)}FKFi)QUvTM)6c)(Kd<^H%2=Z82WBg!lK2vGLEy zV%nK3X*?o5!He6O^m47DVi<((<$$tfDAAFG+-QRGUYd`tUok@yU>< z-SBuQJfHy;9){9e+RmhJ+EQ3E>Fc&2TA5_w`O?lak*8z+Ow^cAH4C&x~ zagnrf4VG026r@K6zDzYQ)w*{{RFvasom2dFTZ-E{nKvB}?#Nr6F|lNmH;>AVW2rX< zl^mo-VNF}OqD4A3;%~wL$llSUz$O{SCaJIq)GPQ9#ffKk&WT5CL1@+XpkU^7$D3A= zXL+qGmXVHKuW zOTiiXwBCs^O$to$N7P3Xx%-4E34eg8p#UO+lkc(ZE4sVAOK@o_!=i7>Ty|SUeC>Ww zo0`LVSR+cGn?`B)_UPUhV&lyEge;KFVLStgmT2CRk;wGZ&vs7sPuqgf^7Sdfx#`xr zt2yq<+#x?Pvjxtru-EhFv9V@Nfb7IUEMC_c{xIbGiBljybljT9AEQ$=aB!V?!V@)D~)oVZn_LecD z@vHpoR=LTg?2Lgk%P}et|@oTc-bE)<{Hh z$lGm+tNHU*TM)7HX9@iA^#a11kG0QMJ`)>9mLlIH^xBY&L}YRNy)6MX5B|;;MC?3R zh2&ML!Qd3@*eVC9fWl1_8eV=98!uKRkey1!bXwjXVo5{-#*c03saf(PTM(@*vG5&e z_k);~T7b}4T*m!iJAGYwxQGcWmxDvSS(qM0ESC-&;C)NtGQd|Qu((LPJ!!t(=ob-Y z_l>}(L$iCMEr{6pvH8Ghw}5PKH8nEG;=rvNOkqQkdn7gv^(8!skDMeK_$157A8t<> zUNRD4wjZ`7xR&IDwjg5X*{Y#Tz7E$y7TYaVsRAZ;UTU2>Rbpe&>V(U$Pqx0^kfI3l zy=Y5L&5^ts$ifbGtJm?1k6Hv2DZS)jT2Gg}a`b8!=eaQC6U9Y*{8WGIW}U0a)hjtH#}8t`J@c^s?A^w3hW@)hpCBZzKZ7m=%#H{SsS}YOXwC3nF%| zEZ^tWAV?~Wz1;?XPizcXkr<oTx9kw81=S9~ra$(B#TF86z zh1lpnKar8|Gx~o-Uh`*d38rcOX@AITMS!ga&@A{ZOfsalg_ z>XY%j3KAAY{$GfP!?E#j4~icJ*l~H3VB5T)5~i365q3d#qRG!??fe|FWrmi+J8eO< z^3x({q~lIvA%F!4jm0|K95T7igSRT4DjjpmY)tEH+D^AUK0i%el|DOi&Bs90nvch9 zdyDRHXBdRVnh)E^;ksZ2!hy45Pg2@oI^(6?u)Y7fxG36K7pywK5?0<76&RqN17Q#h zv`eGb8N8-*dcVpRgchw=2$oHE#_QS>9>sByauL*RK#;XMfJvMgqYCBZD*x*7$fcB-C8^M_UlF zbKzQYQqbB3uJGoU3!M9alr87p;R7L#%4LXsl;PMX-QEBEfPJDkQWtDwIAK zXmx08&awsZl(}O)em|4gBaKL5Rzk6(d|IVmb2vqKt3!4=7P?_$WL;<|o6Ddap18x` zDiSsE9H!dAgCX8-jg7a@VBX3v5T+--0JYYDNRq!G8GHbW_Z7!H>=-nj6dxdN@I`hf zo)Y+rR&z*;i0Aj)@>C1$CI&I32{EOy*0}|7yjRJ(UoDpk6@04b}chVS1eF z+p4qA$JSMDOo*Cq5}Bk&$WW<&MkY#kM( z{eKMN>gm`GdaoyrPrEc|Vn03X2#$(GS#13qV#J@v#)yqrqbPTs5~HF^qo2S8g@+gu zL*;fwsoX#7oc@1m3qp&}pV)%XDp&M^I90B-5MOuPt=4EJKb#vM_o|qt(8&Q=fjFF& z@}t;#%k>H5QPP_st{RO%PK_j#r6UokME_>XA}y%@${?l`)ahSHy!0f}N2!9Mnkt)% zAeS$TYcA50sNub2f~xl#QaC?K3VVP{hZB&+wji_=b~A`+w@FtV6Ao;LHulL?d294= zr{5bJD^@1dv?28J5I-WECcA8jsXP02TM(`Muuy4QD+-ab0HLvn%e1*ZaCXxClynlT z!Ktj7iP(C`^?}C|4hvR|j7FdrP5UJxH!ow$X5CX<1~H{toOXHKYm~?&CyjCqurclo z)oW!6vfkzpTgK~!0%=dQgbUwXh>an86Gov-;)#-AFbiBqU;-46ribmfeT8Ks(=2*;C*O3MKuJP5o$wjMPYny?UB zfpiPIdCJiA(vgVl&iC4KP>cM#Z9(XMbQ!g5%fa0zJh9gn0@J9uYFLT^D}D9_a%fsb zt3wl>FU7{lJy?J$v7?Lv6T5(;!qdm0=>WS|&YLlF2K_TsL`3rE7j3zs8T&a~5Uq^0 zkZ!FYMBvf_gvO#~8*LygP>b+J1Z%H+NVA?|t>0fRoh}#Zl+}v4)ur^z%H`8z_tO_f zhNEwf3P&%)TRXJymokVci8}q$r`0WFbWZ!u{$t0&YI|2)KY7|u zrb{JbrrHxV&TsFW)C0C4v;f^CxHavOXi-Ck^0YJLx81q>{=fuX66;umAy_>gVXc3Z# zfb^s-B{efnGl;9Gj|L4TkNU@49n-%RZt%insazzVT1znm)7|siv5|ieZrEV(LE&S8 z7aZXVtto3X^h9s){o2lX^J-fVT2PvTz0NiHO4-lxA{AP{ zcN|{rat1p=9xPW;3ef30h%L8r2wkBRUCF1+7{5Pcgnl$Oc0Vu$yVVB-DU<30IP{DU z;t!xwRQLe=Vf<;gFp0l*G*GvQ(4QZ+<*63g588rg?G_fcr8ZB6;;;arv50AOU48Nl zHX1iVZ1;}kky9PEczzgL*Ft5(@cWZx`Tk(y(i4@x@_k#j>YnjkTM)7HWX;iXl~dY> zHD90@yf9N%u87I1Z>&uefqa7b)cn{7?F0${5su%a5EhBYbcz{7%MT2gGQq|yfP`^4C-v9Wyv2h8* z72Na1Qcmu13cr0)yx$PJ$gG2NofG@4EeI`mb-}Ocws2RBPWm-56GX@p8zhny8!}Gc z7#mAgCj8*#P=e~^A$~-dman%ZrtbW&vjq`5KW;i+MbZUA&zMhmm@hu%>f#@djZHTt zP*Ggt1cg>8z|Isi1zqsGrPes~)=0!u{$sY3*UbBfEr?d;Ss3+tULf?R1qh8rO!ETX zXJY^1gF`2_Z5JESLDz*u`pMY(ir8lY>Dg{|+jdcmgkEllbVRn!zuPiY_lY0af@tN2 z1w(b85KeCk5E_d`wo@A!%Gh+NP{!0=cWb-trW0PeUZpg#R;%n*=ibw(7gSAWsW5Nb zPQQaIr)LgC9~>FkxF;&Iu@!IaaGKN4Ag-d})HCGxTtY2!3-V)e(Xr8s_0i(R%1@}} zxHl@6G1574@3sY@CFp?Q&vY9~Yle`!d=?%sxJtQ>MI<+>v2kN=GD$S0F2bUEg)MV5e_v(`B6j}v-OGL(N{!_} z)l{4e`*1!G8!P)0WkM)PIkP@F&!=LV5%=wl@`Aq4mgJgi@395Z$~6mvMcX`LIJ5wv zvDm~PGG*$ZwQbUS$Sc7D;VL6Xv9Pib?j_%et(TzkOYmkb~%GLBgr(EKF_q*`{7yZ_suq14GY8fqzbBiKzdva8*pUUc)gd?P@%N zGz_ggmqnQ@^MP51_TXGw5Ly6cF^K6GfNc*{;roa6eQ&+$9L!Zwwer$z3k`^av2ksC z;*3CyH?kfl#FB=@MDWpMV}y;f)s_cZg8Bsyrjnp3nPV$JON1+w;>CxCTGEt`$42?3 z3E=_7Ym*ZQ=7#7RB_KyTr|dnpAhduSvISv`Qw*Y`aY~4(1qh9W(9;$mG%dtvX#qld z2sU5JLoj7Gw;qJoi9Ok=pC7@7R%ICD6G)7u)e|9Y^Gfi?A9M3h0=@IizWh({_mO7) zXX4M##h+h_KQl0XKGJN=hZXHrkwV$L(3ppkBS=zc=IQey{Q=L2@(28uZO77z<2M<^ zRrCk!*2!BKa1Rp=FuxELx@i5_y;YQRcB_-l5w24_iluuXc!=2ViHq2cJw|#LvLTs6 z0d{0CmnC*oo(pQ<)& zEi03XLah5jY^>YB5i*5Hk`bY5N(LeZmm)^t&)Sk$3)812Q*<&K5-MeAq;MU^YM;c(Kjl5={qbbtL2j zV-<=GveGlzKt`QQ*`PN74B0|64~7@9018*0dw+*)$(v;`46$2Nl}DgoP2 zWz0K@GswN+!WKg|HkS1n76ZG~q;eC?Gx0Ha7Lgz2*%DmyY|Iu!>^$48GT5+w6xq3y zQRwu6S>2(_-NV4+)>^l=R#3a=VYGH%4i!CgUOo5qed+!4sJFakO7Qy_u#6MV&}|?Be~Kk@|bb4oG8QMGqLexC2AS-%SB*FszH+* z5lj02-j!ZH85>)Pne<>vEq6y zaPQEJUu6p-c0O+%#+n~4g?jIiQD?kbE;=q(B67!a1th6sb-fSMd5jk=hgf=7Y%EPC zG7_m|xqt~5kV?8-rbr@%M0x`$!#s@GdFKvW(rYQb)fPnT+#B5IPI~@7J^wZ2OF{xY zmcyE`;DLMU_6pIkSsg~8^2XhtpSL32#V{rmDnto|8az++9{&MF;{RdkRT0TB6_%hwW zmsMB;m@C1~pbA2x%7_j3sM3GXuLrnTcY)tAG zOfoQ1t8X}lg4McM=`Rp61Suk}^-s1m*KGTWEr_SgHfz&u)q%5KHJzgfBsrjtSIg}c zX%?)D&zkns>)FACXry?(!IFr8);u88p*205K}>1UP5-3vc138cvW!aibNb-M?I)cE z-C6edJR6^8PKcvEx=0s}{W>I_Zy{x|sF=L-=+H!(iKP?FyVb5?O*7>O*402yVZs?mT{IY3>V4+Q}fc}ZxjQtyPQj>DsncNS5Z z(CAlw6$(GaI2W_GWzWs~Og%WOehO-{Eid$8j4 z2d)OETO)Ghl#CdiFU{$Q+2i|c*`zuB9$OHxa~fNN2rK7SXNWW0kQ9N$*Q$q1h_-3u zKgPyJ+8RV!TO!d884tWZnf6ek*=$H0BPNvp$CkpHNnfx9(aIzXFPe7531i#>gvLU+ zdn`a`EJPg10))om{kA@O84~Qjx*@^kN6HhN0yNbUuf#ReGxMYGii`=?qGEy$-rC{x zZassziqpHxl5$^Mq-?arK;}jH?4Q;-NpH6Wp~d4C!IkNrea+*AKH&)9cx>eE5-WR* zh~K=ZDN)*%Tv|Cl+ZIIZ6kkd4iGordkzR@?Vq?H64o#@HM|dFSMMVLB-Ikb|AHQY` zB6fZpSLN!-jw$3$d4*iIT+RgkBVTOjfMTtRY~jpMY|PDjVFU$QLvxmQ#Ky{}!vvD` z>|yz6!~>@KVjl=pYE_6E>1XV)5Qs-qkC+!##N}Z*MVT=Z!?nGd^WYOwEtKu?5k}4-3mr8;`=^umGX4 zSZDho(s2Y-Y{fs99C56kJf1{l_kLORQzxaB0vjaS;@y>9ZOER<=L@ z?gbzaOiiPG{hd?$23u;|rrfL{lcr^V1SjqK;oY`JVk9>CZCVh3CItc}v7?OpyE`ZM z0b3ASeeDw*n{MO2cgPv@YFK{gxD|v4p=3g~NjWx#EJHC(@#bXk<`5?$>M0a#$*703 zoGpk}PFRdZdN>o}X8}TEu_2!Ruw}?Wt?l$FxGZ!U=n)rNw3a5YEYmq!FVwKn4>s=! zk2|lAt)tw8j>0IxG}Wo0g#e{sen16G{ctQ zN9Z4VN&{4z)yed|T`IH$# ztFl63vv7S(_H<)&BG>^pX)>(H2ql9>Vx~@unBvU`f}NV)wjg5XSua_Q#WE({k;SD4 zV`I)Tu{jF8S>?5ve29n^ZM7w$mYsfE5V7+isVk%)h*z%Ea>d+ZqUbj~Vzi-;cUF7D zLIN}z8;=HzIw^?hnOISvPhh28T_*n`@(_>P5?}N0s4a+A{#h7?+O`xEX$ueTf2jIEVqpYp^ys!ADm?cCTf%DIe2+m~MfJ1zsLPdyB6QD+>!!yl zZ`0w8B`_-Jt&+kavPP+%USQIp`MtyzgzoeU8N{@&le}&;E$iYL87#DjA&tH1s$tjd z&9SlM`h>60FgiSyJ&|Y_YAJH-<=wUf)}4QcEr?bgS=eRTv=bs|0YYPOnL)~0J+2&b z_Djc8T1*U1(ztD|-p|I?Lsr29rJPUV?QK>=gtx=9<*Du#V+`Ud>ckuF&-rz%$a{=( z-{G&ojy|5ZwT1K{s$Yza>Ng}#Yh*YrmdB(pBTNMvS;_kG&bja+TM$|-o-Y_V-L_jo z?ACL}o#Yf)gI>%$qF30QdwY!B5*zgpE;Kzha0z&4_|F%N%!#t`-fBx~Ei-Sn1<}ea z3q7b+sgPU?5E_fPRB7K(VWJFwakeO`IgcVfN8(RAq*8{r_rx(}Sf{2|{YxZM3JL)G&Q|3^vltZb^j2Eu?doVTztwn8gi2x6Q z6F?v-5HO|`kwtIz+Y(i)vb$_S#LkyHM#b)`$0t#12+59A&%Rcs1w0w>hqE3zbcXCH z)Y&dx%KFU5#=JWdax2yfV+nVE2KhZyKGDs9iDIi|+SN&7*)l;0OJ2n6V8WI?n!_1e z5Um`xFjKUpBZf>15E_fPES>cSsOVZ{l6w=)W)2VU)Z=VOO}{p_j&xlDdo>dl94-eI z$y{cq9$(Fl5)t-$wJnczH)%46t7y~QW~47W?gTblbxP$kVqI+zN?{9=8N>62$eM;9 zjEyO`CyXTLB;%L_j$)x@*yv=oiMfp`j%H}@$mKfk@0@AxwFRN2>fM6x({2A?(WT&+ z%>CcWQ%w;>XF`VL>MOCa&`G!g5fwn^F_!TV3=K(E)MWEZwlvnV^+j6{t!%Q;pIQYA z3AX^Dv4~3rUkh)pNL8Yx4ZQmaO)a-lsq63rbEy-Tp?od2gD(3l$AkoVC)hJWF5b|b zG9qlh2v~J!ATMALQ|jMo_o^=*&iUBU6Wf%y6D^bYZL!gK39^mMw;C$4D=My$?3}J! zY(eN=zu6XqmOa7UX^=e&5E=^+ySD(LY4IZ45FTMsAIopOx=9)710~-pjuo(vizr!o zE2=JW5&cn2_|QxS22CrDx}`}z5o+kbS;&6Tb5NoayC;+;w&HoYH_SbCeh}#hQ3aOk z(K#J4!{pymQjO)E0ym%ij>(n{F$vYo9v-f5b$H7w?aa7xR&F zBiplv-!nt)6`{n`4r;Wh8iXdLdTImqMd_}ebOV+ z(1wlZMjaV8m(ifs(!AsZ+<&k*;G^{XT3D8o8>VM6(`GC*mL{-N3a_QO48PV;s1a_) zEkLBh`O9VoarGoQNp5==1%e%=F5x5=?jo@_iy-^K2(llGjqFMG9)& zIM>uniIUHe&e?LeEeI{22W&xT`4pYwzodMw*CxK}?8{X%ya?yOr03iZgD|A*pBr1h zK~ANdXdvTn3`k(%jW_8bJpw2gq8ky;K4;4vJqVv=5L1fo^qc;79L7>#K$A`yeFFC~rWYNqW^P$`0(uEX+9=RVVlLvGL@NL{4Fu92Z!ciGM&c>6=I*_T$%r zg&rZUMalN-I_KMKY(Z$*ewASTbk7?GA{ZRbRbZd~P{w8H3rX3>V&mvQ1V_U;3+(-K zGcHSGn1vAwc|T%He=T|+vIP-43%7zfSYZVbRjLVv6eYj5V$8+9_fC)^6k^~vV`Cr| zVN0aQ7-q)|>e&izVZ;SWQ4zE5k;vfr*KJ9!x%V|&5Ut#^7#a28D%7_H2#v*Mj9=TO zJ0pj%nMe+SO`{LV)jVvYjn+J`0`n9ZWqumB4Js9n<$b}U@>-~it=<@y;XIN+kpae8 zcQLsRh&R>qCCZp-G0K6$$J)Q(NqNmwAQ3^^75In_jqYAs5V5nqKj0s0cy^(h^~13Z1wo5%El3-H7LIVVdVM9(Jve{_Eq_Tq&Kaz^WprS-C_OSx^+c z@A&?q{*ZR~>NNF3`trya<{w4HFu!cuVRWDS2L^E!9T~gSCb24Pu)iGaWKph~{;p}0(Er_Sg3G1lI z>XYR&ZZ*TzkQ^u|${uX1igYM8eymB<#QRxpDrqcqm+v>25>ZlY*p{MNhYc}^tEj`S zJ&Ba(z>|YIj1)48q<1zpdavh2Dta}MKq8XpQCi9Coa|$^Ahax`1m~t(E3J6|tMF)i zQ6Xy*2U=$k8nYX*@n&t}j6fi%0?{N7jDRT&Q9A0PEm5^NJZ=jjcD{@Z=iG@hwwB2~ z#^U1e24Nmp6PN%(kyCnEd8|eW?$9DgX;sD;t@~kPENl|IGd5NpLHcEYGpja%Gq|a6 zP70i94U528Xh4NC?hJ4aaX&&CzTK8}n)`3H1<}fViy45PL5!4^d9{MdiUD*atETkEvg5IIgvXr_iHot3xCai6;Jq~BXh$+g z8#OP;cTU_1TM)X(X9RzydtM+D3t@d?NALYZAtt;$HYTXVLV9gG4jeS`)6Zd<}>+W)aFh*sKLn9te*7b?yIgvKH!3%uuE z==-W;Y@=-!8~a9V{h&7?78@HcHb(!5_~$>_GE(=2uh@cU<$?txbzcxpehUy9i_hD- zpa~0vbOx?&NN4G>OYGZr-Hp>TL!fVp4Ay)gDp+$3-rAvMzlK3vMYHAFgC52#csI$j ze967=?ud(~jrECf0|{(sSFd9BT=K9bQ1z4%d|4c2&fMBL(f8Vd(89Az@NK%ynQQl> zetWrqLOkSCb7Yr4CMXj!Zrm8TL*?27BwE;`&CSJ8&dz7ql2$9&hipN_&YiUSW*ssZBnB;P2}wSO3$_kdJ=E?8bf_Tb2v35nQ;_ayl?j#ID)O|{O6;$>q zg`NC=8yinnCyKN?7Qdd#P6e+wSQ6o@`)gZzYLZI$}|Q#>V7B)G+_u&N=ZdTM$|}z9E=7-NXEf zp@0xmY}K-CQ%nYJV`W13iKvX1#aJj1;lWr6ggUf!7ukYn<%h+zN6(yud|H6eSX{>V zvsRf7LRXH~TaKYOoxY<-j)xP}_Qlpkt`j>hMJdo^u~0Ehzz7ff(`?zSb?EI3VoDu4 z?F-^>=W-~BLX+#fNJUV1;6Ujm@}t*FSO8S@VUP3fm}hg)aJ<7!M4&Mp8&hsijH6~t z5=fGNosdilB-z&<;3#(RR5;Q#4u%@)HnI%cvpZ+nBeo#4U_EFHLW{TPI{zibdwAHZ z=1x-*3-v0Blu%`_3JSq-ccNqxu4n9;IzZbl*nBPH6+*-KD`M+y2ND^HEO##s>lny# z<_niQcEz5bt%g=oAZv8Hh(#nXvt_r|6fa>AQ)-IoU$uK3GZ7p+EqAIWUZNBQ@>;6( zLO31iJ+ZOpdchun2AftugDPU6+A5=A5TnU0 zTn!q10!6R9>Q-kwSH&v3Ho5b#ez`5>;0v*_Zg*l#VwXg`Tw#aB)j$r*HxWKHiEYP@NZ8lpmE z#OssuK&AXF{N@WFSpLUEt})v#to2ulK}<>b>9^Q=f(7Lu9(Ll>%gm0z4#VmM zqgcUcxj>%a5SxZ#V^cq({R$yoc828$K*+4W=1YVOu0=#O@3bYkmZsZmLB!6ro9?5v z8CsI0ijxur7k(jE%!wFem~mcgjJqjOC!DlwNt{$M?WBp4!MccG%9t(PHS1EgAf7Vo ztZl*#qqQ=emZ)KtZYy==^o<@G4oAE$#>O!RBbCCWoo)U^#vHFAJlT)i(pagxkR3Msj_#z=YCtAqzf6Eq$H7ebVevLxT!IqlzM3qp&? z8*D*nOH%ZJI4wzQLG3*X8^2Vm7vY2TvDD(wFn5nv&YUrG2K_tV6#jvKh^?P2OB5vv zRO1jStYMjq$o2lbEl0FWeuhCzDU;LhtC)Y{=qLvTL(=!3u~BtFqFVGh;9!c`{3tQ| zN$2GJu`LKKW4) zn|r)sOH|F5qAiHn`Lg7MS1Gt@&p|12WOasg3T$E3xrtdjd6VkN`v*F9R{XKdVA4m`+7GU>CP@@k_QW&|LhY zEr_SgMeFIzPORN8)HCEax|9ck%X3Fc+w3iI*@U}jeZRmBPIEOYZ)2aRN)jcKOWK(^ z1K4(GHvWQ{Ips`f+Rd=LubQX=qeE$sI@BHT#~SXGC1Qk}qn^=ORPMA!}Y}%&U6i^=huki725I zj0P3?z-p;XJxy-pynEKPdX}~e_|t9KqqX%hTM)XFoZ}hUmC!>P(7Ka|t%(t@^8p34dRQR`ksWqxt}ZK1mC;%=`a;6|ZG5Ib*10g# z=e7uQn|I=0Y)Ps`_@5cXlysebhj=hFw`J(hzbPilH5Lb>FTb>nj&t$m4$bLVwji{a z{z|Z7x;^2&YRo~U-5LrqwrI?oVx#)91lJBEgC}@%8|nLO>888zjkX|SC;hd@t8jKc zTBjToIc`zH`b4!{FNK{w$718c`UI+VY9x4Rk%2@TLq=?=sTp#&Er_Sg5NoZ@3v$z_ z`0RTQ(i9vNsBz4s?QnSD(b%}MffwWoM3O)x2}F_-ktAi0`^`4alx)eWIdh6ZTt#i4 zM8;w*N9iE6*cQ{INk1nHI2j}3FaV{>XE4l$2GWvqs<4P~;Oz=txQF0c;M!N{S%a&?d1m0l_ zqLuI##;~@hg&pL`pe__iM-K&2lm@?f;ZS{RVd~+!4D>zWdjaA)hYq#DM8w1uLf0W*zlzh;;J3|4`uY@ut^U zB*Hx2c&;Fx&OZx$$UmEY#_;1c<`>6Ht+cr{x!PNv=uQ*<2QPS#xO9?iqDGpT+ZyITMBQSo30IUN)D>cqKTT7t2@5hKr5)rFwA;)_$fr0_OqN7y^}Y zsW92RQ2+m{`x5Xtj_XX`Bme>+0G^T(4LPKEh(i!JsEgt$)4UXtx-3eQo|y(P&ABuO z2{4v#TY%Fm>66E6$Lq~Tj$+G+y>jG3aU45}Eo&J$i6eXCl@%qica>P5BVYNVjpKOj z{okwVsc!UuM-57{`GMW9x_e%|djG4t>Xq|o)0{a6=Qt}iYpjQh)z!_n2PLK{Rest5h zlfLKio|B*$h~`?8O(=g6md;}%C28JCiI1GK^s_hOVml+avXY%qQ0{Kp2wU7n`PIl4T9ODM0c*oyncQHvBX*0q0*d7iVR*(h@gWvfJbEABp)mucY~hnN@Od z&c&4J8m%1FP!dD%8Z1;p>07!J0VDrl?2LdRinBavOjqBS8( zzAPQtC!6g1ZHpQq!2!De0C2N0*+S``HrXO(PJdKy=JdyiwZWd!E6n0m?0e2U?Q&Vn zkPfF$yVRyi=hd4kT?H{3RO)hF7AhlkFpF7V{1iKYbFhloI$g%(Avsg!ie3759rKL8 zfVlx{bef6ZU<{sZ(X=O(cRU^>@!Y(6QeiY&O69|^j=QRBb(OCMWQKKFTr__AgJRp%0KcJMQDU+Ejp?WEL})QnQ{-gx8A^E1R#BL`F$Y z%MEWgrex{$LPRc9O_BR`T(8)AOXFI;SC@q{`?I<%R4YVa-B2sUP~o}kNF`gC;1bi~ zwn1P*;*-WKq9cmYl)?nh3(1Im)=FQz zEMBl__P>nb5|!Th(XuDEOud3R7=$7F5rosVS8jh)G zZ2|f*aQe#z8c_Td+e|gKH%BuSYz6V1xXONhr9d>74adGhAU_!O>a;ZwvcabKN?jHz zcXa8pP<5Ixidm@BG+6kg;J%&)3#E&H(d}`{{(YZT|6X^QZrdKQz()G&3}gUnmNEdP zXj+dwG?jL@t_7)L{?w1&ewD%VPsjyR!r}DPS7ZT!J}EY4B>gL(3^$bt}-{#VjtpF45Ncn!VT_jk$Sl z4$02XFk zgzm}7F(H8sp*cEWNS+swN|%y2@*Mj_;ix2F<9}fclI#5rjA~LcKIHhAq{Qw}Atl%J zo{%`D8AAF|tB2d4H;&uWx-3-L_$2}TY_Im{M7f2oKp7c`T4Fcv-5>0m{LmO`U07}F zMNMt4LTbb}hJYMY&k4x)bQP(JtnccwFptH)@dBoI#X8T^RtB@{fgH5Fdv;pHp<2T2 zirOmbn2b7*FY1h%bwHd3+rDkOER18Z4*8;1pYO%8++rF#ZE5~_e)5GSDbfN3G?Fp;B$sV@7;UX>C}5|M z^p+27^7|-%i!tPO(x9b~Gv<@C)^1R6*0vk4n1prkTq&xo>WtOgtBa5V`K&Gr;~=*k z1D^S81XyDkU9d2G#29Aqh{Msw03AmgsHEyRr+ipffeM4))@5NFgVp4pBbmpkycyAG z7?E{TpzJ$m42?DHEXpT9twstKfOQ-cepy$k3YRbHvY3vG#(0w&vJzb~4Him@59zi@ zl@y{M-jEb;Z$t_#j2U=!2Va)+I$V@IT(6MV6I%D@uQ^(`&rWlh5;kp6B{Y=MjJO(X z3om9CGY+N9e&2rO0c>$7VzD56nx%6i`69-=X^FL8hg(hQ&|QU%#$NQ;Y@wnL>2`DD zy1h!5h03H+0ncprzpwI^lSg)89ttbBsim|ZTV5hq-fGiQ&`9t0-o5}RM~#89G0MyK zk&|LpO`O;W#DR++Ax@0N34wG-SHr42KcLIPI7rGbCLH8X>y#IbfzghRpLEA5q75lM zud6Yo^o%Zxi&nb!Q19Y;h5CDqse4J3d{R**64LWSx=K=det=obSh~;p`k>|mxk#y! z&058YK-uwWWBO}Bfgbas45{$(##Q*3E(>L}SQZE{VeL*yx z>Ir9=Z|iDWWtqR%Wufpkr0KdP)8ZaO%}tk7+#6Q$0z}wgn{%!%3*#zYdjexdXpf$@ z;Z4*^Ww7KOGZbhglH)@MqvXL6hb_jGzYa|zDGAn$c#`me4K+zZrQJqdy(;TpughXO zG#W*fD(yt4M1zHrVu5ZpCMnRb96VpYa`t;gO($wwRs9{I#4H&YrRGAN$+#}`%6d-f zvQW6)!7OIIVbX^UEvMj3CLh75m3^Al`HOwL=*Wvf{9AI!^+osrnxGlUqoqtf(1Lk~ zDJ0zfC`y@Aa4?g{F;hY|%o~#f9Qd*mL;RJRY&2z=C)q-o|E;>ZSMa?_m&J7WG;*%Y zc0=dOb~9Rqc4L1R?Lmw5^3c|gzPt1~$ zkQOeKT|cf1mcsk@nZ=B~gV`_TRvtwb^Y(d*v*Bs~U?8J?)szk~_LG;-NueS*X19w18)}$9}qwOOFYW84&mvV+bsddfpn&tNtJ8>Q0&V%epL#tADk0 z)*uR#J^5^*g5kD9Rw9rKR`kwJvp!UkxNG3_kG(|*PpPyVnFPo{fKWc@gcJ?7_1kq> z7zbh{be~1dniP{**#4c%*_mJif7lodtKhBH7k#W2eF%gBT@9+-(5uVBI0&tBCGvir z2MA<=JB{hS4Z5fH6?LaD`w+JK+jaG&RDO#t3*#zZ;!R-%wEZSy+HMk~`!ri0vhCl{ z)sj;5xGoFhie9&uI_{wngvh2^*M4&Vf`^Skur|tL2qa2i2mvnKlP2Hiir;O?UJcImyrmG{R<}c{7Fs|lh$B+ZaYXnMJ_VE$0_wN|fcL$sla>TTu zN<Q34GMqL)h)qlBH*pw=x@X_+*HY3^Djf3gw2wy1Gc*(B5|Fbc~4n@l> zG={ucXe`RYO6PP*4uRu9HgwO0dvYw1zGO$>@6iQI;r*a43*&e<;Jf{^#sF>Dcl&2_ z^{pWLq%I5Nkkx#*d;VJk1>+0GP`Mc2?cdbZno|5XbXgczyzsc_dCZSwh5OEc_J3|n z`zz7^whJ}4`7tl{P>bNFy82TP{JSm-;}C4!HxB>MSsOkdg|lr;$L2HG!HWgC`P?WT z7s)#Yw(X?s;NdIl`b~9NwqgsS6e=b?fU{7jcZ5uslT|W>@VXL$HQ1lILYIYcysi=b z8PR&cKHZ#fB~VJHZR~X81@PWU6kcf-q1p6X(nkaNYM(Lmu94jyDPk<2r9~U)&)Z4y`8!&jAOY=A4_bP%je3$1^Q#gAiYr^Qrc|~IYXJ$)8YfV!x|!zOk0rd0n{5~d_|XqaUj<3%0>LA6LiD79X#Bf6?5$5QUKZsfby@#K-mzraxuR!F~2Y| zPnb}B@grUBDuBMP%fdK7{U@-nt&pMaPtl6dQb$>IL~4B_oZ68urpsel96wU0o3v`c zRAaoK&)oBbt5>YWlx#D1qLxFz1{>uax-5+2cg1aEXks5IMQ*Zl?A8-Y)eqy`W6U#; z?hItCYmDJ^Mf41Wh+&X!v?fF>D}@Lbctg6%RQai2mxVz*H6dV-Oun4p{V1LHh<>RFz@erpv;hVF+|L`yjm) zi(lX{8xDPva7)7eUVYg%fayEfKpH=tI*jR-s1HbtwL>EE>*JHC25&eIzg1Vy3aU5h zvM>mh%){$_c{r$&ney>R7$$*q|BU(Af5yxCGP2-iA|v1K&&a`~)zF?fGyBIZFPNJj z(ZyGp$iuoU44R0*c-6(^-M?a^Fp+mxM`yq;oKwG|t3w6DIb9aUVc57UvY*Z$rM_ON z9I*;$#N%8%+Sfw|0b@*3%n$~O)E^mxWmD8b9axhm=E*~Qc}SkxjAa<6@9XMV!Sv6% zER4gnf*YxHj&x)+pHGQGG}t;_K4_{Jv7**GwPF;zDCfcmbU=azy9sT&EQ}+tu`U9U zJ#aaHhW56OU@d4QT@GS0WDJw5>S6*%s5z{T!3I|nCzh_W61!hl!z!Ef=&~>l(%R$s zk@6XwZOU^gIorm(mv{Cs510g(kG;VdJnNz*DG5)R2uZLK1B)**!pB#g#bmeX>QzB= zLYIYcXfALP^u5NQxTuq$&+6(;ss4;E3*)N4sFR?7#~1*Ya1!)?)zzVb;RCuXjKi>w z{B$Av)AH?OTO)g}z8VvO;&5)bLwJ29lePk_tS=cu}C;Bqa_?GaMc$s z&JO8nPAUI@E(_zzZ?CyHzZw3ItzdVgXiW3Vlymcfh&N>Od0m|;wP$o$T(sJ?-I6p3 zbw_p`2-y02jcL3&Dij^F(qR*SNLNux*AFm@SH;ta9Ma!#9Mu_Swq{L8jE&PPGH`@3%` zVZNq2JLcFqtaIu1ytj7Cg-71<%kES@*)9D`9HGP1yJ_!HU;nmrVIr5PI8dy{7p&Lr_o-&|CYC@e#t2Tb+5kbX9fV-L?R-j01dF{A+4S{X6;EfWxv35abg@*P+2!#`UE~z_cj&S(uJ-G18=Ht65>2{ewMIl_lXc3994(@n zgyZQ5T0@u{K7LOHGb^G_#F_Ew%*5tEIr=tZAYF&au8BHL9SDgrOCcmRBMC(zBv*Vw z$Z&3cvo0D6&^PL`Fb?PjABx`O{niL=1-6RGF{%WubT-)U{5@m9ZNT>ZTEoH?^FC`uKPpZCxfl^$c*U5CO<%E)IqN*QTyijWZp;>0WuSSjPoJ6E>pVxjWg z)w(Qms5+y+@bDFBPaCFb3)j=gPcKSFH+}sxAxT$gDY9 zDAOKIZ^*G=6)kN1BZsa2QH{?VQ~G65xl0og@W7)_f>6o#$GUn{P&}c_!Z;LLj$+7{ z+i!g63>AGdO#=k5`Ia$ku8z{6K8wVzV<8ew!R48W(7=AUXZ&}%npR*vugk(XSQ}@| zH|J??%)iD|KVaj`x#$9oi8+v|!Ct`s`-Md*hLb()BHRL!BRO&jNt0x%yx~a}UwPv% z@vbt4)i$h~3rNMiH6_&rOTlR4kVSM^7|&P@Kz6_wWHTIxwqI9^s)XC4%iTGn z`odLBT33q-h!I^D#({_)J(GhQrW8gou=u+EOh=$W@PIM(uaD->iT`?@Ea!8pR^u@n#OVrYRTU5}R7qB^$HJg#bv88v|)e)MANZHc5oa z#pgKHlL%)k!xLH|4Ao=0dR9<>dmRzUhHbq7%xn$^^bRqer%#UBJ z1Bfnm#e$T_dSkVyLw&KY>B6im<*T|ZUJFZUzLpyAmtrX${0qyOHA_iMg)N1|x`3q! z$xT=awTtTbHebv#HMCl9fi8>J!Zda=(3fHw`}r5MXc}Mpz*V6_gv7dlX$Z+pnZ`i4 zez{H;S5<24&}H#jm`0g_z7*5=4gSR}nnv$CH-=1u#JYfK2+2*E#$ebq?$O0nnMO{R z#cN?2A7!8~#WX&_znDeSc>MF5LZ(4tUBEPiZ@{lae`^Ec@{fXFI4+6bEj1SFn8l3hoB2Rx zZT+p=ljo6#<94}H-~oq2(^tm^dagH_Qm7lnp_(KL4J%X%{?$inQKgr4mD2Y%uJm2H zN_TDraCOc6&Y51&rYX10+inHtM%J{|B%{Xk+m3U4ts)I2!K)EPE3HrIGS#>)EnOBz zYkA;q0mn=wg|5<(hq&mk#rDzxOrF1!8oCrYkf-z{eC}?^K6Osr6 z+E_0saq>wN%Ddmx)wBZZ8@eovgVkRHt8Y43!i7TQbh=oskhlCCsC#{Ho-{t^(`{lZ2xb8c8=(t=RBpqG`Fk5%M zp~kYi$&Z=0Mwy)JXG(HukaTz%E)=hXPz|=Kuh3;-9H_N5p!$)|Xkv*c*KqtQHlURX z8F)1GuP51O44{YtsGnD&Dmh|8j!+KTt*cd)gLdk&m=26a%R@C@M5jxGg_7b*-J*b` zs3a(( zcsNM6c@AygKlnO_dc_BuDsCG|U$T?rwTo7L!cB$STswG^!(+P-&V6Uq`P?>QdT{Q$ zs?H0NJUI8hsxzlsJU=)Of%RTsJ_4`v0t*nxc!7loJm>`$A@C_Luo!{oyugzCsugz; zHnCvm14#ub=0S=rF~l~Hr8pwUc@U^5$oU}OBT1I+OCqoU&%Yzcg&clbFL{(~ z7lUneBiJrxwtpl@V*5==a(bFJ@h22VOpp}EuL+XWQxPPo=eGn&>iJ_yGLP?y0O_h_ zGqYR5&%3<<=_)P)WC3p%Bw4^m1W6X~6+w~({6LUo0bN^|A=z%fB$@Y^2m}myKqh?& zl77t<_#MQ+@9vZ&|L)CRfMj}11V|}g7bNN7-vmi|xcpa`3+Z8}AW08NK|&7-%|RJ= z6XO0Hy>H^U|3Q!x!~9?6rxe3_NwP$*^8zH%xCl_(za>fj=<{BHKKh;rkW_6~GE-y$ zJn!`a^wF(efNb-u7a-gGeG#CPd_|J{^B;Ht`uUX+W=vS$C`tNAC3r|CxETq4guZCz z1fLTmCHQ4QQi5C8@&_nW!TNd;pfuhsNq&C62vD*flO#X?x)&h1{>=+evNo<`wv>{C zf+TtKf+TZ%k08nDo)9FAPNiX3rlAFCXpe$d3#Xw^kd%ho1W9Qamn6&dK`%fuJ?RBV zrXPv`rEd9pW=RPe79>e}QjjF+y^>^>4|@S(`DHIaEME};B&gyxCo>f~UIm?BDg06h z|0Lyxr6jyc+z$D^74Hvk!24EC+&zM%#JyLLl(^3dlBE7SL6X!zlO)Tx?keW7kSqOT zB0%XWNs>SMT@j$fJtawg{*o7<#4V))s1*ehC2qSHfN56TCa*f)E~RgS^v9Os`!<%o zBuQraJ}*E_|Az=rx?Ykbf3&ofe>{)H-tGk`<`Z6kGR3$TpaT1YUVz#vPkI4rtGwt1 z&{lDlv`N-Y2n>q=Df6TtNttgEBq{R|L6XYO36iw*vLMM=m$frHGS+@UQVDsxAgP3W zqadk-{2f733Hg*Dsf2tyjn1Sp|@C`d}^*CffG%lf^ zDWw|(Nhv)bNJ?B*kd(OlCCOYK_X5P_8(x4cZqW*6$r-t(ey=^#0deo94?0-fw+WKO z{jeZO+-C$yLj8ouPWPO0#a-}QclTCIl>Itg5_CCBmp9Vo-E?v3az9-jq{~BesnX>`ba{j>kJIH-bonA( zo}$asbU8uB&@N4#xc*Jc_myY4!1oe8#?TBOH zY9>{*bMeGPJcrGUkk(xucnVL6-ix-lmCNU9Z=sAz!d;wCICip3onPmb1;|fxM1hhU zwK3Gpsb7q$iGu!5mL$_+pAmf_e~5xrKBQ0GRxbpb9-$`{cYd-^;2(VY2Fx#BHeHW} zLQRv-6Zj&iMdx#Pbj0}+y1GrQv7^PK1!%3UY{LhpRkm-Y9i1gou=iwFaTHU zyM3Ruj#;HRj{lK|b$H30!RgCDZvCr?&QX zKAemBUwgJczf|VJ*KL7sic#ubVlN%3MrvzO#!A?jm4*)7B{*nK*n`u#la`OTtnHC$G=2WgIy|6N*Mo=GLL2~{IOMwuHBV|HpBZW4}cOG=^LNxP{jj6;~V4t=! zq<+R{ZpmFRnyFAR=RLrrZX( z=}-3b4fhWx2YReQyJts#a%gC1xX&I*^`{a8y+f(q!Gy>5PRW*XFQiQl_6`jXCVO@y z28OpM2M7Cxd;5nit1mfX52b9YZ+jw@NN(S5?da{bhwT0V3x5OFkhOiF4{!R0CEE%q zU=Jz(db-?*%YQWuN2cj!)>Ah zH!kZ4_<~|5$OwCPMetn~hmMK9pL==m z4YP~;-meb6$B7!gD5w2cNTi0d-Ic)~qk81S$4R2_tD3Ma*@;q_l8lyS)m_CQ?6!qF z<89*MHfmQ?-L}IiR8n}nM||p4ac+AupUq-|(kh;cBQs4@r%=3%WIehAdclg2I1~!k z$Xq57gwM_edS9i>*XZ)sbon|i54vqQNRNL}n#h%{@v8GTc*tw(~M}?6} z7tyl7Visz4b&6&eRFtyg36zhgN>%6EBvZS<4{hHu+k&58MB7djz(#$X#QDvo>cKf~ zhxe?Msf@;HuMQ$>8Y^craX4|Xs?I+Vv#t@e)N*hM9mmWN6vYjxNp~6inWjqdf?bTm z8E>U*R}Y>;VO>d-5IM>VD&w8+5`#88jMH+MY#RE()9*7|(olM20`Z zS*tu2hf?!r;<5mobUXY{O9d-wPyaN9EQBW4q}wGvP30>DZaRl(Y^1-fQb1O+VbkR? z2jES*?UIb6{YNg4QQnYTo>>5)6!%cVFP!_L+GP|7@#&rm7k-rTUUc(x+U z0L{T>)L}?!JXxa5-&V7Klzk)F>J&0#2m4D94H;=;R2BN4dKs8nx9;WsH-t?JrHIJj z{C?ea@L*EVS~0ui@>?+npKB<#{ub z*0vy!OHzgaVV#f=2;mBZBM^>+D+UZWT!8@2Km2nN2zMYPfrKRd^7~eG_v?N$Z&y;! zKK-JPJ^Rk;dR1N3)m7EiKR5TOrI%fP8U1T4^()z2>C}K%I+3fEOGU3#n{0INuICDw zea59%PWHWc^7LeHW42qVOdf7@X3FVW)$=mB>SSZNgi){M3InN%Tgw)5qmzyKsgYXs z2+k&7i5D*)oNUa!*Y~PE?i90mujG3r93J!BG%y&SY%Dp%N8Ultt(N%8Y_u}jSai@! z*XmX8aJlB;<$0-5!fvv$U_c__Pu3=n;)Y*M6F%7mz1hy}!tA2#;%rxTNw!l|f$gAmc zWkOx+C{FmbR0%{_l&@DNYF;%pS}q^QsREJpMm>CT@NSVPkFQg0P}9|tTR>wV!AkMm z0Im+?O1LA=gO5pNJU=&XAVaOCYFV#{Mpny4Bqn^T;MTlTlZ`df+L!A4QF218&ucp0}S4x*N%(?~j+F0_& zB(TCqm(WrCl*IXPqpRi>D};g1orP(8VvzgBpz0Ze#at;jR<4eFTL;lM{lVe{{tTjH z4T7H8a%tUwLWU+Am!Z#8y@KcZ-eh)nV{WaSaVO|# z0UxEtiZ!T~)sRw1drI}b)L5?I5pEEJC#vOg?exGC>fY%mAQ7i08_u{_Lx--V+(Myz zGBs9z{PBrYt?HJ1bTgLUvyCf_^AwODuX?_pN*7$;hgvz?=q$QE2`cNHxsbAIndD1! zY`;D>mOI57aLz#0Cmo5`Yt=eyrVO<`*_b_0gj!szt3TfS88dtF>B*S$n2twH|9d_Q`K_8Bf0}LC|61o zc%WH@d}KYM&6!3w-7A(eb%;N)g03wkeNi5zP6WBHRdamx>ej0%KMS4+fTX`O<)WKI zcUXsaim5Sh6rIjXRlP9}Z9*Y9$DkD`mV>5Rqb$Xo^onCjx*@bfJ`2$rZ}uXqC^< zBgNTLCJPO1=wPX_f;&#s?y7)TZgm3PQ&4YKWdfa0Xjb$-HBRBX78Tu6g*0NSUM&Fp z{8Fw~$)%4^5DDdR3Zn&eFW|B~d5OdO?rFO4G%HWdu&!hRkd@;XsR3dhU-BLU+0o4y z#lww->N)Xg!Kv?6CL5Ozpxs)*PeEtp#wPIT3+XJ4{#b=zf>m``FewlhW)fieU`g$< zIvXaC2;(&6N;1rTY%e}?xp_;f0-p)CKGf}^jcOwr)W zxePutH7!;F)K_b*3GiiVhF%%dLjlw`kXTeu7@=ro?PQggvAqo8#-`Jfk7iq{ICr+~| zex|X=EtSe3AG$x309h|C?DUBm5M+T~HeN1cdXNgPrbsk)H`YQ->wXTp&bTGDIc~3t zy3M3muSgL*K@`Zt;N{LXkSG1Pf_kI*ZDMwU?oBr^PpgT~kTVo0NAAA9U&lBRTIPw8 zP&<>2hh_}Eo*ruG_stk|7Ca63`7;KXOc(TXU&C%8iJHM*oKUJ+iFPV61@=Jzb1A6S$U24n-a4HKouZ(>=4%oHm+QGif|&ZvR`gI!PY^_KQT@mSZ3VtF@H_dP~Pat+~5gB}UO(WAk00`1cZu(M}m1kMCOFsD2Q=K`di-jywjm&G^x&J%m@IW)r5 zIPDw@FnWP#_65#K4E_RB(MvM)_F!i~Re=w^*ehP?SIsEmqI38)CtZN+$GKI0&H$g% z2liq%?}3;^P4SG&xWaI8XeKu{!L%*pPIwM5Wp?GpoL>1YsuAKMAP5{41{v|`{0=?9 z@CP2Nd)0{%NuQxYApkXizOxU`&|V^#bL(yVHhKdIM{i%>%?#(X* zw+Ed)mq@2UfkpwOYp&^cNQdk#r}vTu;Y+FmoWLVIhpq@d z(K(Ca&7I6KkZ^C=Iq5m<@^Qdwhnm61>2`2WsxDzxU32HYqx+K3SalwZueqHe5oC~& zP$ndS#rpM87@H5@DbVI;%})q$OIJM%u<{E?R-vnCx(x+Ks-LYq^h9_*Xl_o;F?f;$ z#Rx?S>gj?KXs+a)gyLb9x8c#=4bBab+{EP#eZ2!4`!@73Sm+XPR4T{wzzeBWVrX+$ z>1%;5z8aK=UJxCTh)Lbzp+_|L#Fy`f(VxK`?qmAqc)4aUp!KFExN%RkNW?o(u8F>A zv`Jl}S3jbJh0z? zCQy1S{P_ar06C|6sVNPeo(+A{K&9?ydyUt_&k{C5rwI!9&h^)ujWy#z@C_yj==?bk zZFwXNx~1FDyY#Q+{nk$qdR08`gs3EG>y4O(Lg-C`0DkGdv|gVD56X|GR@O&oz-DruhZF|1Cglh&@mG+avbWE<_10UR z4I3^Nx{&Tb>)b`g0fY7BK#w6v^c<*4XrD2bJ`7AHCtruOLhm_nnvCZ#`xNgnL`C92 ztOk6{;CoNdAc`NL%A>rAEsal7v>-huRq}Ah3prCGF1G;%ew2q|45)Aym^o`Jp5vCC7(}#r_nINud+{F^Kj{!kXRc}0X^evz{lwA^=|5S zlAA^E4_O4plQ0J0>C+l9B~2FiUIz&2QW?&zg~GX#0%2j?&Y@X=zDSM*c&1E8N|$_I z`5Cj%H98dta^UP!UW>LII6#r?be;%ich}{-N-)WYHqPZ}BgFw|u0$=u?#C>_htu|q zoD&M?HKLnp8r7IJ;J`C0=10PDrhKvR?TYD`oH7}%QPG5&N~PgM=gEVd*inEqBOQq# zElv5vglZH)FX=eDQX+{-D|xzkG6NRKphosX@++X(vY1Md*9}b^7I3zu$a9GEv*hNl zW%F=bGI1I=2lURA;1Z&l&57zj$*T>PD#bzh>>UWE`r8N5H1M~YidDxBn_eEy{D4+vSglfK_rqN0JaBY+9r z>Jr7QIX(f#sB3j{UY~6&Rp%tSGRfdfV{LF%M&2kU&od!*k*!j2(8(F z@4Xp#ym3;K4fz zxd8;g@%SPR*Q38f-H})Jlqci&3&jnnMGB8DlpfjLoNCZ1?y;{89s_ZP><#oNc74FF za)r1Ekw@4JlumIT0DQOgVhIFUBQDm;m4bJ|D^U1{NCzDTW-b>uTm32GI{P8e;laaM zCFqW2#$|HJASS~hJ#iUxAU15NN6g1e+AUIO=z&)uNrlxVjeplUBcMC%v@r|};t2;4 zK2U_`5yFj^G?E;W4BNRbfZ#lgFO}&64;@4|DD@ve4$JXJo~V<(EknV2{5KwdqF#a% z2*D}pfBK2*Fbh6K>L1w?m~qo=2jdpv4?&v`!xdF4KTJk6NQab{acHIN6AweMy_4iz z!=&knhanGS+W8b)J^h3njr`NPp@*{wDpol$<%s|`QKga@Q%M)MQorpF--R9^mv(Sb zL%hz}2Xoe*n3H~ErC^@nYzsm&J>nLJ_@rVZ^)R0T47iA1u>jqyT&JR#B_;Vh{k=+5rCGiQuY$MH2V#^^!pTWrdQVJ zOH~hZb;x(tb2-R{ZrWqsg((DI79yg?)mpCTK`~7ZUu9lBS*{+ZFx~K6^D4z5%D4)5 zodzo}8Xr09(e;Q9lDQ+lKl}&jDedgrBFI>NA;r|6f~b>vZU%I z`g{sUc^ROSFyO!js0J|nl#94QwR{|zK?t;=8;dYMi~QNfTDnklkHcpvlm=xD;Rx{^ z(zMVuxzboU6;?;x>awX0R;r6Fnx;qHF?7v*l}-R1t8SMNX7XyV)1YGNXnnQ1k*<~z zWeoBTi2MYzrf?>ufS%g1@z&%Euesp`2!iolY3kw59B2h(Q7z-klp+z>7;JVZScsCh zm>r#`V5mTk)oSF3U6MwSD22=r4ius*q5CZ*vj=LT>2h}(<{L$+j(Zd6Oc{E%x6uLT zHR9rgEgzM~fE|PlhFAxUf(6R?hvA5O+sIM<l zu}q=5U@queNZx}*Q!VQBjFtK(zXvm1*sV(j>wa|*(dEHxjuJ>G%_eBbrGyXpY)|m4 zx`EU$4H(3vR1%U8l{;C60fHDP*||){a2Q2jCpD+$1t7>{7;!*!PIcmBB$(gw8jnRt zWi6FU?g?0I=4Er}T44qhp%_%yVBLVY(eDhn8Au9ea}8vwdxp)sb zYH4msJsV+$mS<8~%CRsW6z6M04^mty#d$~s^-O*d%&Y9~{9^n$oA1K!nfwy|?dIPe z{Nnaf@n@O%vmAf&E9hr6{j9-HV}&ub6oC^tUu!JqJXoO;5OX??)(bQMEavOuV?}jg zC0{_ApW>g9cDs-)g;I*_9DE`&j|&Btl6erh?;yTWq7HJ!>Z6!m*U*J1`DlL4^^ep1 zkBke3tvZK4fiw%wZ(7CIG3?O{?6@Cv8{l^Z-wIz&LGQ!zAI0r%z72s$i8L6YgONsn zkO*vDOrC2-^Mp7|4BD661G6(>Jm!k&t^b}7Nc~lS- zWpk7ACxk&lU7X*cQ<$THP$G*(P*&aW3s9L9G64!*;uVNSs)+HCZ12^4NsXamI>Q|l zW7G=1$3spEwrC16UaxtR!++XX%6Ci#ut_E8j>|gC2JLkD6Zrr1Ze9 zK~Z@gZMGICAdm7=5$>Q+>~>I6hcZ8 zT~04}=!BvB`TwM57UQ1q0MKZU0X&(kV-8VECSl&-9xTZc=4I9KQy@|f9tLh~DeibD zFpQA;qS1lI{?(5@_EP-jsS|G@M$nng!uB+V|5!Gc&LXH)Vv zazAB`kUyFK86mhT^eWQivYg<`aqGqEWxgz5xH9yDwA7(3NMUpTUZXK-zAKs}dg*7B zMN39@>(!_?cw{P)!C<_gE{Ye{gqjd`v4^P&jKQ;wmAaYG9r5ZFp;zG?q;P&I#cz|8 zhHeWpg%pZ`in=J^b>oGihqx;yQ8ei#4CtEd6%~jxQfZOcM%_U;(_w)@WB8n6K=?M@ z5WW)CRbd!Gou>4~Wq6zc2F7Hz)x}HJ8Z`B*XyMr$LUbNuB zAEfCgrcdzH4Q?6OIXqLpZCG>Ike|=_qgPZchKYh!ib2 zuLdT__kU#wuyU#tbG}awKqQ58o~X>8E5Zt=%g_m{%*R&U{50 zYUH086BD6FVCX^ccfp|p_s4P{*|S3g9}qu4CyeQIE^=yrK~wY>yltezy@3; z>0{Dzz^yYEKe$O>Nu!7AM;?5oPGEI;2*pW>4QjTcdGYgu%ouj2qXz`~-c_ z?xWs`9I_AK>x2inMym?aGRk<=^IbSc$b);#OJ_?ta`SPD!$FKpNUswOZxSvwMj$#x+5C+pInsAO_{R>Q~ABH|7L<9I}9Ac z1leiak=P~odRXB^Xi*3E`{BR#F`&The#pZ>o)LUQ?1Q7MGlSrb1{$&lVmTEp6GN9V z?nx9^?}ZJ+Ggp}7wF!_ig9Tzhd1kP=$!Jkvvqbf*O;g`$?RCLht=%rTW%fJybu0EF zjX%ijR#b2A)8_AMMQ>4P4V&)X^Y@wfN~Es69|4@g1Z7x(DQ=-3c?8H!VHqYvj~B&7 z<9Ie75?>Ds5;?#9aDCKJjQLSWx70-*4)f2^&lr9lnaqC$fB5l0XSO{4)riLpb=va$ z-$Xp$s`gr5`&z_nVU5`G%0EWDqSTJ!O3O>%ndT+Mo|c#XX_}W*M{9ZMN7KBdbYII$ zKaO~58nxK+{!b&`S2R%?v*o3qP4kjcaV;-hKD)IWsg{)XYk6tzG%qP#)$-DUh?lxs z)L6@Fiz8lB2r5m~@={O4OMy;NN~-0p71O;nWmjA?-CI-k#dXuYHDza9AMuvfT`eDU zUBu&Iwbt@VU&Jd~qBro%SZO;!@c@&-(ht>nH^Av zgV{~$aDVoII=u62qeEs~Q0sS{RX6U2r7arC??AJ`S@vM|ka}iIcB?uZ$sSXO4`v@y zhmQn^JbFf5O9e+S2#(zBsJfjFFvtW)UUp1<`*=31p3G(Q>Tr8@hkD@nVRcjp-Y;fL z>d8uQ@v&@GJ?RH%)`FvYfZU1fN%i)&!`a=S(y8nO|9w1o`iZlA;mPc2{`%) z1wdXre7OShlI+i`!$tt$r2$^Q8eDr>@b=5IuTZc2T5$1|!Gn{*(Nn?QU(f!AdgWEw z-&BXctD2DuKq>c%=S+>BBh6hI>pSqRuMbwqX5Wa@H)P+)f8UgSGyPR7XW2T)-prT8 zDp(OKpUmFM*TizuAcrhFz*pt6SNNQf_<=d6d{|B(k)|w)CuDD@r?bD8eGC76tNi=> z*|(_AdRw15dV6s6j_fPiarP(b@ZSSi z|B?Nvy7-^j|5AtleMTMqEI9f<+5b@&Kddh1sd~;?c{zVMPJ%nL%#+zT2{6si37yTw znL;N&&pheCNdRxYd9uJf>BLEXA^iwS{EalE5Cxxy%dAxMN9m~%_0%yOuufG&F9{bi zV=~AI;TP`>Uc3)S;@bT<%0GY~e&YE!U=aBS>H0(J`lED~QfDuqGgqD6LuWKdGlUGC zdFpJ8&c@|g0}I75)RTR{$#HP+@he$+D5qY@)7f!#R-m(@IxEpxS)Enr>@js##li69 z4A7^;tN5@+hjaO`jzjUyCvcQMi66c`frCCidK^bVG812U0$0R4PpTt?1CWd))`7y4 z1dB%1uF)0^T(WX#2$jKc;5WJDEHbB4j622ern7pfzaY=ScYX&Mwr4VIz?`h`=9 zIN7P$u8P0{gUhY?1#@7%U40Ld$>EstkmDE>6%grqY>Xk+e<&R zfLH$K@Wc4Nn9g3J&VC*Tf;hiG*MCu6|0NuVulnWSi8J)XS^AM8aE^|D#kkPGu^L43 zFQps5YTSL9dH3aX;}yo;U!&t!8W$$%_$lMUYjGSXli#5muQTrcE*-z#xbOx#exq^W zO*jtTe=|Fu=jMO6pvnwLg40g7_(M%cQG%<|W!M!EHw<6(?3kIx-l&w3FVxw?uT%lj0 zFe4}ZApD2LH3(=0A8({K1y6|zIdH~E^8FD$Bmc+rBQJb_jz4Hz_z;fOS2wzmLM1Y+ zrn3^Mm=Hfki3>eJI+3<4mxB}GNfJ-0Nc(>n%>~2LM~suk5}7=t!9*CSYRLQ(BX`n=DCp8qV)0%`r6dGdLJ^k?)VwbobY_&MXk zU*I@+_y03b{*s>hs`2RG;5bkre@i$1&ba&cbo@2r!av|Rfc$lw1SWL;8#oIf{Ug2e zP5O~IehbHeBK41`SDXZx{+oH{ z$2j2>7lpu2%t!y7?*0e;NX&mq$CRp~F8o*Xh5x1tKQo^AKXm**#)ZphvT`~7NT9Rm zc(!q24jr2?=Hdc+mVAC*^X(40FrR+p6APN3=){HLtJLj<&9@ifLO`Fz&3C)ZyGxqy zcAIy5%#)?fk1jLsF2_kQ5zDVIZ>=;>R+%TOagtv{Kk`f0((x6>g)4C!e9AhU^r=U$ zqNlDl9(Cw=y>a0hI!+iDuBGGaj0@M(@do2UFCF(87dF!I4aS8XbZjznCoTjX_(t>O zCVF%i{YbU2n~wJw7xvTf0s4{Oe6`WLuvq!Q=e;G++@bFPdIbWpJsG=gM{f1G9 ztP&r2g~$;J!bJ%E%Hx~#;WmAEqdwfJ4|gE8D}pon8ZwIK!A?cw6*(gcJ}#;XinI3N zq*J?_@n#S#X><}@L_ikFMDlGjd}+YkqTAeVT;7Uf9i27;H^CPgFXG`@`?gHJywS}W z#j*kwn{5cYHw=&NXUGUQ{I7pO=gR#ZxTHTtS6lttPlqlCPycD?a^Ru(Df z(E|~IhtJaW`A7k&mZ^Yd{?l~c6uWwst}RsA3jElY8JzAwG6I_aJU^=X7(eJuX019`^i8+qyw`LEIsb%p%@Pd~)|{9n-zaXkOm^g~R} z{|)^RU-N%UKg7!X-_Z}TFaP)SLoCaGjedwt`G24vVom<*^h4~(e}jIA1^IuZA0m7H zoAg7Z&VP%3h^F~((+^QG{~h`v`sKe%KSZ_s_vnX+mH$5d5P9!kp1Ld?jCfuaC=Ouh#TJqb^8{t;FVqkJHx%VD5Ety6f8LEN)QwZ~J( zQ3VO9<|(W=qD&1acVw+qP`H%l5u$t^sy>PGdARHki0`Mxq=QIOqbH3;PgvSe9ZMVU zUa#JzFQ1XZIM?yf)8dSj#<`An|Cu-= z#c{6V#BaWjpCsjRu4DH*#2G1&a~*@f_D6h=l*qY``~O^=ks>+QanFB=Gg2n!I>u@r zq$g4I5t(4LoV}E$#ga%jMdmSj0vJhWvRE(HCf7JKjY^6KR^8||o6!(_@l zBnv#MBy*9K?G2`PY}t9^j`Zf6Mz`O%DZOLI)*H8MyU}&GrpLUU8PDCiX*4sM-n7ZR zY0DOGr?+jpi~qK}JKas&x8ly$8@V3g%UKUj*Gy(?=jhHI+eWu<+nV0tZQJa5uDjjy zwq!PEc6c{#_ih^7k>0fZru5FVJ9^XT*0Jk#K#kvFTQn_R679;vh$ub88}eX*#4M)BJT zy$+fB4ZB+EZww~&Mi*-r%GBf(E~@8EHJ0a7Vmc+Ff^1|$44z6#JOs1=D`X+d%u-nO zlr%@F=)qlGyjmup&zD$ioy_kitmdJj{lMn!_?QLcZ9_687HJWk<_hQAp~G`gTbvi& zU?R~`ud_~~kGcu3M-X)G0A>_eqIN+P%nwN*kz{*lM#;v|0>apn&6Q-^ymesHWIg{p z@df!2{5>=@Cw~lohv$M$q^(aQ3S-{?)OF(irn=9@Tl4+=3UU7m+$W9Re1GL4aeoQ! zla6n`|Aock{!-j0t>1jVXTG?Pg3@dhHs2q=LflW_K8*~`_fM}C_piZy8Y`ObzqCWc zr_rPNez8;BUyS!@JZZlFoV?$K`!vEd-yc{Y-d}|KH0CtlUu)2ZMxo~W-I6{iL(4{N z%l(xC{*|~-BUST#cec1c2lr|0YQFz1$^Ut{Por7${oj)GUx@oOzBSz+eoBD99Jgt} zYr1{tIr(|rxKBf1^Zhr@67SE(eHs*-@Bi-Q;{Gh$r(tp$F>XgMPA}E3fk4chGGu5o zAk}XmtO8{imfDwJE66rj$EuHcY(Q#bjZIBjLhKJv@j~P%Q+j5Q2*04}o)rEOa<{Cv-K<&C2Bly}_Ox{Wj8F%r(>1?xz#dJkUkN8LuJv1Fe# z@a!C~$Cq`pfdZB;%oY9@9L+~na(zN;%V9jzGxbJC&BgM4r1dk-$R+1V<{zRNnR@y@ z9Kz-Ke*6K#AHbhhJhw|c^M8Wd`Oo18IOd;LiQ)t$S!F@mi-gcBfD-7mdcxQFPu3Clk)=+qjaYCUh;;X;Qxd%2K)lMcF)4Gok^_E&=+0%q2 zdvd17a_v!C?kAR$ptwfaSXvQ6%CSajP7_k>RGJ>CrN){Uxo{$c%z!mAbDNN1zt;4~ zEIU{pXZP<^q`Ac!8|+^c5>584O^;3A6pLL@P}b;EZVt<-XjOvh`FC66IKK%;GQ{%J z>26{b(ESlz6+c1We4!|_%4wHo>GhJtV z*a1B~CPVia>(TnfNjGJW&6?30BMiAad~j=G~LFN>9g$Fd$ur!m1sRLB(a zv)9~!C4}r(E*e=ywac5R#$lrA(O!N`E*WM74^%}Q!|ZVXaM9?5X~z+&>5*7K^EbUl zS&hy=Y_nyKh)v(}5UoMPG;x=67X>lpnok<(uxKB3FlbeDzoSeY(e01W&J;P6i$!@I z%o9yo{dap9*BhNEmo(lqIp!&=m{romP=qdNf+j+jGzAi&OPXJZ&?Q;)B6LYsvj|<1 zsVYL3WG9NyCD~{qbV-(w2wjrlAwrilNQ=-V4SynZNyCo_U6L{vp-a-6B6La0NrWy* z^hM~BWRD#&dFt3AbV+?mhA!!d8AY_KW3Mzbh$E)=3rFh+H;AIZK^jFqJgsIwzyezR zpUR09@=>#I+Gk6ID-nPbo`yyb>NarAVT@=lc15UUr*9*Onoads^bB8lBE*#I>W+(@ za&efQYoj4%HV;VDtGP*zbitKTqC#%$gg~WbVLPuw3!<&HII7!t6IKTxmNSTa7yd+& zHNR}iJ8P!6lfP=J8%)1z_V8}B&IoPj%erZLhsAAxc99MX%3C-;K^ut#q7fKSk{DR( z6_lpQBrz7E!ax%BndEYNxUkv(qKSSp@M)9iH!=tdqUSAX-lewwLcx0M#u3tNkH$vd zB?)XR%ezJ=MYAome5id|K41%CDP9y}bT5OLvZvb;eGzR#LUUAv!^0$hF&d1gOD|4v zjd9gz3&CG#OD(N2p0EYcOmOewy`YQHw&0l%Aqx;1i|za&GtTxk%2VxZc9PBkOqJ@R zLfM7H?ppyF~piL$HtnCnl)R9CxlUwh91Qy1EUt%{h}?UHM2f%3nF%AZ3~#SwV7FR9(p2I zt)afJgYbE-iV|+{R)o0rli0YnRda1?m}?^EASqExny9s~?_X_cui5v{wjg3>UtfrQ z`*T9p+>&$8!4cZ1%`4E%i*naOJX?cpkmFH;y(Z7b1U|6vlBhkLWPFm?35M}$Vbn?> z)uw^B%oarKj9MQsY8&JX9t_?sr2cqY$QM5g!;?2)Y97Ii?$$|32rf<=rKu% zkOc^h#kqLczU@wwxhB6ocCJC;1*jWj<(xn{L4;!tVJ$@NR8khgM24?)+lBSt+yygZ zX0{A3jqM54;3iX&kV_iKCCg(dw#>+#Kt6rBnlzDX(SfsoVVgGR|FqwYVq)MOfz3%C zk^NXlMB^5PVKl)moSD6`G4pn9l5S374>R@+lWD5ayhyfQ*FIaXu>}#kT=oZSCI8A^ zWuOpWbzkSLlraem*(1ZTvG4}X!YzVr#@O@IKXQg(l^AjA_O31intibXZ_fKE5!)AyR|KA>Ni^N1~K!# z$2Iq7-I|m0TasCC5Ak9!HeReroCF5R93l@`zKaYK@`!O>gvpm#t$gXXC93Z1eS)Y{ zX^q|~$!*)&Hw+=coOb4gcM|SMRFJEd@zpB)0|OHlByE~wX4f5wjrP3>H#w&9Nh*9) z_y=xFiH{*7v!aCLQ2Wfd+ZKctk^_RDGwoVip=88GhEO>pLUv*KsKv&k6@h#p4vyAE zfG#8(!x7T)m@PH6bd+pCG&97)F3_fhkWmW|8jG3NoxNf`<8>oEo8qdc*m_4N#>uFz zm>PzJ`HkFBM1to$?2w1NSDjF4u9aNmr*p@1HK7edAl@PS8fAi_B#8N#NXL5{9=MqF zA=*Bw~2%P36fk4hoh51TgUk= z37;9%nz<;$++yw!CwKPnaDF}rT_8-3i!yjlQbvdwvDkQ(%|~b_G$c_EeNeNzGMqRt1iLmi|N?X z_|#a;yy_B~YkkY~rQbmVa-A=1ytfS?4M zW0VcoVns~G)}4|GPI=>WU}Z!kDH~2A#mP04qSsX z);#({TM)7HXyu&-W8~U$L<&Nx`LAPR%}S(Xh)ECi3y%6xoWfMS9jy%csx37&L;k`R zMC=T?nfE*;QGt=+i0yu9D9z9^H<3sKADM>=so~NUEtOg>TNKxTdUK*m`?3N@=9s{d z22_b79ZDRNM&S>n-lLd|A#3wnS^G0vzG&9|r!9!sS$h)=Rf@BRJh$Y_4A6|TkE$#> zD2$=(Nn$xg6;bPuHt;YlrE6nj?5+fjTZ*qm!ceAyW|AIZm~{QX;w3Iqj8S4}@O42e zU)KZYHm5~b*@B3juM1QxIP56DGd4bT260~0Xwwd&&Q`kbvn86Ar`@(7VyF8SMR#ZO zE_2P;354P?tf-0gA~GiiV6XYGv(9ZTX^Ed(IX_?5uSH)^6S997g?e6zrJV_xs-1_-EuVZB3Rm z9+7_F#jQ+ww=IP=liq0yB6cPv0w$pZ%TR_>ensiMDGF2mWo&#(XgS-KM0pUBvno{q zH0wMV^3>JJs^@I!tXcIXTM)6cO3TxBu*%Hv^r~qT)e4Vr{~a5j40+lv;_{qn3Js|6 zFqpKYl}SIfrLbnwPi#RnGs(j9rJZLYL&*Y!#^UR?c~XQ0yx?5b^`ZlK*6Z7BD#UJI zJTsF~`qan-v>!($plv~`ZO%Fd8H7bJ#+tlq_hM%g_HL2`APu)wFewa81CPZ;(#927 zC@4^n9vS#D)x1>CH@c#t9M5Z?;)iW1ZtG;eexGmy?s7)O;!NJEH#dgjViZ(zkQ9Zg zFyR`C_1K7iJqAGbN+tz1$uKrag-xJd!G|bLRNLo7*%pLWZ3V&1nT|ItC(rUod8}4+ zPen_|Z^p);73@y-MdQ(O5tRY;>$b$y1KulbK{WHjVzkl&o{&!q5E_f&>3fB>>KxjO zJpc>k3e6u~k%f=-Lr$hl7naS>8Mxk5gQtDSP!d1>2uTA zpQ|;x_ubeyvkrCUcvmG3;~7XaMf19bBNI=*)jrw3VGBab*VhE+W?Jj6;VzmeLXMUozRK;Jq7bEf7Xqg??ZElAAY$jrI<^-*nd3>0GA`a< z!%F5lcGEN6V_Rb5%~ir=^kgC7DVhEns?Bh@Ty@kxi2PorO0NKmadmla?8<*B4Bs0kM3naqaGW`#;h9iJA2~@f@JW_sHBHWAHh30cwm)P`aLuy^ zY(d1%vz0@cd>yWZEVc`-(pC`IIk6$wjg5X z$m08QK1w-9SsZVUjS*c5Y!GF%YS2HzB!7b~@ihHkXA2^B`d?vY5;?qnl&Xog+JB#k zjT=`cOdNE{CV_*&n+S9KSzE$t-h9FqMC`noMmByB8#m@8a+%HMG$_IV|DG+$G}XUt z3nF%^Zwhv(Z4T+s(jctlDp$t}C__oE&|&PZ5%&Bn?T*Vd-<%k&V@qV3wl+s_xLCGA zj2#Zg88Jh4B5d|0K($SqeUUAQ*tvK;g>ZMHvK>bIJ!B}0Tb{C?NxUk{X{Niy|rTN#>Hk#~kA#(x#uVrMG6;DO(V+vurhK$D{iW-m6(P z<`#2>@JgW9$Ht>ItPiVRp{{u&5irKAh&<`vu_dYI%5U3(h@C6TcDpqQl1gK5wZT6X z8$*^SMyc%}b-l&*?rP1kea4oMnin6n1ra+hI`$$Lrd$v2EA-vi=s!P^k?k4%KO(RB zTebw#H2;Pzh}db~!#mSr_UY^?jOSINd3axJ zJluiOM*((R9wpeQJ*a^xrb2{Wkez7qb7?C-M{Jp)`FX?^L^D4vf<`*-Bo+c#fY4a< z+h+F3bsm3;#Sc@iM3}Nrn{_trmXUISqgi`%V`E;GiiAS5?2`p_d`EQEz0_3?sv40Z~5D>?Jc^)on;VHQhMeSU)KgJ5cZv_luMG*887XI?frMg zMbXCEVATPZuqq5t&w(%q2HGW2>kQu3KE2;!3qp(5n*_^dI^%WCQIF!dNVy2=HXz8_ zvKr(|G4cg_jB5-yA4Xwd@FgMv{0p{3)pGDTTM)7Hr8_W0GoGJ@yoBtWpTx$F9&LxJ zh7B%61la%8mV}xM|7;5)b}n2=P6}GPz!lznIa(lP%eiOoz7R*&E}fZ~LCa9kSjUcv zU;%|hg5@MsNNCA$L@0d~&}ws%u-q2J1#`!G{C+;MM@|I9SPjLF@@bWN&EXW`erzJ@ zrLoY>%NT`*vbhY(;fW{wevzn&_b}BCUJUW}w%B<40P|LUfiOMs1*o+KM3Vdk*nL!f z0E+h&M?LHnG?o+}ARh2Vb|>Bv_=~INkQNcozuA_jT4--%5L22EQyOcwxkuW9I8v_g zaD}2&ORy~`8Fr(GH}v6EgJEb-G>Z1T&DcxujN zmtRKzRAtMr#Kw_puvk%UJSB!lmxe)s5-JceN`~(3h|;}(-aaG#%oc=}qtDuc(7IQ2 zhd6bwwKQLK#I3UPB-)b_lHWPY;xd)5P9UL@(16@(hfW}{Mg_7Z!x1?}mjkgjjphHt zT%J;5XWp^0tdsHccO-N$re@Z8d5kvN(!%OpSV}sg3wZUC4-o8 z<8=99v63A-k~~(=Re3k`a8lM?v9V%BLd_pSjfeOV;XT=JOH4gH?Xd;X%nu8drnRCF zISUXPi@0o@s{^kmO;kxI!7QB0teJ?dhg==FK4As3YGgD5y=WRT5lMO{Y}u@Piq9aX zREsk%k2^;^QB%EM5k-cjc(PUQ4ApC8iniYD5L?FTg#u|$TnSge`K{O(vNK^+%p{&D z2^O;kSK`N3D4|D6meDhWdO?(u{f+inHE9b%i_{C9VSkpw)H~{nDdsCE~b`h zk|I1%GCVB@gzzBnnb>;NU})w-Xa&+O?B*$B)JujV(mbEF<)9Y%PuPOc{pdVu*_MOb zk9uOuEkvkMeATcN16KO%6yzAS3R{Ov*dN5k$Q@XTDzT%~0u#G{qr&CKG3o$2Lo-Aq ze}2!F8JeHpwgu75PYa>edO-v(EkI~2e#ti4KvPbIsNGS+`=0hmeTOXwEkL&lZq0ZkTGWuCJnanmZF#9lD_8Qdab>M6D?^#t zsJ~QBG6pStiI_``+Y(i)*NiQQ3+9WpZ)X|G0l?9Q1c5Z?bhQY|ug1obAl~DXE!phCy6HeKcq&dDK7V>X`m6xWNk(rE-yylA4Mkm|mVgh>iS%@V*9v zkJ?Mfha+5}HD!&4?&z((-`+lN-f9a%3(A`XduLjKJ%0)&qM&~oRW#U3H$oYQm9n4X zMJlv@?+BdPBvQYO_0 zaOfEy#2-MVsPF;!!}!x~VG@7sU!ZOgp+CQ9%Tq0~pSK0k+$}6@OKqMC#bE(LV-eHn zy6V`;vKh^C?itM^r#fu$%w7?b0ZEk&!|f-_vi)G<(jAq*av3mc)42Ru`#mEjo~%Ap zu5wEIULE>`VrZ&xzaciJtVtAsfS6_mu~&hB!Ig;gk!#!M%6eN6u}jKT`|DNM99XPt za%I9f3KOV$JS;H7vGL~W#F#+C6k6<)g-s#^8U}|VatH3TC9l?~`)om6Fo&!y(h)L2 zX`4*yzt}gf=H?36PFb!^N5WoUw7_x_Q!SMy#;=`s34J*>zK$elo=3ZBvg`@m#Y}^i z)rU}TURc6=a0aXYqZZkBN7aif*fLW0g&c#pga+}dWAMx$I(*m2F;#=tLH+7nc>m*< z$HpZLSMbUgOF6m6Dcto*(Y_&ek;w)xZJ*fZY(Z$j`(?qenYM73k4*SAF%v|j6B{Iw z6&o^6-y0iCRwVqOaVSC6c!(bnrscbBiK#pPJ8eP4&W{_8RIzFW5ogRNJj@p_xVrdX z#>S=_5~wJyae_j-kXqx=Tf-4k`R8mYubKBHTM*66voPxQyg=wr3lJKMm~2qqXJXG? z`-hHh+AKDrgRTpS^!$}^O$x+56G+c?tD829VkFeKA<_{^I&*FkxH>Abahq*F(dy@B25|`ur|u!g=Mrj>TadpXE;=@Pus&Keto(#pj(ehF z8IQD2+y`wzXbHMs@MoqCrPV{oT|Na57+j@X$0Cv&zYrTY)+F2#_+TIq#gzzi>BY7r z)m{FDwjg5X%B?V$;A!QBj)%RIlx1G3`Of{hTGkmNe=BM&WiZcIIw~9_3=N8JiH&u) zkpU&K#02mprJVyymHiBs5|*%lC6-N-L{sV_EUGuzGDq|G^|l~l=Wp*l?6;weSPoQ8 z#mTS_=X0^KvM*64gp!mh>yz_*DyA86-<~Kh=%;N-uDSLpTM*4$voKh+%_D|G3lJKM z8~H<~%owz`O?nP^C0HO_W#kYRRu;m&l`RM@0LvM~%nQJ#`>XK%!}`9bUUl~8s;F8yJ=;QD8;XrYrDmIqpbZWcV4N`j_jj%~_fmvDtry!gOSQ<~CfY?NP;5FSu8n;b_l zH$>Mc0ZFw_*@taGXaRY?EeKp^&}*pr?5`9W-GRfaJ>j(k{JG!fD^zX$&Kqi+6b?1_AOvN!)R{C#9H|BU$aN%7}d z@#oY0r!gN^v{yw6<>Z;hJd_+klEP%3J}=TAkc{#NywSE}X~pro4B`^{1GekrEeyDK z5e+cE5EZ&;+1Nc*lyi2g6I9>`8h-?}k3jGcu|E|Tu^T&#^e!ygV-5w_k-=P+*im^d zsC{2z7kCKgL}{UC+Go>8Z9!?M{JC|1Mc73FDQSu@Q8D@db$bEZ5tm#@E zlL~9BL?KnQo}|`Eh$xilpUCLZdrnlWWg+ls)3}{)3nF%Y^z6Zm&gUXNQM}j^8!wh3 zr4B7qZd?=}A}aO_+7eOAMZYbG*!gfh@qyU@ao{yJca>;5NQ)vNCm5?xY@liQxi2>6 zY+&XvZy+C>PmxpTkl;Xo1UiN$i^zr=u_d?W*b!S0v2$z#c%l-p4OPaxqd0@y8!l`y zJRTd%dJT(#U20Of3Fevj7(9!}k2+~faLu!tEr{58wq0egVc96Mb19?H=>@aO)tZaY z8*~wBT~)zCw=m)39zqmeHJoJp+SoX_El6zB*wKbN0y!2&AP0U5TtF)6 za&;n!6cXtTqzv;gV&|QEY)P-VchD9@?A#mN?M`_9Km6Qpk}nAf^k5$RtrzHdJrN@V zM+q-7GYBcR=&QNRxW`8Yat9ldYKmXPCOy6_^)&-4wjg3>;EhVnwlIjqn=M|n#oo^aN+ zzPxhn%uFv@`f_6xN&|`JX~||_nHpjFxl!p3%YaauGmdUs5Esl5>xi+bTCkIPi)z^w z8#|h2dvl|LDm!fnr&Y{01~H|InKA}%n`*LPcez}Hq3l*dhWkUY(Xlfz8nkHWmf2CE z-3QvI=e@QdbYDJd3qq%M2(Gqe1H`1`)?!lNjOJV)FZ$m zS`MK=#Uum@Mh}Syo}IDfjFz`wv<0F2M@*dQ-cv36#Fj!X!;1<+{P=^|_|cO{H#cl> zAtE65c3To^F1*ziMC@Exd5G4WWBi9r3;z|FwBUvt;>DL^Dbn zhaAjtuBq*`vRNOQ#%@mbTJ zYMvcTh(?O$4VFX%v{nP5Hm&Iu3}Q-)Zt8WHwkF*ziqKeP8I|tm^umqXM>-9@H)lWU zHXWz3$fefh{kO!%j$NoxEqX(A(jPl@G3^0{@a+xG#AYy0kLCsvJ+4^%_A2GHFu3|^C43c`sMRgn=g}hUU%7jM0UxmwO}9`;&jB!?oGCA(wu(1Er?6W>E>k*R-FFarQmdP zM2?)25u@|;oQ{}1e%h8zn$w@M1ra-^u_K7Ea&|dGoZ*I~2qeB%J!C?(Oe6m}Ha60Z zAkx|riFU|%;PuJ0brQ{HL*f`Qq5LOX3Tq~P*A_%GlPtVw+7Ty=aSIR{3*qju0HLuE zaU=^68jH`^MonZ$@MD)YB)IHgd7M*#rdr~){mPk{`O$Yp#su$*iV1E(t8Gs21{uU9 zoZg+6l*i&CWuqwuGB3(!|Gf4|de|0(7LQ@Um6@J>&EthW;RxWV*vQ=>R`wVXzj;wp zqPi`)v~sT6f{2~sD=0ouP|73HOYz&WF<>Q!CRFPY9*B8SQNZ7{C8p-buiJu%ogYV3 zxq7l=3c2H6A(t(eGlBoe7aKaDSgRsiI5QL*b9?nF%pbW2R#=uk8XGI02NOut*(b|K zBOWl-7yCe89~PFMHXen+VF5y8(Qo@9 z(s=yhr8OS=u#EyO7;^4I345$&I^!VWc!gAiA{c+CIp~Kfq+TuDC7RY_Q`#} zEeNf?jtP#G9iEJi(_NRQWVn^EhmGPL!5}Hr|?2sGV0;% z30n}&oUj;+^l&D`&jN(TVrxA8VdId4THER4a9QXy(1R{^W-X0lS*CNSUZ`QCA8g(e z9(UdyTSvJ89feVXX{u903js>O{!E0D(SsuT&pU0Iulvv2Y(d1%IY_c!9;-QNa$4uc zz@d;NKNlN=NRp}Lq|vg$hlr5Ymu!is`S1l>5V7-N%`i$Zp|m2K6eR=_SKyed|T`IH$# ztFl63v+k;x?CHjaM6mH~(qveX5lRMvzQF~wU81lu*eZ9&A&vmUY?5ve29n^-DXQfEju^cf{2|DNnIfYUaWGZmMi8S7e&9}5u*)#yi?j6 z77`#YHXaQaby5)1GqECfm%vK9x=j8>RxP&IzwfE)xI#%R8 zPPy;!S71jU&+Bg@eTeF>j*aTqCQfK%I4zdPq%b2)1sYk&`c>_7;VD}XS}cA|Fmk4C zw}jZO=cGHqDX<2;n0Z96usQd1jNB3%^$;#JJvML&cxU*}7YxsdvhhA_OKL4MAFu_{ z%q)BC1QJOLba-|$fWoEo^&EE%NW6&DZMwbZi z5I7D5k^%u^N)cJ~=3ZN(YQ7w`1ra-6ZXFT3s~(v^tsx{kQa$@xnHKP5z#qk99YBGlRH*Jc%T#4DEuONCjS?31(fl))HYp>$ujG5h(E|2y z5hY7^q3RMB(I3Ht50}ZnplQV+w=}^gLJjRZ1=%lp4oY-l_k_}TKi-#n!`xlx7mOtQs}>x@!SZ)l(Nud@ZA#qu?Rdoyjtb?kP>;g1*(@!~VF z@nSwwZbW;*z|a|Gl09onFx}lhVGH7dX>J|FM8XpjK^0dF8$dsZjrx@E#0@J`*|2&# zqtZFPXG=rPhHo>7DYedwTRppFHpc{Q%T_*FA_XsMFz<5qWNCGYbORprPI$;M6+4QC zrlTQQSaQwGn4>M~zOh@SfDo!lpqdPdHz(5)Q=ZEd2vfFJ;WP+MO7+wR?2FP}i-3Kb zX6pi55L%Ar*@Do9jp#;g88+w9pw`mdbrkMDSRC+CdVVb|%eTkYdzK`yR0_>fT!x!9 z6l#Q<@fKSaXi2__L0mdXPLkW+MS);PsY^JCg}X@X%_7LYKZ5Kph>h$?_T>Ysq*A3+ zs*9$wvN+e&Oo@`uN7`r0gSH^FeBN&hLd&P<9RDljbDcKvU1xW$n&Cw_`zAc+J{W`{ zW&iTn`VDd_-aYlFp`b^TRR#A3f4`yM`{-`>+?~aWpwt}l2Fc87uaIOOT^oKGoOJ7LJo{Np60}&hz z=Pa=I&&{|jjbRo>Ead%?E&a9VeZdw)>@4gDbFjh+BC1pq3Moo{^<&J%zV}X$A{1ia zPh(>s7GX=I$QWkF4C>hmZehd)N>LHB&f&=5`A=*~uetZHwji3hXE8GB!Bwbl3lJKM z^BBLjOLvA3U^9^%0-Hu3lB;>xMjNSlUIpeUGRpik9vf6D9?ScJSLMA>7wb#JHSHQs zpvVB@th<<82gIA|`4VN!v>4?;;bZMz@TRHI3Pl zi>9e4XsxJ* zY?DzDTc`h;EtNH+US#~)uxo|%UmvOFO6gn$RuwtT z$|bVMf}-fXNA?W$g|x%J%uqk1FOQ62zBnp|`9s?dqx;C3G@>+BO z-8OC3g|;BHNX};vGaejTYO)KpMyk1R4*2%ic(n|EW6g}BXgcuwEsTnRZm}h!7MDR= z5Esk|>!``9W95@>HN(}A94IKt9&D+K^!(WPu{u!`?X%oe(pcy&+c%gJQBv$aTZ(F? zj4+5xsKc&2hLq>PlY=^p6f%mW_v5kAdmS%Q(W{9B5|KoY(n=@WCwt8ngqDTJ1m|Yj zm%93Xtiq%5MTM+M9B7?EXv}_XY`j^MI4KZFsz5Z!10!GxLzIqswJlM#IK0XhMC^PS z-kWpB%h*~b_c)7-!yANoU`=2G2t`imW#!QtCAdS2Af;6qW3=vvjj^yv@bTDKc@XKB z0nV)21kT{5!Z|5$rZp@AXQ2TV&UiAwImG=4W%#r$>ooU2Yzv~9`xY|*J&6!1*#d;d zA|{Jvh49T_ds-eOp~+hY$$yEhH?2$vM+{m|PBBDAq#FFtmZiE^eBTyC?EKhsz$+td zu!`6SW(FwhUG-|h`ls0dN^l+*>su)Kv|ezl&V6p7?$Ls35-T5izFZ2a^_ADfH9gpy z7+1Jrp_+*+PYwuIk+VR1=n}599d?~Fj0{{29S{+lUIy&joGoc#phTSgeMPsO?G1gdG z780jNW8=tbBu}P~)3Ay5qxZ&G7!^%;$d;m-DGxA+OE_a#a#tx$+3{F9!sAYe#4n1C z#NBXc1nts@qaDd0ZPdKr$@YmmVGBa{_!ELZGd(YmiG{E}v7`5%p%4?^5E~OzVj(r# z%7cg`iPzZ@PWCGK;Ks#V;gO=*x0|v)(?6TVzIH&urc~a#6SPdmXW$I{KytWGZ!ov zsr!O(@>_t=SbWDe)F4ATADwgAWmAr~Q@W+CO`9c$r`fk{)AchmL!fVp4A#6XDp-?5 zt8H5L8yUouWSse|(<}FT7_;ErB+v4+d*R&^7fl=M5@QAu*wBtPH? zTM)7HWtB8Pu!S2(`y;pZ{7`IMS*@%PYM<7i1?>mM$l|CSI{(;~pqeM|vjuU%Jh2|H zSMv5$1$P35A?iM*y$UM(l)_H_zl)70s}e=p9gCZ%vQt6x21_D*b$@M3PtB6AGKfn! zqF*n9^vM%qb4nC?5zbb2%eiS}uk(3m^tfz8O!iXadaT%_oB@drDg_C2#G(R?jmd|o zVg6_BbK*a3L1^Llcfrh=9_E)11%#MltCnkHW6+9(?h{cNFN?8IAi{&O9tgE*>t1CG zqM08S(;hu@67p#QLSu0rC zGyx+#?9a1hv(}-98N`%2bjr!4?GlY!xEusN%pk|IEo!S6^?X|gQ14HjV!}fZJ%jn zTM$~X3br7$c#E#{zf!#S?)9p<6O_b4y^10wRN1S7LU7!jD4B%o8M~$q(6$RUU(0xf z&@ld{*m~Q(L`EXZ-HXFI2C|&_!sU)#vFB&Ap_LTK8r?2p5y|Uq*{wCj?=XldHO0*P zPp)Dnf@3G-PSwOql%hahOSN7Irz3qTHuhXC*dx$j(+X%%MGRD1Wi(8}N10d8v`_kv z+JeyX{t>~wnKrMs??$E$7ct4zpwY)s^vbLDJ7c*jR@t@4orm?yEhz`zjg58N6QdHl zB;w@?J1njSa#+5J@TnBA~LoHZON|XtKSwxGv_R<8*Lzo5z_*M#zM@0EI?>1MBKvygvMfn z?ew0oz>=gtyR`AEp?i$2TE)bIR(m7z;h=&~jaDhq#N&{g&SH~l=O86gk*&yGv_@Ny zgrw%=3`q@9Au{6int7m7{AND~qw%EL3PLp=46LAW)J)_r+o$q# zwjgw`|B@{TJ){X6}lNA_&pTWdyY$daXaztTuflqy`F)-vd z!W{0mC92kqeYPNC=S%NV4_or-^=1cQkYSmEQMF*CTp*Wlh)pB0v8fMXe}xdQKf}5N zAY>L|^CdzC*CN83M{G&1xpv4FMC@F<;a*y#p(RPGI4M!^%gv|y+enbvhdJ()e!YJ$431n zRLK(|{h(RH1ehP?kr=QgpzhK)*n(*0frWa~8cqn71qh8rOmniXBUQ?eqQ&I3TqSHM zx-s^IifT5=%^E!+qB`88woKIh;2~QOv2$R}P`y^B6q~e@$&HP9ly9ADjnMvLY|L1j za1{p0W(*9zbb2lB@t4>VRrBRVwjg5XOV?4aQgG9rgHq(k>I~_~x5mZ{EVUHPCd*Yy zCN;;Yg{>TTvn}~F2i{-{B7P2t87|2O`HMmv_3+2GzXrw z1ra+3HjTLOuXCo>XdSCS)K;y^HVr0Ckq*`$#m1w}3DmGb0uXJy48-*QtO~JU78T`p zUEIpWAK0=$bMbq&ATF4T*0Y$cSi4`SXUJVNo#TPab4N$776$c2%2gyJ2Fdayd3`Ft z<%C2L<@paZE5{wNvFmo)`kW9=3Oq4N0nZ@B$bu;2aACoO<=?DKyP~FDTifT|CR-3% z5RE@gpeDH%mSvNQFV+vj(BSJ-9ryV+`O1Cy3kNH75TtwsZ2dh z-s8M`%Cvfxv;lUH9~4Z~kMP1(AP7{Q0E#^Q`zY zU4{gGXedJ>yPGY${M({3e_m(XKXey+4THFJW61J7Fo(oWwnG&tVPsY{?eYBaxJcPp zfn*ajE>^wi#skxNQIxKKx_ugd*cOBqpbyxB&|)k&-If^tRy$Zgj6Z#8#dsrnSRn_I z-Rss|T~?ebqcvyrg@pOP@R|N-`@&40+a$~_-iiOPC8-wSA2Wz4MR?{N;=$0|lA*u0 zKPJjG76+p*H`+qSRcN_Qb9%Wg2rZ^P3}VI&?;bVgpwey)1sR((=B=?&eQAPg2a>@P zwA@1aUAA=7U3jN0h}cPg<&i3!osZQi^+b+al(0TtE!Rt7XU_{_FSu zXko}Bw$#)NdC(Tb1vA9jPrwUu)2R6Ddk(S_9F$>k%%tsbc;M$_L0+A#T zNlHYLls)cGws7X>Y{{xQbDBY1LT#T!qGByaSs=9778$)0eoh#0GDgN>07{k5V3-XJ zq;H6g_yd%@sIY=S2&`B#OsuHz2V>R3iPyEyiPzYI&@%E{f|*n0b=&5ab}REnY3~qM zJ{22RdQfMAS4xPIvlup7Mc^4*s%a7Us4a+Q!dn=_+MX7IXaPcFaUOHQT1BonN+VVk zmhUkyoh_9MAZ=heYd1E&-@oZxO+Nv1Oy~2|r{Imrxn@9GR$Pv7L9W z1pQMeG^d-cNXBLVE=^RpWmLotT5h4}QZ(PD5xm3}gznXg7{rVl!K?1|v5W*|eFX;! zxzVayZSK}L#>RlvNE@a0ladd*{T4QCx22)(*jsEtG_%1%KWP;w#LEJN#zLyNRamXo z=s@U%ww#}Q&AO676#p~K!eECJV(Zmuso@sZ`%0223k4*L+{=e0I z3z!^Nb*A2$hh|2akz~mR)Am@FWUFVSc}ljyNVY^|J&|NE0nzS9^-P!gML#5sdBwz! z8Gn_<8#f_@FTiF40ox%269=ye#sU1o#*Q4k8^>NSfsKjzV8bSS8$K5Af6lqpw`;1$ zlJ3m4L7!c9ZdK2zbIyP6``&ZCmNG_`QndU3mKstOhen#PI-F9i6&wMGL;sFZ((#tb z?2ePpWaY2jJPA>xO9>apecyt4^R9Ur|J(^s?hUwjZKe2$BppPI!1(fH(O9;Ss7<-= z#p_A0LvbC?q*6{EkKd2Sd*-{XMX`5l71eO?#p2bEbj$W=1xFr3nq56;LFx+t2e*IqD!#) z9h)M_oNh&a$!>bf%Z8=~u0*0l}u#%d}dpzVF6?fuQPy{E1%Q@EaaGYa< zQbH`)NtV#6KTo=Tmo6_fQ~2q+!b~++xM>X&Dm4x2blohF0cQgix>@iu(_wFt0y7B@ zzq&ICm#f`o+p}X1nw^^>zlBKzRCzf1EFIZiD#a&cL?lNC)TsvQE+cJmD|#)mion{)1V(iao8FCmGJ~vt+~RlMMBV!3E7G2G4^Svoz`& zQx-ZStzs5)-r0)o#;H#w><_MBs*FO1@;tbTW;*UWe-R_$@v&(xzQY4TK^ zOTq>~nrtginp#r>ay_${v6PzgY$Bl@((zoOT9%BGoR%AIwPs`$+Ce@prlyZK+PGP< zb<^yve4{A~?e-rqWuaRkg7vIgA(jfy<@>9-;slqNac&y~hTs3%nn$eY&C`cveT#g! z!5f+c4ET~M3OdLAr6~*T3zks0Py*05R9|nCVB80=l#A!Af=#o3w1!Ksibaq8GZcio zq0TC3Q5ukH5+vY%n3`E5^&L|dXN;6_Wwr7x@oKr8iRU9bY4d%a(FtUlm#u-bs&`C% zJnA%jyaA3T8~=Z4YD*2re=v(zud3R(_uAlTo)YdXgXl{%lTMM&5M`MhG+_8hzpcjh z#@=id8y_0Z7v-q7N+6oKMHpKVs1b~2b=vD8fb+Y)W0{KFYmlcmXoN+lz|N3%uxm)ji&Y3LlZ{{QxDR`O3ajn z#_nCFEEIN67-45zd0ep*@zgN&!>7z3Y(^rU$Ye8>30fyqC`mKyuwE<@Tnrk3`t~`3 znhY=vYA(Ut7n%*u{GO?~HR!i7i?gpwjBUR5YHS9^OfQXX36y}$t#9l~|l52WjNSsLwAsbO^hT9Xf$L%pw z7P@SFSfHQl1zX)Hx6l=+AOlfL?BLbAf_;;JvxZs^)|UF8rZ!hGm2MbAKn`ja^5S1j z4XKN)pO~_+kHyZDMRXKpRU@A)AH-tK=DBGRhiVD0r`OS+mP75QVbNsNY!$?rW!tyg zl!bLHE=0bl*XLJb5o#%eeWi5H%dTo35kaP(U3P^vYJg%+j0uXGw#O~zZc znHpN>n-NnM*0EZ(&v9_F11cm`MqK`*tiK>o`CM-eo|U~he4VQm{qbO((qvTSsHrhE z9=~nM!a5#TViXcv7-`iu6)UBTG?Fp;#2{%iZK^My=g5cjmX9ZDjL3bHHRP_OK}(S{ z+8}4W-Js>HFRo`X3G3h~N>o`j8LPR`6d?`r2TWO52f6bQ@GRsaz&bH~G3sZnVTSlG z{Mjg=qaw*8e=V7U<81Y#W`uZ7(rpp)=`vfoHD!-xvD} zt|Qwq4~4bOG@Q5#8$2Rqj4jgo&B(y!fx!SMhpmCKzL!_4BPT`Ub#bC;h=YJOB2JXW z34wIb)WW(v-)G9gI!HPSB%;7iTV`?G8W>&Z_^IHN^4XBl%cd67MlYJOIAfz55A`l> zR;Yi>nz>tgDFPE!A|X5ffvJ(So$p{4GnVdizCNgJcRo_C=5q1UM4;@r&zk+(QJ_ct zPlil*@9a%@k0}f7s$Vc=p<7x4|ZXenAu zvoT45e&wlG>Q~Nvuc-A%eTQaaM<_i<4o2FXt}~f5gi~NWP*;o4B}s#N3Jg+>dgerNFFF>3xO8Qq%9=8pMI?Qh5C zw8-GlWf4F6CQr7(O=w<6P}mgNi*OPY4@L%k>aqF5y|OROk&}=WPM2N((iAL>_ve|# zjJ<=oFXdJrKo&cmaZXSclatVCcVHl+J#EW|82c%x&r>dvv(EYRp}h3e>}~smDGQyK z9us)xdhDm?unH6~69PZChQP{RKS;qVn*aZp+D^On|2Ab|-TZ4*I0Gq6t}5h;RSdW7 zjVA)RVC~@CH0whpiMJLZ?l_);#0910$Rt1(0)+CxYDh85wtkN(3+q6vhV65xS(7q} zg`M2_yps(!@UO52!#Rk}+VJ^k{qrFZwwYQ`=Y|ne7S=)NP%Dvl@jO5v3&gD1z7w{m z_7!!fF#8bp`*)ezOPl;1rYx+Ryv3iw3RwF?)~wwsqx&>lAM))Fnp#O4`hBJ>tQ-2m ztEuB28bOF`sCVsy2TFe08Uz>g@)!b%5*R{2z%n(+5QgSYOzo(DeEN_)hb z{a;~_|C^>Z(q{f!Qx?|Ey!;Sy07Z>JDT^gf@ob>TdC8i+S0E%Gf0#B@$%jKl&x@v3 z(}w?{DGTd{@7zagfVF$ms3(_ADEeEv7828@)$1;1&zT zDjga_W(c+>w^+0MO10xweVZX@Qr-5M+D`lYrKT*boBuq&uqju^5Tk<^OGl`GkMW&A zxpTxC46mz~I}I{KNrot72<3*in%YvsvEP)1bvQZ#fy4sL=F#|ykcrE zZSwb+vaoLQy+;Z~8l}V%Vi+NX-5w#SUI9$Svf@@VG?L zF|chX=8)2Ed&n8eq#09GG_Yw?7S@41 z%~4q&wg%K09hK#o+D)5&%9Mq5)2}@;QIz-v(~H4C@%Ke*5S-gvAqi+T!}L-voD2TK z)Q%d8yG>bGhhojnGL8|bV1zk>B~rK{h zdR^}c2oc2~-B?|SXif=ELJxZcCz7CFZBqq=sgXmatz@mo=+IO3xsvYBE#0n)c&@e2ZG zBcM+bVM#dNYeTjTU^>YTGW)Zb!8AO@^H{3Gv(vE7$!k_XvTbu5yvyi$imG=M!v2wBL`KhqdjwGZXB~bZEpUoDZbi8 zK5fdvqKgQ|=bTC2J;hF8Bk!*1odLUWPJPnUh8l*)O<7onVg2^VE;??U-ZvapW8`lbj(u0qK9 z6STKAjkTbWOeKiP<<>B{xG5$Ggqp|d7;JDQaiWbHCasdPE)&TXs$D5 zVI7*&oO^tuH7L&L+~W_J+D)7Oy{0UzoBoW>J^oW`0G!3S$N$CDh8l)Xn6j`A!-W*5 z3)!ERZy(wex$4qOF%c+7Q6n6}HPvh;9%yAfYz>h&aLg_dC0dFmqasGMP>Q-NMrdH+ z8>Uv(z&vQm!a6V)%?QjU4vQI&IF)1ont!ndO=JdWC{AWPN^*p-`DatZYHWUB%ECG} zZ$?~FoP?zY&t!|I`N@JETWz(Gx2u!0d_lR|KfA*QL%_B{+F7=zmzuJ$4(ux*akkkS zP)&|F3mHCYYBimsE-__c-SFM}=tv|S^hc|i<)qm_*>>2P>3gu7MLvy|aJ0x(U${6s zXlgla{C%b@tQ)_p9^(8UdMNQ=cjUM=%P-f#&5QEgkk6M*ZKlm$G-YwdW;YH?(kkYT zY~LO5^^aMzcw4U+IvUS}UHlJB4W(^;2eWupB8{ja{dI>?oncmv_TOSNR>>K|(bQ!+ zNr7kPumY7DwgL0r$L4kKZkso6 zKR50=d8~8k_oKJ=tA$7YBtw6yknC6SCH5C8PPw1<9t{rlWr`E|M9uvw_`9Sg0$kk( zdVELY#z4{E#w^4@Z<*1E3L02b>!Trqb69!{G-%nTws0uR-^+5J&FH#fq22HvQx-bQ ze?hD_*Fjm{aikDIOak~RmN3!K@nw;GHJK&XO=R-ChnA+=#}Eg$FS4VQankrHW)f3~ z)Jr~%xVfkzX&7nx&jYUd6G%T7$FYZJ7q7!Uo+vZ`w(+bCek%o{N}{N*Kr}(}1v2t+Ke8?Igw16!Y z<+U~}jVdNZny4>LID>34HLuPfeWom?BXl;^iE=d^2U4011}wA`mV#(?ZCBB={LsUV z6=~9%M=Yla$4seQnK|U~^`^*a@Q<3Zux|D@zI}WmvR9gPhw6=p$cFf_c;r9{)g%H> z(~=9!4Ih3_6*DVRC*sWbOm-p|ZRHkgAYFmUu8Ag19SDgrOA(Tqk%XcM$rYan8P3hW zYl?;j^mj~ISO;`n1B(9S{iXo~QjHEhAm z@>6R#wE8Q{nq-I{n_5X5`bVZLtQ&fREbPqsLkm2fAoYwIY``LQCs7JE+q*Bb)okCW z)^%q6Dn|t3Iz{!jeStzHR~Mw3Wm|rkDGTdBU4FWs;M0{6!)95unxr~2QcI-6I68Q z3>AGdO#=k5`GGZTF72g3eHMvB$3i5Wg3B`#p@IEy&-evXOKY(Hrzs2TV6C4q-@HO| zW9j9#`T^@_&PAtbOe}&-v+M=TH)UZRr6^AJa7qXZNP*-EdFiO>@`fi_8p@l-67Ldg zSoLAuTtF)Bt!b%FTMDi>wYSb#7n!p7b%t!8HOOW-4(%FKD{3I#V#?yz8Hl1)AO^!# zP0rMc8VJ{vg>@i$51hy&3{wiD7-$wWpXmrR2=1_E{)>7GC-Mljl;YzPW|+_j!*jc- zZ8bcC;cR6@LMw)$y2sSc8mcdtvakr1S{SzI2pZJ^82*7_aEIEPuKQZ@F3dx{dm!)^ z-&4TxiZA~Mves)5T+V*2*TBkkvewJ}Hddy{IxP}Qt<&;aBk5{B89FCjBtM(^@iPqo z(Ziuwpm}s4TK{yYFZPTn%-U0)Hf8ZzcuMCL)ObG|Pg%u(5qIazQxa2QPa&~R<0&G! z4NswVQIpu_?JU!*R_nExvUn|A<0=OFY+U0S{);(ujZ=Sgai|a>u}>78P;;LQa&rDgo7OwGq2KsDV z<461#bLbk~pWPU84HD}#t|5}!a*eHF*Z8g}uG%%8Gi5Q|HO@wD)t=VtUFASt0~T6} zyG##0l>?ZoURR}%jA8$73^8`FPQT0>VIISQ#CuxZXXw`EK18=w?gMl?-+hE`E8HjP z*6IEo-CEthr(2i%9l9-dAEet7_bIxyx!<5$kNb6b_qTNGa=#|GZ_{m+`z^XHa37^x zoBJ@`THUAVHs5_h9zHG)>11>Kk~Spl`K_wgNz#{{ByB*Z2`ucm@!INVh;Vv*$Da8& z*W3sCiQAs}x76J4DRR&JTWjv3&GLND0wgy0iG@h)^%IMbDEf)TNYwnq5+uItCzc}d zoS$g9wN~|(U_A~NagbDyVgaP+>m%`5ibFy!fI!EETnO@`ie%Xymc$}Fe@V#2AiGHX z1$&l&+^k6Yb=7OZp`t|$GjCBJT!;^zhYdx07V-zL6p}u8y(0N5H~9&&z+;jiQC?Cc z|L8pQbHRs1IjBf}Uhxz3$A2mb67mrtNyr}yxfJB`Wz3}od$4dnZTYzO%% zA(w&thL9Z~Ulg(vH|vkMNs{jbMSr! z#l2Wa689P*N!(+KWV?S{5+wTrLXzzNBqYhc^gL!qvR^7B$-Y;S%%kWh2)mk}AlLu0 zpCH$N&QFl*&wCxSZP^3Y*Ln;oJ(fU^6#Zfe>oF-L>2bG^q{mZ2lIZ^?Bt)-zJ<5C4 ztO(EED7H(%_A?v6b}6%cOh{t;k|M=NH}X&D8&?WR-$)8c=DAf!GS6K?l6f9iB=h*C zB*<2;zl7Pf@bm3{f^793NstG8L`d?0`-CJ9cwR{Ifccj)7xI90LXz+9QY7=fK@tH+ z9#*QiK=r+^2MdUSzso6-|L#Bg36kkSNsv)~C?wgT^?d$QvO`ozvcp@2Bs)}vgdH@R zBkJ3&`1TX@zLmfIuR_unR=t6r(icV)$rAmxpCE~DlLUSHE=BT>p7ay+qn9N?Qbi)n z6j=bzul5u4qj5h$zUlc1^3AVEf;4$vk^JZLFJN9vxK!UF3Bvl2BI!q3;Zdb<8x+2m ze$mDXKPx0D{3{_z;Wu2!KR}s^?;nr^=~z@GKmU{@NUaAI$|H z5~RsdAxYjFg(P?Uw2iQbIgDVC&&0ci`~=CD zk_734ZZq*l>4oQiB?(gQ8AbB*U-$|7OjFn%I)wIaLuaM>Q~2`mGqsEK1G#Q zCrf{`BAMyu`~)%m2T71#zfdIq==@Ip;|1*1JNyKFIqfGXQ{3h!sKEZbpP;tNDL+AN zm7n_wv{l@5yAa3#R@m{h*6Y z{e2>Dd~2JkfhswAxXD_BH7}flmsdIbsFZcRuM z_sc?(xX%emLM@~ASvUSD$^`$>TZN=QDk+jgs;WkRIy0sl{oZo)18}aB6U*dLsHC_X z!`+Fa*n*#rVveOqml9oWqRTCGxq~hrq02|<@-e!6oGyP%mpkclH(lJ*8qQzW-ekpenJn&=d%q*J7uPLYy2McV2VsjX9_zfO@Mbc!sZQ)C*QA{*%x8B3?g zYC1*c(ai2IO9pm#U6J%RmV zHJ#q!WD6&dUYtpxLk*%mr`0v zrKp!ES%2S>%*bLXdQE?V@>D&cA9)>q3a*@{Csl7@vRI^FyQw!JV8yx9%~vee4d|xv zi=gpb7mxP4$LQ*{vY`%?4isUm&WeL59yr7MAgxI)lU@BM+e>3eyE*>T1}nOGJel%Z zwGoa&Vz$dw%9OREFpEo}H++jMrbr4WeRbU|JdBi~VGqRrpkWW(XxIZc8uq}AhCOhj zVGrDB*aJ5j_P~vXJ#eF858Pz>S7IaHC-l+-TSXHyZZ9jfOpNqrw0;8uq}A zhCOhjVGrDB*aJ5j_P~vXJ)lh0Ti_B78{8_C2SQ7+V6%$i2G1CBm!_o~-gk`0%Q3_) zR#8TMU8h*pN!E0d6`f=~Ct1x&)^d`SoMas*S;a}#aFP|AWc?;ty-C(?l9iie-6mPJ zDb{R~6`NwcCRwd1)@q8Cnyj+i<0rJx?w_-6;=&8D2f9IF^{`ywQq^n6{+TiAH)6dX ze&t(bNj9Evu>Au(y%q$+7tr0Wf$#VZuR}R)wvdcx9h4%mBc*u04DSP*gQddBi5k1@ zc-mVkk@QheEZ)tIb89VeYt3tc)rBV{n4m?ok{aLPejVRHc`=@LzX{4)wvX|NH2(FH zGNOGX$Kg^e&-6cOKg_Qq2jgQ-O}-cSi2L`VMvnx^qC-L80rfASbxlQswg9WGbVrr? zDE``gj9DN>A}BeKBF>6E8Y5(DZ!wzb$d8ou7dgk{Sx|UBzw9j<%T}q3;uJ=_GC9aw zu6gasy46YsW?Q}sl_3>c*XA?txjK`qh-o*t7b8r+biQ%oA zlOrR8TL*@=#^Zy@v@@D=;)9zLsYG(~=J>XO0cX@18jj zIyjWtyk#i4WpL}z*5vS(_=vM*+fZ_Jbad;WlTHn#62k+dsezG%;$J0}KPBV;h%P_I z<#rV3sVp}o(Cowmuj6X0$I2GsDKcqU?C5pl0G}}$YmBk|W7Sdyn0T#Jc*a!K=e48s zj?tNUI0BR2uEfA8c_7+MC=6qjOhrl%@0{QZ`Z{4p@O!I*?{Wx%F5Nfpyx<#V7i)OG zCioulJsUnb?T#Ih?}UUj$d`lZSoO zG^lx<`%V9fCTdeCNYXiqzH zIdx0R2_{N8u>?xOW96Fr0?E`R_@Vha?!*yw2%qgD3SgsdP2x1Eat-Ce3jbL-TOEth z${~ELb-a?x#;~O%UUUB!G3!aAv6V-Fsu&id!BD)gY0_Jc=qjmltmu?t2n|%NIJG^G zpjfUZaAtW01qYSp?!OR&PCSg!p+~t4?1QI2VYXzUOnL&}z2Sxlv0!b|>rUn3mGKyi zS~w9?1>mH&qVZ|D7*9IWf0{xTLaS-g>ye+P3RMC(lgDQq=-*i_A}cxY>B_hZ@Fu-3 zMaIyCqkv54pORd@TL7Wtu|(k{UQCVWjw17nXU4{}_@A6oTYS=M&*YE8+mVYIQ8eN@ z$XWA=y7Tt1^X3z9+>)cb7mm9>(cE!6lNebkaCY3uM4P39&Iq4)B z!5C^qr{*rCrz`c-Bu;T~VntZ8<{dB(cNXKGRezsbMZ8A15wpd>8ouVX(4`$0%vp8d zHsyBF)mx*bz*^0+gUlS`Ws|ijx0`>_kwfGyCx#<|9cqM5c^znvBM-!iaB`Fv6B z8Dj#6p7*GHDqkW`C!g|`vHB2<`vdOwh4|??xl9h0EoJCTw}}`2nV%fdEzaRI%6imWu-#(&dGZ&s=|QrGzHE_WThTczHi#Ko5DSpDUC zdbwJ?r1CU{YD~Fj&D}_EI}o+*L?$)nRLU_lZ{17zp^`aW$Wkff9h|kZ_`V`;fQDZi z>M*DpOO`3~ch>zMWnVg1n?hz>!Miu4MWcL-s=~&neg>YPD(Sq&H-t?JrHEv3e!o_+ z0xTHCpbw+>LVYsInah#usjMgYr0$+_-vLv&N68-TcNb_ji(zW)ye C4jTUe diff --git a/doc/LectureNotes/_build/html/E1.html b/doc/LectureNotes/_build/html/E1.html index bc489613c..b86e89e6a 100644 --- a/doc/LectureNotes/_build/html/E1.html +++ b/doc/LectureNotes/_build/html/E1.html @@ -32,7 +32,7 @@ - + @@ -399,7 +399,7 @@ document.write(`

Coding Setup and Linear Regression#

Welcome to FYS-STK3155/4155!

In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants.

-

If you are unable to complete this weekss exercises, don’t worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week’s set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight.

+

If you are unable to complete this week’s exercises, don’t worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week’s set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight.

Learning goals#

After completing these exercises, you will know how to

diff --git a/doc/LectureNotes/_build/html/_sources/E1.ipynb b/doc/LectureNotes/_build/html/_sources/E1.ipynb index 1fa343a6e..5a9b9a23b 100644 --- a/doc/LectureNotes/_build/html/_sources/E1.ipynb +++ b/doc/LectureNotes/_build/html/_sources/E1.ipynb @@ -19,7 +19,7 @@ "\n", "In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants. \n", "\n", - "If you are unable to complete this weekss exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." + "If you are unable to complete this week's exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." ] }, { diff --git a/doc/LectureNotes/_build/html/genindex.html b/doc/LectureNotes/_build/html/genindex.html index 905369861..907e860ee 100644 --- a/doc/LectureNotes/_build/html/genindex.html +++ b/doc/LectureNotes/_build/html/genindex.html @@ -31,7 +31,7 @@ - + diff --git a/doc/LectureNotes/_build/html/intro.html b/doc/LectureNotes/_build/html/intro.html index 179fca5f2..19dc216b0 100644 --- a/doc/LectureNotes/_build/html/intro.html +++ b/doc/LectureNotes/_build/html/intro.html @@ -32,7 +32,7 @@ - + diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html index 506889057..31483789c 100644 --- a/doc/LectureNotes/_build/html/search.html +++ b/doc/LectureNotes/_build/html/search.html @@ -30,7 +30,7 @@ - + diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js index cb504759c..6e819f059 100644 --- a/doc/LectureNotes/_build/html/searchindex.js +++ b/doc/LectureNotes/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Discriminative Modeling": [[23, "discriminative-modeling"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example of discriminative modeling, taken from Generative Deeep Learning by David Foster": [[23, "example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Generative Versus Discriminative Modeling": [[23, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[3, "layers"]], "Layers used to build CNNs": [[5, "layers-used-to-build-cnns"]], "Learning goals": [[0, "learning-goals"], [1, "learning-goals"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[3, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[2, null]], "Linear Regression, basic elements": [[2, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[7, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[7, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[7, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[9, null], [9, "id1"]], "MNIST and GANs": [[6, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[11, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[12, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[7, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[10, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[5, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[7, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[13, "other-techniques"]], "Other types of networks": [[14, "other-types-of-networks"]], "Other ways of visualizing the trees": [[11, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[13, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[4, "partial-differential-equations"]], "Practical tips": [[15, "practical-tips"]], "Practicalities": [[21, "practicalities"], [21, "id1"]], "Predicting New Points With A Trained Recurrent Neural Network": [[6, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[5, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[15, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[11, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[15, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[12, "random-forests"]], "Randomized PCA": [[13, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[14, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[6, null]], "Reducing the number of degrees of freedom, overarching view": [[2, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[4, "reformulating-the-problem"]], "Regression Case": [[12, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[3, "regularization"]], "Reminder on Statistics": [[8, "reminder-on-statistics"]], "Replace or not": [[15, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[8, null]], "Resampling methods": [[8, "id1"]], "Resources on differential equations and deep learning": [[4, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[15, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[7, null], [7, "id1"]], "Same code but now with momentum gradient descent": [[15, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[11, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[14, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[4, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[15, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[11, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[2, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[4, null]], "Solving the one dimensional Poisson equation": [[4, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[4, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[15, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[2, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[15, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[15, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[15, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[10, null]], "Systematic reduction": [[5, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[21, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[2, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[13, "the-algorithm-before-theorem"]], "The Boston housing data example": [[2, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[3, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[11, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[11, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[5, "the-cifar01-data-set"]], "The MNIST dataset again": [[5, "the-mnist-dataset-again"]], "The RELU function family": [[3, "the-relu-function-family"]], "The Softmax function": [[3, "the-softmax-function"]], "The \\chi^2 function": [[2, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[8, "the-bias-variance-tradeoff"]], "The code for solving the ODE": [[4, "the-code-for-solving-the-ode"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[9, "the-logistic-function"]], "The moons example": [[10, "the-moons-example"]], "The multilayer perceptron (MLP)": [[14, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[4, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The plethora of machine learning algorithms/methods": [[23, "the-plethora-of-machine-learning-algorithms-methods"]], "The singular value decomposition": [[7, "the-singular-value-decomposition"]], "The two-dimensional case": [[10, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[13, "towards-the-pca-theorem"]], "Train and test datasets": [[3, "train-and-test-datasets"]], "Two-dimensional Objects": [[5, "two-dimensional-objects"]], "Type of problem": [[4, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[15, "using-autograd"]], "Using forward Euler to solve the ODE": [[4, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[15, "using-gradient-descent-methods-limitations"]], "Visualization": [[3, "visualization"], [3, "id1"]], "Visualizing the Tree, Classification": [[11, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "What Is Generative Modeling?": [[23, "what-is-generative-modeling"]], "What is Machine Learning?": [[2, "what-is-machine-learning"]], "What is a good model?": [[2, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[23, "what-is-a-good-model-can-we-define-it"]], "Which activation function should I use?": [[3, "which-activation-function-should-i-use"]], "Why Linear Regression (aka Ordinary Least Squares and family)": [[23, "why-linear-regression-aka-ordinary-least-squares-and-family"]], "Wisconsin Cancer Data": [[9, "wisconsin-cancer-data"]], "Writing Our First Generative Adversarial Network": [[6, "writing-our-first-generative-adversarial-network"]], "Writing our own PCA code": [[13, "writing-our-own-pca-code"]], "XGBoost: Extreme Gradient Boosting": [[12, "xgboost-extreme-gradient-boosting"]], "scikit-learn implementation": [[3, "scikit-learn-implementation"]]}, "docnames": ["E1", "E2", "chapter1", "chapter10", "chapter11", "chapter12", "chapter13", "chapter2", "chapter3", "chapter4", "chapter5", "chapter6", "chapter7", "chapter8", "chapter9", "chapteroptimization", "clustering", "intro", "linalg", "schedule", "statistics", "teachers", "textbooks", "week34"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["E1.ipynb", "E2.ipynb", "chapter1.ipynb", "chapter10.ipynb", "chapter11.ipynb", "chapter12.ipynb", "chapter13.ipynb", "chapter2.ipynb", "chapter3.ipynb", "chapter4.ipynb", "chapter5.ipynb", "chapter6.ipynb", "chapter7.ipynb", "chapter8.ipynb", "chapter9.ipynb", "chapteroptimization.ipynb", "clustering.ipynb", "intro.md", "linalg.ipynb", "schedule.md", "statistics.ipynb", "teachers.md", "textbooks.md", "week34.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 23], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "00": [2, 3, 7, 13, 23], "000": [3, 5], "00000000e": 23, "001": [4, 10, 15], "004": 7, "00727646693": [2, 23], "0086649156": [2, 23], "01": [2, 3, 4, 7, 11, 13, 15, 22, 23], "0110": 20, "01719003e": 23, "02": [2, 6, 9, 14, 23], "02334824": 23, "02857": 6, "02f": 8, "03077640549": 6, "03097597e": 23, "031": 7, "04": 13, "0458": 11, "05": [6, 8, 23], "062292565": 6, "062435": 23, "06730814": 23, "07": 23, "0713": [2, 23], "07285": 5, "08": 20, "08078025e": 23, "08336233266": 6, "0917": 11, "0n": [2, 23], "1": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23], "10": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23], "100": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "1000": [2, 3, 4, 6, 7, 10, 13, 15, 16, 17, 20, 23], "10000": [4, 7, 8, 12, 13, 15, 20], "100000": 10, "10001": 12, "1001": 20, "1002": 20, "1003": 20, "1005": 20, "1009": 20, "101": 1, "1011": 20, "1013": 20, "1013904243": 20, "1015": 20, "102": 1, "1023": 20, "1024": 5, "1026": 20, "1027": 20, "103": 3, "1030": 20, "1037": 20, "1038": 20, "1040": 20, "1047": 20, "107": 1, "108": 23, "10th": 11, "10x": [2, 23], "11": [1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 22, 23], "110": 23, "1100": 20, "1101": 20, "111": [3, 9, 14], "112": 1, "11340253": 23, "11590451": 23, "116": 1, "117": 1, "118": 1, "12": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 18, 20, 22, 23], "120": 5, "121": [1, 10, 11, 12], "1215pm": [21, 23], "122": [10, 11, 12, 23], "124": [2, 23], "125": 1, "127": [1, 6], "128": [5, 6, 15], "129": 1, "1298": 11, "12pm": [21, 23], "13": [2, 4, 11, 14, 18, 20, 23], "131": 1, "133": 9, "135": 1, "136": 1, "14": [2, 4, 6, 8, 10, 11, 12, 14, 18, 20, 22], "141": 1, "143": 1, "1446729567": 6, "149": 1, "14g": 8, "15": [2, 4, 6, 8, 9, 10, 11, 14, 15, 20, 23], "150": [6, 10], "152": [1, 23], "153760": 23, "156": [1, 23], "157": 23, "158": 23, "159": [1, 23], "15g": 8, "15pm": 23, "16": [3, 4, 5, 6, 7, 10, 11, 12, 20, 23], "160": [1, 23], "1603": 5, "161": 1, "162": 1, "16231451": 6, "163": 1, "16384": 5, "164": 1, "167": 1, "17": [3, 4, 10, 20, 23], "172": 1, "173": 1, "176": 1, "178": 1, "179": 1, "1797": 3, "18": [4, 8, 9, 10, 11, 12, 20, 23], "1807": 6, "18392847": 23, "19": [4, 20, 23], "1940": 2, "1943": 14, "1970": [18, 23], "1973": 11, "1979": 8, "1_1": 14, "1_2": 14, "1_3": 14, "1cm": [2, 10, 12, 20, 23], "1d": [3, 4, 5], "1e": [4, 6, 15, 16], "1e10": 16, "1e4": 8, "1f": 3, "1k": 18, "1n": [2, 23], "1x": [2, 23], "2": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "20": [1, 2, 3, 4, 8, 9, 10, 20, 21, 23], "200": [2, 4, 5, 6, 10, 11, 12], "2000": 2, "2004": 15, "2006": 22, "20072279": 23, "2008": 23, "2010": 3, "2011": 3, "2014": 6, "2015": 3, "2016": [2, 23], "2018": [2, 8], "2021": [8, 16], "2022": 23, "2025": 23, "21": [2, 3, 7, 9, 11, 14, 18, 23], "2116753732": 6, "215pm": [21, 23], "2167072": 23, "22": [2, 3, 7, 14, 15, 18, 23], "221": 10, "225": 6, "22948497": 23, "23": [3, 14, 18, 23], "24": [2, 3, 18, 23], "25": [4, 5, 6, 7, 8, 10, 11, 13], "250": [4, 6, 9, 11], "25000": 2, "250154": 23, "253775": 23, "255": 5, "256": 6, "26": 23, "26303845": 23, "264": 23, "265": 23, "265109911": 6, "266": 23, "269": 23, "27": [2, 3], "270": 23, "27n_": 20, "28": [3, 5, 6], "2830637392": 6, "2861": 20, "2873": 11, "2882": 20, "2886": 20, "2890": [2, 23], "2892": 20, "29": 23, "2915": 20, "2931": 23, "29364655": 23, "294399745619595": 23, "296247": 23, "2968": 23, "2980": 23, "298273": 23, "298375": 23, "2990": 23, "2_": 14, "2_1": 14, "2_2": 14, "2_3": 14, "2_i": 14, "2_m": [8, 20], "2_t": 15, "2_x": 20, "2b": 20, "2cm": 10, "2d": [3, 5, 13, 14, 17, 23], "2e": 8, "2f": [2, 9, 11, 12, 13, 14, 23], "2g": 4, "2g_i": 4, "2k": 5, "2m": 8, "2n": [2, 4, 5, 23], "2nd": 11, "2p": 20, "2pt": 6, "2x": [2, 5, 10, 15, 23], "2x_ix_jy_iy_j": 10, "2x_j": 10, "2y_i": 12, "2y_j": 10, "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "30": [2, 3, 6, 8, 9, 12, 15, 21], "30000": [2, 23], "3072": 5, "31": [14, 18, 20], "315": 8, "3155": [2, 7, 8], "32": [5, 6, 8, 14, 15, 18, 20], "3200": 3, "3250": 3, "3297": 23, "33": [14, 18, 21], "3303": 23, "3310": 23, "332331": 23, "333": 9, "3331": 23, "3337": 23, "34": 18, "3436": [2, 23], "3437": [2, 23], "35": [2, 8, 23], "3581341341": 6, "359": 7, "36": [2, 7, 8, 20], "370782966": 6, "38": 20, "39": [2, 21, 23], "3d": [1, 4, 5, 6, 8, 15], "3f": [3, 5, 11], "3n": 18, "3x": [4, 10], "3x_i": 4, "3y": 10, "4": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "40": [3, 8, 21, 23], "400": 6, "4000": 23, "4050": [22, 23], "41": 18, "4155": [0, 4], "41589548": 23, "42": [3, 6, 10, 11, 12, 18, 23], "43": [2, 9, 18], "4310": 23, "436462435": 6, "44": [2, 18], "45": [21, 23], "46": [21, 23], "462": 9, "47": [21, 23], "479465113": 6, "47958494": 23, "48": 23, "48257387": [21, 23], "49": [7, 8, 13], "49152": 5, "4940954": [2, 23], "4990": 20, "4992": 20, "4997": 20, "4c4c7f": [11, 12], "4d": 5, "4f": 8, "4pm": [21, 23], "4y": 10, "4y_i": 12, "5": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "50": [3, 4, 5, 6, 8, 9, 10, 12, 15, 23], "500": [3, 5, 6, 8, 11, 12, 15], "5018": 20, "506": 2, "507d50": [11, 12], "50j": 15, "50x10": 3, "51": [12, 23], "510": 3, "512132": 23, "5177783846": 6, "53": 11, "54": [8, 20, 23], "5411205": 23, "54894451": 23, "55": [3, 23], "56": 3, "56536": [2, 23], "569": 3, "57": [2, 10, 21, 23], "571": 7, "58": [12, 21, 23], "591317992": 6, "5cm": 20, "5f": 10, "5x": 10, "5y": 10, "6": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 21, 23], "60": [3, 5], "60000": 6, "6019067271": 6, "606439": 23, "625": 9, "63": [2, 3], "64": [3, 5, 6, 15, 18, 23], "64x50": 3, "65": [3, 10, 11], "6887363571": 6, "69": [1, 20], "69069n_": 20, "691": 23, "6n_": 20, "7": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "70": [3, 9], "70653767": 6, "71": 3, "724": 5, "73": 23, "7304881": 23, "75": [7, 8, 10, 13], "76": [21, 23], "765": 9, "77": [21, 23], "7718": 11, "7782028952": 6, "77893972": 23, "78": 23, "7d7d58": [11, 12], "8": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 21, 23], "80": [2, 3, 7, 10], "800": [6, 9], "81": 3, "815am": [21, 23], "85": 3, "8702784034": 6, "88": 23, "8f": 8, "8g": 8, "8n": 18, "8x8": 3, "9": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "90": 3, "9040": 11, "91": [21, 23], "92": [21, 23], "93": 1, "931": [2, 23], "933": 7, "937": 20, "938": 20, "939": [2, 20, 23], "94": 20, "95": [3, 13, 23], "954": 20, "955820c21e8b": 6, "96": 8, "960": 20, "961": 20, "962": 20, "9649652536": 6, "96611194e": 23, "9780387310732": 22, "9780387848570": 22, "9781098134174": 23, "9781492032632": 22, "9781801819312": 23, "98": [1, 2, 3], "985": 20, "986": 20, "989": 20, "9898ff": [11, 12], "99": [1, 15], "991": 20, "992": 20, "993": 20, "996": 7, "999": [11, 20], "9x": 8, "9y": 8, "A": [0, 1, 4, 5, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22], "AND": 4, "And": [2, 5, 6, 7, 8, 11, 15, 17, 20], "As": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 18, 20, 23], "At": [2, 6, 8, 15, 23], "BE": [2, 23], "Be": [4, 17, 23], "Being": 15, "But": [1, 2, 3, 4, 5, 7, 8, 11, 12, 20], "By": [2, 5, 7, 8, 14, 15, 18, 23], "For": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "IF": 8, "IN": 22, "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "Ising": [7, 14], "It": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "Its": [3, 4, 6, 13], "No": [8, 11, 23], "Not": [2, 3, 7, 8], "OR": 20, "Of": 20, "On": [2, 5, 20, 21, 22, 23], "One": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 20], "Or": [2, 3, 8, 23], "Such": [1, 2, 8, 14, 20], "That": [2, 7, 9, 12, 13, 14, 16, 20, 23], "The": [1, 6, 12, 15, 16, 18, 19, 20, 21, 22], "Then": [0, 1, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "There": [0, 2, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 21, 23], "These": [2, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20], "With": [1, 2, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 23], "_": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "_0": [7, 10, 12, 13, 15], "_1": [4, 7, 8, 10, 12, 13, 14, 15, 16, 18], "_2": [4, 7, 10, 13, 14, 15, 18], "_3": 18, "_4": 18, "_9": 15, "__class__": 12, "__doc__": 8, "__future__": [10, 11], "__init__": 3, "__main__": 4, "__name__": [4, 12], "_auto1": [4, 5, 6, 7, 8, 9, 14, 15, 18, 20], "_auto10": [8, 14], "_auto11": 8, "_auto12": 8, "_auto2": [4, 5, 6, 7, 8, 14, 15, 18, 20], "_auto3": [5, 6, 7, 8, 14, 15, 18], "_auto4": [6, 8, 14, 15, 18], "_auto5": [6, 8, 14, 15, 18], "_auto6": [6, 8, 14, 18], "_auto7": [6, 8, 14, 18], "_auto8": [8, 14], "_auto9": [8, 14], "_build": [2, 17, 22, 23], "_c": 3, "_compon": 13, "_depth": 11, "_export": [0, 1], "_fraction": 11, "_i": [2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 23], "_j": [2, 3, 4, 5, 7, 8, 10, 15], "_k": 15, "_l": 14, "_lambda": 8, "_leaf": 11, "_m": 12, "_multilayer_perceptron": 23, "_n": [4, 7, 10, 13, 15], "_node": 11, "_p": [7, 10], "_ratio": 13, "_sampl": 11, "_split": [8, 11], "_t": 15, "_test": 8, "_varianc": 13, "_weight": 11, "a0": 5, "a0faa0": [11, 12], "a1": [2, 23], "a2": [2, 23], "a3": [2, 23], "a4": [2, 23], "a_": [1, 2, 3, 18, 23], "a_0": [2, 23], "a_1a": [2, 23], "a_2a": [2, 23], "a_3": [2, 23], "a_3a": [2, 23], "a_4": [2, 23], "a_4a": [2, 23], "a_h": 3, "a_i": [2, 3, 4, 14, 23], "a_j": [3, 14], "a_k": [2, 3, 14], "aaron": 22, "ab": [2, 4, 7, 15, 16, 23], "ab_channel": 17, "abandon": 3, "abid": 20, "abil": [2, 12], "abl": [1, 2, 3, 6, 7, 8, 9, 12, 14, 15], "about": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21], "abov": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "abovement": [8, 23], "abscissa": 15, "absolut": [2, 4, 7, 8, 15, 23], "abstract": 3, "acceler": 15, "accept": [2, 5, 8, 11], "access": [2, 5, 13, 20, 23], "accid": [6, 8], "accompani": [2, 23], "accomplish": [10, 11, 15], "accord": [2, 3, 4, 7, 8, 11, 14, 15, 16, 20, 23], "accordingli": 13, "account": [0, 1, 2, 5, 7, 15, 20, 23], "accumul": [14, 15, 20], "accur": [2, 5, 6, 8, 12, 15], "accuraci": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 23], "accuracy_scor": [2, 3, 12, 23], "accuracy_score_numpi": 3, "achiev": [2, 3, 7, 8, 10, 14, 18, 23], "aco": 20, "acquaint": 17, "acquir": [3, 17, 23], "acr": 2, "across": [3, 5, 8, 11, 17, 23], "act": [3, 5, 18], "action": 20, "activ": [0, 2, 4, 5, 6, 11, 19, 21, 23], "actual": [0, 1, 2, 3, 6, 7, 8, 10, 13, 18, 20, 23], "ad": [0, 1, 3, 5, 6, 7, 10, 15, 18], "ada_clf": 12, "adaboostclassifi": 12, "adadelta": 15, "adam": [3, 5, 6, 23], "adapt": [6, 8, 15, 22], "add": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 20, 21, 23], "add_subplot": [3, 9, 14, 16], "addendum": 7, "addit": [0, 2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23], "addition": [14, 15], "address": [3, 11, 13, 15, 23], "adjac": [5, 14], "adjoint": 7, "adjust": [2, 7, 14, 15], "admir": [2, 23], "advanc": [6, 8, 14, 22, 23], "advantag": [3, 5, 7, 8, 12, 15, 18], "adversari": 23, "afecionado": 23, "affect": [0, 5], "affin": [2, 5, 10, 13], "afford": 5, "aficionado": 23, "aforement": 16, "african": 2, "after": [0, 1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 17, 18, 20, 23], "afterward": [2, 23], "ag": [2, 9, 23], "ag_0": 4, "again": [2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "against": [3, 6, 9, 12], "agegroup": 9, "agegroupmean": 9, "aggreg": [11, 12], "agorithm": 12, "agre": [7, 8, 20], "agreement": 15, "ahead": 11, "ai": [2, 22], "aid": 13, "aim": [1, 2, 3, 6, 8, 9, 13, 16, 17, 18], "ainv": 7, "airplan": 5, "aka": 7, "al": [1, 2, 4, 6, 22, 23], "alarm": [7, 9], "algebra": [2, 5, 7, 15, 17], "algorithm": [1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 16, 17, 18, 20, 22], "align": [2, 4, 7, 8, 9, 10, 15, 20, 23], "all": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "allevi": [3, 15], "alloc": [5, 18], "allow": [2, 3, 4, 5, 7, 8, 10, 12, 15, 17, 18, 23], "almost": [2, 3, 8, 10, 13, 15, 20], "alon": [4, 11], "along": [0, 4, 5, 6, 7, 8, 11, 12, 13, 17, 18, 23], "alpha": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 23], "alpha_": 12, "alpha_0": 5, "alpha_1": 5, "alpha_2": 5, "alpha_i": [5, 15], "alpha_k": 15, "alpha_m": 12, "alpha_n": 5, "alpha_opt": 15, "alreadi": [0, 4, 5, 6, 7, 8, 12, 14, 17, 18, 20, 23], "also": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "alter": 3, "altern": [2, 3, 6, 7, 8, 10, 11, 13, 15, 18, 23], "although": [1, 2, 3, 7, 8, 10, 12, 15, 23], "alwai": [1, 2, 5, 7, 8, 14, 15, 20, 23], "am": 6, "ame2016": [2, 23], "american": 2, "among": [2, 5, 7, 11, 12, 14, 18, 23], "amongst": 7, "amount": [2, 3, 5, 6, 8, 10, 12, 16, 17], "an": [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22], "an_": 20, "anaconda": [2, 3, 17, 23], "analogi": 15, "analys": 8, "analysi": [3, 5, 6, 9, 16, 18, 22], "analyt": [4, 5, 7, 8, 9, 14, 15, 17, 23], "analyz": [1, 2, 3, 5, 6, 7, 8, 20], "andrew": 3, "angl": [2, 5, 11], "anharmon": 5, "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 23], "anim": [6, 14], "ann": 14, "annot": [2, 3, 5, 9, 10, 23], "announc": 23, "anoth": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "ansatz": [2, 23], "answer": [2, 3, 5, 7, 8, 18, 21, 23], "antialias": [4, 8], "anticip": 6, "anymor": [3, 10], "anyon": [0, 6, 10], "anyth": [0, 1, 3, 20], "anytim": [21, 23], "apach": 3, "apart": [13, 15], "api": [3, 17, 23], "appar": 4, "appear": [2, 3, 5, 15, 18, 20], "append": [3, 5, 6, 10, 11, 15, 23], "appli": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 20, 22, 23], "applic": [1, 2, 3, 5, 6, 7, 8, 9, 11, 14, 15, 18, 20, 22, 23], "apply_gradi": 6, "approach": [0, 1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 22], "appropri": [4, 8, 11, 14, 15, 17, 20], "approv": 23, "approx": [2, 4, 5, 8, 12, 13, 15, 20, 23], "approxim": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 15, 20, 23], "apt": [2, 17, 23], "aq": 20, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "aragorn": 23, "arang": [3, 5, 6, 8, 9, 11, 12, 14, 15, 23], "arbitrari": [3, 6, 8, 10, 14, 15, 20], "arbitrarili": [2, 3, 13, 23], "arc": 8, "architectur": [5, 6, 14], "area": [2, 5, 8, 22, 23], "argmax": [3, 13], "argmin": [6, 12, 16], "argsort": 13, "argu": [3, 15], "argument": [2, 4, 5, 7, 13, 14, 15, 23], "aris": [2, 8, 14, 15, 20, 23], "arithmet": [2, 15, 18, 23], "arm": 8, "armadillo": 18, "around": [2, 3, 6, 7, 8, 13, 20, 23], "arrai": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20], "arrang": [5, 23], "arraybox": 15, "arriv": [2, 8, 11, 13, 18, 20, 23], "arrow": 14, "arrowprop": 10, "art": [2, 3, 17], "articl": [2, 5, 6, 8, 12, 23], "artifici": [2, 4, 9, 14, 22, 23], "artificialneuron": 14, "arug": 15, "arxiv": [5, 6], "asarrai": [2, 8, 11], "ask": [0, 7, 8, 13, 14], "aspect": [2, 8, 17, 23], "assembl": 5, "assembli": [2, 23], "assert": 6, "assess": [2, 8, 23], "assici": 6, "assign": [0, 2, 9, 10, 11, 14, 15, 16, 19, 21, 22, 23], "associ": [2, 8, 11, 14, 16, 20, 23], "assum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "assumpt": [2, 5, 7, 8, 11, 13, 20, 23], "ast": [2, 7, 8, 23], "astyp": [6, 11, 12], "asymmetri": [2, 23], "asymptot": [6, 8], "atom": [2, 23], "attempt": [2, 6, 8, 9, 10, 12, 23], "attend": 23, "attent": [2, 18, 23], "attract": [2, 12, 23], "attribut": [2, 11, 23], "audi": [2, 23], "audio": [5, 6], "august": 23, "aurelien": [2, 22, 23], "austfjel": 8, "auth": 0, "authent": 0, "author": [2, 3, 12, 20], "authour": 23, "auto": [11, 12, 20], "autocor": 20, "autocorrelation_tim": 20, "autocorrelform": 20, "autocovari": 20, "autoencod": [6, 17, 23], "autoencond": 17, "autograd": [17, 23], "autom": [2, 17, 22, 23], "automac": 18, "automag": 23, "automat": [1, 2, 3, 4, 5, 6, 13, 17, 18, 23], "automobil": 5, "autonom": 6, "avail": [2, 3, 6, 8, 12, 13, 17, 18, 19, 21, 22, 23], "averag": [2, 3, 5, 8, 11, 12, 15, 16, 20, 21, 23], "avoid": [2, 6, 7, 8, 11, 13, 15, 18], "awai": [4, 5, 8], "awar": [4, 12], "award": [21, 23], "ax": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 23], "axes3d": [4, 8, 15], "axes_grid1": 8, "axhlin": 10, "axi": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "axiom": 7, "axlabel": 2, "axvlin": [6, 10], "axvspan": 6, "b": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 20, 21, 23], "b1": 10, "b2": 10, "b3": 10, "b_": [2, 3, 18], "b_0": 2, "b_1": [2, 4, 14, 15], "b_2": [2, 15], "b_5": 15, "b_group": 11, "b_i": [2, 3, 4, 14, 23], "b_ia_": [2, 23], "b_ia_i": 2, "b_index": 11, "b_j": [3, 14], "b_k": [2, 3, 14, 15], "b_m": 14, "b_score": 11, "b_valu": 11, "babcock": 23, "bachelor": [19, 21], "back": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 18, 20, 23], "backbon": 18, "backend": [3, 6], "background": [22, 23], "backpropag": 3, "backtrack": 11, "backup": 18, "backward": [3, 4, 6, 14, 18], "bad": 8, "badli": 20, "bag": [11, 17, 23], "bag_clf": 12, "baggin": 23, "baggingboot": 12, "baggingclassifi": 12, "baggingtre": 12, "balanc": 8, "band": 18, "bandwidth": 18, "bar": [2, 8, 13, 23], "barber": 22, "bare": [6, 12], "base": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23], "basi": [7, 9, 10, 12, 13, 14, 15, 18], "basic": [0, 8, 10, 14, 15, 16, 17, 20, 23], "batch": [5, 6, 13, 14, 15], "batch_shap": 6, "batch_siz": [3, 5, 6], "batchnorm": 6, "bay": 9, "bayesian": [7, 17, 22, 23], "becaus": [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 23], "becom": [2, 3, 4, 7, 8, 9, 11, 14, 15, 20, 23], "been": [2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 17, 18, 23], "befor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 18, 20, 23], "beforehand": [2, 20, 23], "begin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 20, 21, 23], "behav": [3, 8, 15], "behavior": [2, 3, 15, 23], "behaviour": 14, "behind": [2, 3, 8, 10, 15, 23], "being": [2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 20, 23], "believ": [11, 18], "belong": [9, 10, 11, 15, 16], "below": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "benchmark": 12, "benefici": [3, 15], "benefit": [2, 3, 6, 13, 15, 17, 23], "bengio": [3, 22, 23], "benign": [3, 9], "besid": [6, 7], "bessel": 7, "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 21, 23], "beta": [1, 2, 3, 5, 7, 8, 9, 12, 13, 15, 23], "beta_": [2, 5, 8, 9, 15], "beta_0": [1, 2, 3, 5, 7, 8, 9, 15], "beta_0x_": 2, "beta_1": [2, 3, 5, 7, 8, 9, 12, 15], "beta_1x_": 2, "beta_1x_0": 2, "beta_1x_1": [2, 9], "beta_1x_2": 2, "beta_1x_i": [9, 15], "beta_2": [2, 5, 15], "beta_2x_": 2, "beta_2x_0": 2, "beta_2x_1": 2, "beta_2x_2": [2, 9], "beta_3": 5, "beta_i": [2, 5, 7], "beta_j": [2, 7, 8, 15], "beta_k": 15, "beta_linreg": 15, "beta_m": 12, "beta_mg_m": 12, "beta_n": 5, "beta_p": 9, "beta_px_p": 9, "betavalu": 7, "better": [2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 23], "between": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 23], "beyond": [2, 3, 7, 8, 10, 15, 23], "bf": [15, 16, 18, 20], "bg": 23, "bgd": 15, "bia": [2, 3, 4, 5, 7, 10, 11, 12, 14, 15, 23], "bias": [3, 4, 5, 7, 8, 11, 14], "big": [2, 3, 4, 7, 8, 16], "bigger": [3, 8], "bigr": 14, "bike": 11, "bilbo": 23, "billion": [5, 14, 17], "bin": [2, 9, 20], "binari": [2, 5, 7, 9, 11, 12, 14, 23], "binarycrossentropi": 6, "bind": 2, "binomi": [17, 20, 23], "binsboot": 8, "bioinformat": 2, "biolog": [3, 14], "bios1100": [17, 23], "bird": [2, 5], "birth": 23, "bishop": [22, 23], "bit": [3, 6, 18, 20, 23], "bitwis": 20, "bivari": 4, "bk": [2, 15], "bla": [18, 23], "black": [10, 11, 16], "block": [8, 12, 17, 18, 20, 23], "blog": 23, "blogpost": 6, "blue": [2, 5], "bmatrix": [2, 3, 5, 7, 9, 10, 13, 15, 18, 23], "bmi": 3, "bodi": [2, 3, 6, 14], "bold": 3, "boldfac": [1, 2, 7], "boldsymbol": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 23], "boltzmann": [14, 17, 23], "book": [22, 23], "book1": 22, "boolean": 6, "boost": [3, 11, 17, 23], "boostrap": 12, "bootstrap": [3, 15, 17, 23], "borrow": 23, "boston_dataset": 2, "bot": 10, "both": [0, 1, 2, 3, 6, 7, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23], "bottl": 9, "bound": [2, 10, 14], "boundari": [4, 6, 10, 13, 14], "box": [6, 11], "boyd": [10, 15], "bracket": [6, 20], "brain": [3, 9, 14], "branch": [11, 23], "break": [2, 6, 8, 13, 16, 23], "breast": [7, 9, 13], "breviti": 15, "brew": [2, 17, 23], "brg": 10, "briefli": [1, 2, 23], "bring": [2, 7, 8, 12], "britt": [21, 23], "broad": 2, "broadli": 23, "brought": [15, 17, 23], "brownle": 6, "browser": [0, 23], "brute": [5, 7, 13], "buffer_s": 6, "bui": 6, "build": [1, 2, 6, 7, 8, 12, 18, 20, 23], "built": [2, 3, 5, 6, 8], "bunch": 13, "busi": 2, "byte": [18, 23], "c": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "c1": [10, 13], "c2": [10, 13], "c_": [2, 10, 11, 12, 15, 20], "c_0": 20, "c_1": 14, "c_2": 14, "c_3": 14, "c_4": 14, "c_i": [14, 15], "c_k": 20, "ca": [3, 23], "cach": 12, "cal": [2, 10, 12, 14, 15], "calcul": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "call": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "calor": 2, "cambridg": [15, 22], "can": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "cancel": [2, 15, 23], "cancer": [7, 12], "cancerpd": 9, "candid": [10, 11, 12], "cannot": [2, 3, 6, 7, 8, 9, 10, 11, 20], "canopi": [2, 17, 23], "canva": [0, 1, 23], "cap": 7, "capabl": [2, 3, 10, 15, 17, 23], "capac": [4, 21], "capita": 2, "captur": [6, 13, 14, 23], "car": [5, 6], "card": [2, 9, 23], "cardin": 3, "care": [0, 13], "carefulli": 15, "carlo": [2, 8, 17, 20, 22, 23], "carri": [4, 8, 9], "cart": 12, "case": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 17, 18, 23], "casella": 22, "cast": 3, "cat": [5, 6], "catch": 2, "categor": [2, 3, 5, 11, 13, 23], "categori": [2, 3, 5, 9, 12, 14, 16, 23], "categorical_crossentropi": [3, 5], "caus": [2, 7, 8, 20, 23], "causal": 2, "causat": [2, 23], "cax": 3, "cb": [8, 23], "cbar": 3, "cc": [2, 3, 7, 15, 23], "ccc": [7, 14], "cdf": 20, "cdot": [2, 4, 8, 14, 15, 16, 18, 20, 23], "celebr": 15, "cell": 6, "center": [2, 3, 8, 9, 10, 11, 13, 16, 20, 23], "central": [1, 2, 5, 7, 8, 10, 18, 23], "centroid": [16, 20], "centroid_differ": 16, "centuri": 5, "certain": [2, 5, 8, 9, 11, 20, 23], "cg": 15, "cha": 2, "chain": [2, 3, 15, 17, 20, 23], "challeng": 0, "chanc": [3, 7, 15, 20], "chang": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "channel": 5, "chapter": [1, 2, 8, 12, 13, 18, 22, 23], "chapter3": 2, "charact": [2, 5, 7, 23], "character": [10, 11, 12, 14, 20], "characterist": [2, 3, 5, 12, 15, 23], "charg": [2, 23], "charl": 2, "chase": 6, "chatgpt": 0, "chd": 9, "chddata": 9, "cheap": 7, "cheaper": [3, 15], "check": [0, 1, 2, 3, 5, 6, 7, 13, 15, 18, 23], "checkmark": 5, "checkpoint": 6, "checkpoint_dir": 6, "checkpoint_prefix": 6, "chen": 12, "chiaramont": 4, "childcar": 1, "children": 1, "choic": [2, 3, 4, 5, 6, 8, 11, 14, 15, 16, 18, 23], "choleski": [7, 18], "choos": [0, 4, 5, 8, 11, 12, 13, 15, 16], "chosen": [1, 2, 3, 4, 8, 10, 11, 12, 15, 20, 23], "chosen_datapoint": 3, "christian": 22, "christoph": [22, 23], "cifar": 5, "cifar10": 5, "circ": [3, 14], "circl": [2, 10, 14], "circuit": 5, "circumfer": 11, "circumv": [3, 7, 15], "ckpt": 6, "clariti": 20, "class": [2, 3, 5, 6, 8, 9, 10, 11, 13, 14, 15, 20, 23], "class_nam": [5, 11], "class_val": 11, "class_valu": 11, "classic": [9, 11, 15], "classif": [2, 5, 7, 8, 9, 10, 13, 14, 17, 22, 23], "classifi": [2, 3, 6, 9, 11, 12, 13, 23], "classificaton": 3, "classifii": 12, "clean": 3, "clear": [3, 7, 12, 14, 15], "clearli": [2, 5, 7, 8, 9, 10, 20], "clever": [3, 12], "clf": [2, 8, 10, 11, 12, 23], "clf3": 2, "clf_lasso": 8, "clf_ridg": 8, "cli": 0, "clip": [5, 20], "clone": [0, 21], "close": [2, 3, 4, 6, 8, 10, 11, 13, 14, 15, 16, 20, 22, 23], "closer": [5, 7, 15], "closest": [10, 13, 15, 16], "closur": [17, 23], "cloud": [17, 23], "cluster": [2, 3, 6, 8, 13, 17, 23], "cluster_label": 16, "cm": [3, 4, 5, 8, 10, 15], "cmap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 23], "cmap_arg": 8, "cmd": [0, 11], "cn_": 20, "cnn": 14, "cnn_kera": 5, "cntk": [17, 23], "co": [2, 4, 5, 8, 11, 15, 23], "code": [5, 6, 8, 9, 10, 17, 18, 20, 22], "coef": [2, 23], "coef0": 10, "coef_": [1, 2, 7, 8, 10, 11, 15, 23], "coeff": 7, "coeffici": [2, 5, 7, 8, 9, 10, 11, 15, 18, 23], "coerc": [2, 8, 23], "coin": [12, 20], "coin_toss": 12, "col": [2, 13, 23], "colab": [17, 23], "cold": 11, "colinear": 2, "collaps": 10, "collect": [2, 4, 8, 12, 13, 17, 20, 22, 23], "collinear": 7, "color": [2, 5, 6, 8, 10, 11, 12, 20], "color_channel": 5, "color_cod": 8, "colorbar": [3, 8], "colsample_bytre": 12, "colsaobject": 12, "column": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 18, 23], "columntransform": 11, "com": [0, 1, 6, 8, 17, 22, 23], "combin": [3, 4, 7, 8, 9, 12, 20], "come": [0, 2, 3, 5, 6, 7, 14, 15, 16, 23], "command": [0, 2, 3], "comment": [2, 6, 7, 8], "commerci": [2, 17, 23], "commit": 0, "commod": [2, 23], "common": [1, 2, 3, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "commonli": [2, 3, 6, 8, 9, 11, 15, 16], "commun": [2, 14], "commut": 5, "commutatitav": 5, "compact": [2, 3, 5, 7, 8, 9, 11, 13, 14, 15, 16, 23], "compair": 2, "compar": [2, 5, 6, 7, 8, 13, 15, 18, 23], "comparison": [4, 6, 15], "compat": 9, "compet": 2, "competit": 12, "compil": [2, 3, 5, 6, 15, 17, 18, 23], "complet": [0, 1, 2, 4, 5, 6, 11, 14, 23], "completenn": 14, "complex": [1, 3, 7, 10, 11, 13, 14, 15, 23], "complic": [2, 3, 11, 15, 23], "compon": [1, 2, 3, 5, 6, 7, 8, 9, 11, 16, 17, 23], "components_": 13, "compos": [11, 14, 15, 16, 17, 23], "compphys": [1, 2, 8, 17, 19, 21, 22, 23], "compress": [2, 23], "compris": 8, "compromis": 7, "compulsori": [17, 23], "comput": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23], "computation": [2, 5, 8, 11, 15, 20, 23], "computationalscienceuio": 23, "concaten": [4, 6, 8, 16], "concav": [3, 15], "concentr": [2, 12], "concept": [2, 4, 17, 23], "conceptu": [14, 15], "concern": [2, 3, 6, 9, 23], "concic": 23, "conclud": [2, 7, 15], "conclus": 3, "cond": 4, "conda": [2, 3, 17, 23], "condit": [2, 4, 6, 7, 8, 10, 11, 13, 15, 20, 23], "conduct": 17, "condwav": 4, "confid": [2, 7, 8, 9, 10, 23], "configur": 5, "confirm": [7, 14], "confus": [7, 8, 9, 12, 18], "confusion_matrix": 11, "congruenti": 20, "conjug": [6, 10], "conjugaci": 15, "conjunct": 5, "connect": [2, 3, 5, 6, 11, 13, 14, 15, 18, 23], "consequ": [7, 8, 10, 12, 14, 15], "conserv": [7, 16], "consid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "consider": [2, 3, 7, 15, 23], "consist": [2, 3, 4, 5, 6, 8, 14, 15, 20], "constant": [1, 2, 4, 6, 7, 8, 10, 14, 15, 20, 23], "constitu": [2, 23], "constitut": [4, 8], "constrain": [3, 5, 7, 9, 13], "constraint": [7, 8, 10, 15], "construct": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 20, 23], "contact": [2, 23], "contain": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "contemporari": 23, "content": [0, 3, 17, 18, 23], "context": [8, 12, 15], "contigu": 18, "continu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "contour": [11, 12, 15], "contourf": [10, 11, 12], "contrast": [3, 6, 11, 12, 14, 23], "contribut": [2, 5, 7, 15, 20, 23], "contributor": 2, "control": [0, 2, 3, 5, 11, 15, 17, 23], "conv": [5, 6], "conv2d": [5, 6], "conv2dtranspos": 6, "convei": 23, "conveni": [2, 7, 8, 14, 15, 18, 23], "convent": 14, "converg": [3, 4, 6, 7, 10, 15, 16, 23], "convergencewarn": 23, "convert": [2, 3, 6, 7, 11, 13, 15, 18, 23], "converttomatrix": 6, "convex": [6, 7, 9], "convinc": 15, "convolut": [3, 6, 17, 23], "cool": [6, 11], "coolwarm": 8, "coordin": [7, 14, 16], "coorel": 2, "copi": [0, 2, 3, 16], "core": 12, "corel": 23, "coronari": 9, "corr": [2, 7, 9, 13], "correalt": [13, 17], "correct": [0, 2, 3, 4, 5, 6, 7, 9, 15, 18, 20, 23], "correctli": [3, 4, 8, 9, 12], "correl": [2, 3, 5, 7, 8, 9, 12, 14, 15, 17, 20, 23], "correlation_matrix": [2, 7, 9, 13], "correspond": [2, 5, 7, 8, 10, 11, 13, 14, 17, 18, 20, 23], "cortex": 14, "cosin": [5, 8], "cost": [1, 2, 4, 5, 7, 8, 9, 10, 11, 14, 15, 23], "cost_deep_grad": 4, "cost_funct": 4, "cost_function_deep": 4, "cost_function_deep_grad": 4, "cost_function_grad": 4, "cost_grad": 4, "cost_sum": 4, "costol": 15, "could": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "coulomb": [2, 23], "count": [0, 2, 11, 19, 20, 21, 23], "counterpart": 23, "countor": 15, "coupl": [6, 7, 8], "cours": [0, 1, 2, 3, 5, 7, 13, 21], "coursework": 0, "courvil": [22, 23], "cov": [7, 8, 13, 18, 20, 23], "cov_xi": [7, 13], "cov_xx": [7, 13], "cov_yi": [7, 13], "covari": [2, 9, 17, 18, 23], "covariance_matrix": [7, 13, 16], "cover": [2, 7, 17, 21, 22], "covert": [2, 23], "covxi": 20, "covxx": 20, "covxz": 20, "covyi": 20, "covyz": 20, "covzz": 20, "cpu": 3, "craft": 5, "creat": [0, 3, 5, 6, 7, 11, 12, 13, 14, 17, 23], "create_biases_and_weight": 3, "create_convolutional_neural_network_kera": 5, "create_neural_network_kera": 3, "create_x": [7, 13], "credit": [2, 9, 21, 23], "crim": 2, "crime": 2, "criteria": [2, 6, 11, 12, 16, 20, 23], "criterion": [11, 12, 15], "critic": 8, "cross": [0, 2, 3, 5, 9, 11, 12, 15, 17, 20, 23], "cross_entropi": 6, "cross_val_scor": 8, "cross_valid": [9, 12], "crossvalid": 8, "crucial": [3, 20], "cs231": 5, "csr_matrix": [18, 23], "csv": [2, 6, 8, 9, 11], "ctnk": 3, "cubic": 2, "cumbersom": 7, "cumsum": [12, 13, 23], "cumul": [9, 12, 20], "cumulative_heads_ratio": 12, "cup": 7, "current": [0, 1, 3, 4, 5, 6, 15, 16, 22], "curs": 2, "curv": [8, 9, 12, 14], "curvatur": 15, "custom": [8, 16], "custom_cmap": [11, 12], "custom_cmap2": [11, 12], "cutpoint": 11, "cv": [8, 9, 12], "cvxbook": 15, "cvxopt": [7, 10], "cycl": [3, 14], "d": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "d2_g_t": 4, "d_f": 15, "d_g_t": 4, "d_net_out": 4, "da": 5, "dagger": [7, 18], "dai": [3, 11, 17], "damp": 5, "darget": 11, "darkr": 20, "dat": [2, 23], "dat_id": [2, 8, 9, 11, 23], "data": [1, 4, 6, 7, 10, 12, 14, 15, 16, 18, 22], "data1": 16, "data2": 16, "data3": 16, "data4": 16, "data_id": [2, 8, 9, 11, 23], "data_indic": 3, "data_panda": 23, "data_path": [2, 8, 9, 11, 23], "databas": 3, "datafil": [2, 8, 9, 11, 23], "datafram": [2, 6, 7, 9, 11, 13, 23], "datapoint": [1, 3, 7, 8, 9, 13, 15], "datasci": [0, 1], "dataset": [1, 2, 6, 8, 9, 10, 11, 12, 13, 15, 16, 23], "datatyp": 6, "date": [0, 23], "daughter": 12, "david": 22, "dbh": 3, "dbo": 3, "dcomposit": 18, "ddot": 4, "dead": 3, "deadlin": 0, "deal": [2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 20, 23], "dealt": 2, "debt": 9, "debug": [2, 7, 8], "decad": [2, 5], "decai": [2, 15, 20, 23], "decemb": [21, 23], "decent": 12, "decid": [2, 4, 5, 7, 8, 11], "decim": [2, 23], "decis": [2, 3, 10, 13, 17, 22, 23], "decision_funct": 10, "decision_tre": 11, "decisiontreeclassifi": [11, 12], "decisiontreeregressor": [2, 11, 12], "declar": [2, 6, 18, 23], "decompos": [7, 8, 18], "decomposit": [2, 8, 14, 23], "decompost": 7, "deconvolut": 5, "decorrel": [12, 15], "decreas": [3, 4, 6, 7, 8, 12, 13, 15], "deduc": [2, 23], "deep": [5, 9, 14, 15, 17, 22], "deep_neural_network": 4, "deep_param": 4, "deep_tree_clf": [11, 12], "deep_tree_clf1": 11, "deep_tree_clf2": 11, "deepen": [7, 17, 23], "deeper": [2, 5, 6, 23], "deeplearningbook": [22, 23], "deer": 5, "def": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 20, 23], "def_covari": 20, "default": [2, 3, 4, 6, 8, 9, 18, 23], "default_tim": 6, "defect": 7, "defici": 7, "defin": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "definit": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "defint": 20, "degre": [0, 1, 5, 7, 8, 10, 11, 12, 13, 20, 23], "del": 3, "delet": [0, 8], "delimit": 6, "deliv": [0, 19, 23], "delta": [2, 4, 5, 8, 10, 14, 15, 16, 23], "delta_": [3, 18], "delta_0": 5, "delta_1": 5, "delta_2": 5, "delta_3": 5, "delta_4": 5, "delta_5": 5, "delta_h": [2, 3, 23], "delta_j": [5, 14], "delta_k": 14, "delta_l": [3, 5], "delta_momentum": 15, "delta_n": [2, 5, 23], "delug": 17, "delv": 2, "demand": 15, "demonstr": [2, 5, 7, 8, 9, 13, 14, 17, 23], "den": 6, "denomin": [3, 7], "denot": [3, 4, 8, 9, 15, 20], "dens": [3, 5, 6], "densiti": [2, 4, 8, 20], "depart": [21, 23], "depend": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 23], "depict": 20, "deploy": [2, 17, 23], "depth": [2, 5, 11, 12, 18], "deriv": [2, 3, 4, 8, 9, 10, 12, 13, 15, 17, 23], "derivati": 15, "derivative_fn": 15, "descend": [7, 11, 13], "descent": [2, 3, 5, 9, 10, 14, 23], "describ": [2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 18, 23], "descript": [2, 10, 11, 23], "design": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 23], "designmatrix": [2, 23], "desir": [2, 4, 6, 7, 15, 16, 23], "desktop": 0, "despit": [3, 14], "destroi": 18, "det": [7, 18], "detail": [2, 8, 13, 15, 16, 18], "detect": [5, 10, 14], "determin": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "determinist": [9, 15, 20], "dev": 3, "develop": [2, 5, 7, 10, 12, 13, 14, 17, 18, 23], "deviat": [2, 3, 4, 6, 7, 8, 20, 23], "devis": 14, "df": [6, 10, 13, 15, 23], "df1": 23, "di": 2, "diag": [7, 10], "diagnost": [3, 12], "diagon": [2, 7, 9, 15, 18, 20, 23], "diagonaliz": 7, "diagram": 12, "diagsvd": 8, "dice": [8, 20], "dict": [8, 10], "dictionari": 2, "did": [1, 2, 3, 7, 8, 9, 12, 13, 16, 23], "die": 3, "diff": 4, "diff1": 4, "diff2": 4, "diff_ag": 4, "diffeent": 10, "differ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "differenti": [1, 2, 5, 17, 18, 23], "difficult": [2, 3, 8, 12, 15, 20, 23], "difficulti": [2, 3, 15, 23], "diffonedim": 4, "digit": [2, 3, 5, 6, 8, 21, 23], "dilemma": 15, "dilut": 3, "dim": [6, 13, 16, 18], "dimens": [1, 2, 3, 4, 5, 6, 7, 10, 13, 16, 18, 23], "dimension": [2, 6, 7, 8, 11, 13, 15, 16, 17, 18, 23], "dimensionless": [2, 5, 23], "diment": 18, "dimnsion": 6, "diod": 5, "direct": [2, 3, 4, 6, 13, 14, 15, 16, 23], "directli": [3, 6, 7, 8, 20], "disadvantag": [2, 23], "disappear": [5, 8], "disc_loss": 6, "disc_tap": 6, "discard": [8, 13], "disciplin": [2, 5, 14], "disclaim": 20, "discord": 23, "discourag": [0, 15], "discov": [2, 23], "discover": 7, "discret": [3, 5, 7, 9, 15], "discrimin": [6, 9, 12, 13], "discriminator_loss": 6, "discriminator_loss_list": 6, "discriminator_model": 6, "discriminator_optim": 6, "discuss": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "diseas": 9, "disguis": 8, "disord": [3, 9], "displai": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "displaystyl": [2, 7, 23], "disregard": [2, 23], "dissimilar": [13, 16], "dist": 16, "distanc": [2, 10, 11, 13, 16, 20], "distance_list": 11, "distinct": [5, 9, 10, 11, 12, 16], "distinctli": 10, "distinguish": [2, 6, 9, 10, 20, 23], "distplot": 2, "distribut": [2, 3, 6, 8, 9, 12, 13, 15, 16, 17, 18, 23], "distrubut": [2, 17, 23], "dive": [2, 10, 18, 23], "diverg": [3, 15], "divid": [2, 3, 5, 7, 8, 9, 10, 11, 13, 14, 20, 23], "divis": [8, 10, 11, 15, 18, 20], "dna": 9, "dnn": [2, 3, 4, 6, 14, 23], "dnn1": 6, "dnn2_gru2": 6, "dnn_kera": 3, "dnn_model": 3, "dnn_numpi": 3, "dnn_scikit": [2, 3, 23], "do": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "doc": [0, 1, 2, 17, 19, 21, 22, 23], "document": [0, 6, 15], "doe": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20, 23], "doesn": [5, 11, 14, 23], "dog": [3, 5, 6], "domain": [7, 10, 15], "domin": [2, 23], "don": [0, 1, 2, 3, 5, 7, 8, 10, 13, 15, 17, 23], "done": [1, 2, 4, 5, 6, 7, 8, 11, 12, 13, 15, 18, 23], "dot": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "doubl": [1, 5, 6, 18, 23], "doubli": 3, "down": [2, 5, 8, 11, 13, 14, 15], "download": [0, 2, 3, 5, 7, 8, 18, 22, 23], "downsampl": 5, "dozen": 3, "dq": 8, "drag": 15, "dramat": 13, "drastic": 6, "draw": [6, 8, 12, 15], "drawback": [2, 3, 5, 15], "drawn": [3, 6, 8, 9, 13, 20, 23], "drive": [5, 6], "driven": 5, "drop": [2, 3, 7, 8, 13, 15, 20, 23], "dropna": [2, 8, 23], "dropout": 6, "dt": [4, 5, 15, 20], "dtype": [2, 3, 5, 6, 16, 18, 23], "dub": [2, 23], "due": [3, 4, 7, 8, 10, 12, 14, 15, 21, 23], "dummi": 2, "dure": [2, 3, 5, 6, 10, 11, 13, 17, 23], "dwell": 2, "dwh": 3, "dwo": 3, "dx": [4, 5, 10, 20], "dx_1": 20, "dx_1p": 8, "dx_2p": 8, "dx_mp": 8, "dx_n": 20, "dxp": 8, "dy": [3, 10, 20], "dynam": 6, "dz": 10, "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 21, 23], "e_": [2, 4, 23], "each": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "eapprox": [2, 23], "earli": [3, 15], "earlier": [2, 7, 9, 10, 11, 13, 14, 15, 23], "earthexplor": 8, "eas": [8, 11, 16], "easi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "easier": [0, 7, 8, 10, 11, 15, 20, 23], "easiest": 15, "easili": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "eastern": [21, 23], "ebind": [2, 23], "eblock": 11, "econometr": 23, "economi": 7, "ecosystem": [17, 23], "ect": 19, "edg": 5, "edgecolor": 8, "edu": 15, "educ": [2, 23], "eff": 20, "effect": [1, 3, 6, 12, 15, 20], "effic": 3, "effici": [2, 5, 12, 15, 17, 18, 20, 23], "efron": 8, "egrad": 15, "eig": [7, 13, 15, 18, 20, 23], "eigen": 20, "eigenpair": [7, 13], "eigenvalu": [2, 7, 10, 13, 15, 18, 23], "eigenvector": [7, 13, 15], "eight": [18, 23], "eigval": [18, 20, 23], "eigvalu": [13, 15], "eigvec": [18, 20, 23], "eigvector": [13, 15], "eir": [21, 23], "eispack": [18, 23], "either": [3, 7, 8, 9, 10, 11, 12, 13, 15, 20, 23], "eivind": 21, "eivinsto": 21, "ekstr\u00f8m": 6, "elabor": 20, "elarn": 5, "electr": [2, 5, 14, 23], "electron": 23, "eleg": 13, "element": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 22], "elementari": [12, 15, 18], "elementwis": [5, 15], "elementwise_grad": [4, 15], "elessar": 23, "elif": 16, "elim": 18, "elimin": [5, 10], "elin": [21, 23], "els": [1, 3, 5, 6, 9, 11, 14, 15, 18], "elu": 3, "elus": [2, 23], "email": [19, 21, 23], "embed": [2, 13], "embodi": 8, "emit": 20, "emner": 22, "emphas": [2, 12, 17, 23], "emphasi": [2, 17, 22, 23], "empir": [3, 13, 20], "emploi": [2, 3, 7, 8, 13, 15, 20, 23], "employ": 2, "empti": [0, 8, 12], "emul": 14, "en": [17, 22], "enabl": 13, "enbodi": 8, "encod": [2, 5, 7, 11, 13, 16, 23], "encompass": [2, 20], "encount": [0, 2, 3, 7, 9, 15, 20, 23], "encourag": 0, "end": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "endpoint": [5, 8], "energi": [2, 6, 8], "enforc": 14, "eng": 22, "engin": [2, 3, 5, 6, 17, 23], "enorm": 5, "enough": [2, 8, 15, 23], "ensembl": [3, 11, 23], "ensur": [2, 3, 4, 5, 7, 8, 13, 15, 20], "entail": 23, "enter": [7, 8], "enthought": [2, 17, 23], "entir": [3, 5, 9, 11, 17, 20, 23], "entiti": [11, 14, 18, 23], "entri": [2, 7, 10, 13, 14, 18, 23], "entropi": [3, 5, 9, 12, 15, 23], "enumer": [2, 3, 4, 5, 6, 8, 10, 23], "env": [20, 23], "environ": [4, 17, 23], "environemnt": 0, "eo": [2, 8], "eol": 2, "eosfit": 2, "epoch": [2, 3, 5, 6, 14, 15, 23], "epsilon": [2, 7, 8, 9, 15, 23], "epsilon_": [2, 23], "epsilon_0": [2, 23], "epsilon_1": [2, 23], "epsilon_2": [2, 23], "epsilon_i": [2, 23], "eq": [5, 15, 16, 18, 20], "eqnarrai": [5, 7, 8], "equal": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "equat": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "equilibrium": [4, 14], "equiv": [5, 15, 18, 20], "equival": [2, 3, 7, 9, 10, 13, 15, 17, 18, 23], "erf": 20, "eriador": 23, "err": [2, 12], "err_": 8, "err_sqr": 4, "errat": 15, "erron": 4, "error": [0, 1, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20], "error_estimate_corr_tim": 20, "error_hidden": 3, "error_output": 3, "escap": 15, "especi": [0, 3, 5, 11, 14, 15], "essenti": [0, 2, 7, 8, 11, 12, 14, 16, 20], "establish": [1, 2, 8, 12, 13], "estim": [2, 3, 7, 8, 9, 12, 13, 15, 17, 20, 23], "estimated_mse_fold": 8, "estimated_mse_kfold": 8, "estimated_mse_sklearn": 8, "et": [1, 2, 4, 6, 22, 23], "eta": [2, 3, 5, 10, 14, 15, 23], "eta0": [10, 15], "eta_": 15, "eta_t": 15, "eta_v": [2, 3, 5, 23], "etc": [2, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20], "ethic": 17, "euclidean": [2, 16], "evalu": [0, 1, 2, 4, 5, 6, 7, 8, 11, 15, 20, 23], "evalut": 15, "even": [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "evenli": 6, "event": [7, 9, 12, 20], "eventu": [2, 7, 8, 13, 14, 15, 21], "everi": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23], "everyth": [1, 6, 14], "everywher": [6, 15], "evolv": 2, "exact": [2, 7, 13, 14, 15, 18, 20, 23], "exactli": [2, 5, 6, 8, 14, 17], "exam": 23, "examin": 8, "exampl": [0, 1, 7, 13, 14, 15, 17, 18, 20, 22], "exce": [3, 14, 15], "excel": [2, 3, 6, 7, 12, 23], "except": [5, 6, 8, 10, 11, 18], "excess": [2, 23], "excit": 2, "exclud": [3, 8, 14], "exclus": [2, 3, 5, 8, 20, 23], "execut": [0, 4, 7, 15], "exemplifi": 15, "exercic": [21, 23], "exercis": [7, 17, 19, 21, 23], "exhaust": 8, "exhibit": [2, 7, 8, 10, 23], "exist": [2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 18, 23], "exit": [7, 18], "exp": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 20], "exp_term": 3, "expand": [7, 9, 13, 15], "expans": [2, 5, 7, 10, 12, 14, 15, 23], "expect": [0, 2, 3, 7, 8, 9, 13, 14, 15, 17, 23], "expectation_value_of_h_wrt_p": 20, "expens": [1, 8, 12, 15], "experi": [0, 2, 3, 8, 10, 15, 17, 23], "experiment": [2, 6, 8, 11, 20, 23], "expert": [3, 11], "explain": [1, 2, 8, 11, 12, 13, 15, 23], "explained_variance_ratio_": 13, "explanatori": [2, 23], "explicit": [2, 5, 8, 15, 18, 23], "explicitli": [2, 6], "explod": 3, "exploit": [2, 5, 14, 15, 23], "explor": [3, 6, 8, 10, 15, 17, 23], "expon": 3, "exponenti": [2, 3, 7, 8, 12, 15, 20, 23], "export": [0, 1, 11], "export_graphviz": 11, "export_text": 11, "exporttext": 11, "expos": 17, "express": [2, 4, 5, 7, 8, 9, 12, 14, 15, 18, 20, 23], "exptmean": 20, "exptvari": 20, "extend": [2, 4, 9, 13, 15, 17, 23], "extens": [0, 2, 14, 17, 23], "extent": [2, 3, 8, 22], "extern": [5, 8, 11], "extra": [0, 3, 5, 7, 21, 23], "extract": [1, 2, 5, 7, 8, 9, 10, 13, 15, 18, 23], "extrapol": [2, 23], "extrem": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 18], "extremum": 15, "extrins": 13, "ey": [2, 7, 8, 15, 16, 18, 23], "f": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 23], "f1": 15, "f11": [2, 23], "f12": [2, 23], "f13": [2, 23], "f1_grad": 15, "f1d": 15, "f2": 15, "f2_grad_x1": 15, "f2_grad_x1_analyt": 15, "f2_grad_x2": 15, "f2_grad_x2_analyt": 15, "f3": 15, "f3_grad": 15, "f3_grad_analyt": 15, "f4": 15, "f4_grad": 15, "f4_grad_analyt": 15, "f5": 15, "f5_grad": 15, "f6": 15, "f6_for": 15, "f6_for_grad": 15, "f6_grad_analyt": 15, "f6_while": 15, "f6_while_grad": 15, "f7": 15, "f7_grad": 15, "f7_grad_analyt": 15, "f8": 15, "f8_grad": 15, "f9": [2, 15, 23], "f9_altern": 15, "f9_alternative_grad": 15, "f9_grad": 15, "f_": 12, "f_0": [5, 12], "f_1": [12, 15], "f_2": [14, 15], "f_3": 14, "f_d": 20, "f_grad": 15, "f_grad_analyt": 15, "f_i": [1, 2, 8, 14], "f_m": [5, 12], "f_n": 5, "f_vec": 4, "face": [15, 23], "facecolor": [8, 10, 20], "facil": [2, 17], "facilit": 14, "fact": [2, 3, 5, 7, 11, 13, 14, 15, 23], "factor": [2, 3, 5, 7, 8, 11, 12, 13, 15, 18, 20, 23], "factori": 15, "fade": 8, "fafab0": [11, 12], "fail": [2, 8, 15, 21, 23], "failur": 9, "fairli": [3, 4, 20], "faisal": 1, "fake": 6, "fake_loss": 6, "fake_output": 6, "fall": [10, 11, 19], "fals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 16, 18, 23], "famili": [2, 9, 10, 20], "familiar": [0, 2, 5, 7, 8, 10, 17, 18, 20, 23], "famou": [8, 14], "far": [1, 2, 5, 6, 7, 8, 10, 13, 14, 15, 16, 23], "fashion": [2, 11, 12, 23], "fast": [3, 5, 8, 12, 14, 15, 17, 20, 23], "faster": [3, 13, 15], "fastest": [15, 18], "favor": 9, "favorit": 20, "fc": 5, "featur": [0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "feature_nam": [2, 3, 9, 11], "feautur": 11, "fed": 3, "feed": [2, 4, 5, 13, 17, 23], "feed_forward": 3, "feed_forward_out": 3, "feed_forward_train": 3, "feedback": [6, 23], "feeddorward": 6, "feedforward": [3, 6, 14], "feel": [0, 1, 2, 7, 8, 13, 15, 17, 21, 23], "feet": 2, "fetch": [0, 8], "few": [3, 5, 6, 7, 11, 20, 23], "fewer": [2, 11, 13, 23], "ffnn": [3, 14], "field": [2, 5, 8, 14, 17], "fifth": [2, 8, 23], "fig": [2, 3, 4, 5, 6, 8, 9, 14, 15, 16, 23], "fig_id": [2, 8, 9, 11, 23], "figaxi": 20, "figsiz": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "figur": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 23], "figure_id": [2, 8, 9, 11, 23], "figurefil": [2, 8, 9, 11, 23], "file": [0, 2, 6, 7, 8, 9, 11, 23], "file_prefix": 6, "filenam": 23, "fill": [7, 11], "filter": [5, 6], "final": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 23], "financ": 2, "find": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "fine": [2, 16], "finish": 4, "finit": [5, 7, 8, 14, 15, 20], "finnicki": 0, "first": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 22], "firsteigvector": 13, "fit": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20], "fit_beta": 8, "fit_intercept": [1, 2, 7, 8], "fit_mod": 11, "fit_transform": [0, 2, 8, 10, 11, 13], "fiti": [2, 23], "five": [2, 11, 23], "fix": [2, 5, 6, 8, 12, 13, 14, 15, 23], "flag": 6, "flat": [14, 15], "flatten": [3, 5, 6, 7, 18], "flexibl": [3, 8, 10, 12, 14, 23], "flip": [21, 23], "float": [2, 5, 6, 7, 11, 13, 15, 16, 18, 23], "float32": [6, 11], "float64": [6, 18, 23], "flop": [7, 18], "flow": [3, 6, 14], "fluctuat": 7, "fly": 13, "fm": 2, "fmax": 5, "fmesh": 15, "fn": 9, "focu": [0, 2, 5, 6, 7, 8, 17, 22, 23], "focus": [3, 8, 9, 18], "fold": [8, 11], "folder": [0, 2, 6, 8, 23], "follow": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "font": [2, 9, 20, 23], "fontdict": 20, "fontsiz": [3, 8, 10, 11, 12, 20], "fontweight": 3, "footprint": 5, "foral": 10, "forc": [2, 7, 8, 12, 13], "forcast": 6, "forecast": [6, 14], "forest": [2, 3, 11, 17, 23], "forget": 13, "form": [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "formal": [5, 6, 16, 20], "format": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 17, 20, 22], "format_data": 6, "formatstrformatt": [8, 15], "formul": [6, 8, 13, 16], "formula": [5, 15, 20], "forth": [6, 14], "fortran": [2, 17, 18, 23], "fortran2003": [17, 23], "fortran90": 20, "fortun": [2, 13], "forward": [2, 5, 8, 17, 18, 23], "found": [3, 4, 6, 7, 8, 14, 15, 23], "foundat": [17, 23], "four": [6, 7, 8, 10, 14, 18, 19, 21, 23], "fourier": [2, 23], "fourierdef1": 5, "fourierdef2": 5, "fourierseriessign": 5, "fourth": [14, 23], "fp": 9, "frac": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "fraction": 11, "frame": 9, "framework": [3, 10, 12, 20], "frank": [7, 13], "frankefunct": [7, 8, 13], "fredli": [21, 23], "free": [0, 1, 2, 8, 13, 15, 17, 18, 20, 21, 22, 23], "freecodecamp": 17, "freedom": 7, "freeli": 2, "freez": 0, "frequenc": [5, 8, 9, 20], "frequent": [2, 10, 11, 15], "frequentist": 17, "fresh": 12, "fridai": [0, 21, 23], "friedman": [8, 22, 23], "friendli": 6, "frodo": 23, "frog": 5, "from": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 22], "from_cod": 11, "from_logit": [5, 6], "from_tensor_slic": 6, "front": [2, 6, 7, 23], "frustrat": 0, "fulfil": [4, 7, 14], "full": [2, 3, 5, 7, 9, 11, 12, 15, 20, 23], "full_matric": 7, "fulli": [5, 8, 14, 20], "fun": [17, 23], "func": 4, "function": [0, 1, 4, 5, 6, 7, 11, 16, 17, 18], "functionali": 13, "fundament": [2, 8, 17, 23], "funtion": 4, "further": [4, 9, 11, 23], "furthermor": [2, 5, 7, 8, 9, 13, 14, 15, 17, 23], "futur": [2, 6, 10, 11, 23], "fy": [0, 19, 21, 22, 23], "fys5419": [22, 23], "fys5429": [22, 23], "f\u00f8470": [21, 23], "g": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 20, 23], "g0": 4, "g_": [4, 11, 12], "g_0": 4, "g_1": [4, 12], "g_2": [4, 12], "g_analyt": 4, "g_dnn_ag": 4, "g_euler": 4, "g_i": 4, "g_m": [5, 12], "g_n": 5, "g_re": 4, "g_t": 4, "g_t_d2t": 4, "g_t_d2x": 4, "g_t_dt": 4, "g_t_hessian": 4, "g_t_hessian_func": 4, "g_t_jacobian": 4, "g_t_jacobian_func": 4, "g_trial": 4, "g_trial_deep": 4, "g_vec": 4, "gain": [3, 7, 9, 11, 12, 15], "galleri": [2, 23], "game": 6, "gamge": 23, "gamma": [2, 4, 10, 11, 12, 13, 15, 23], "gamma1": 10, "gamma2": 10, "gamma_": [2, 23], "gamma_0": 12, "gamma_1": 12, "gamma_1x": 12, "gamma_i": [2, 10, 20, 23], "gamma_j": 15, "gamma_k": 15, "gamma_m": 12, "gamma_x": [2, 23], "gap": 10, "gate": [6, 14], "gather": [2, 3, 14], "gaug": 14, "gaussbacksub": 18, "gaussian": [6, 7, 8, 10, 16, 20, 23], "gaussian_point": 16, "gaussian_rbf": 10, "gave": 15, "gavra": 23, "gbc": 23, "gca": [4, 8, 10, 15], "gd": 3, "gd_clf": 12, "gdclassiffiercgain": 12, "gdclassiffierconfus": 12, "gdclassiffierroc": 12, "gdm": 15, "gdregress": 12, "ge": [3, 7, 9, 20], "gen_loss": 6, "gen_tap": 6, "gender": [2, 23], "genener": 6, "gener": [0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22], "generaliz": 1, "generallay": 14, "generate_and_save_imag": 6, "generate_imag": 6, "generate_latent_point": 6, "generate_simple_clustering_dataset": 16, "generated_imag": 6, "generator_loss": 6, "generator_loss_list": 6, "generator_model": 6, "generator_optim": 6, "genom": 17, "geodes": 13, "geometr": [2, 15, 23], "geometri": 7, "georg": 22, "geotif": 8, "geq": [4, 7, 10, 11, 15], "geron": [2, 22, 23], "get": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20, 21, 23], "get_dummi": 11, "get_paramet": 4, "get_split": 11, "get_yaxi": 10, "get_yticklabel": 8, "gh": 0, "gibb": [17, 23], "gif": 6, "gini": 12, "gini_index": 11, "ginvers": 15, "git": [0, 2, 17, 23], "giter": 15, "github": [2, 17, 19, 21, 22, 23], "gitignor": 0, "gitlab": [2, 17, 23], "give": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 23], "given": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "global": [8, 9, 15], "glorot": 3, "gnew": 15, "go": [0, 1, 2, 3, 5, 7, 8, 10, 11, 13, 14, 15, 23], "goal": [2, 9, 11, 23], "goe": [0, 2, 3, 4, 7, 8, 15, 16, 18, 23], "golden": 15, "gone": 7, "gong": 3, "good": [0, 3, 5, 6, 7, 8, 11, 12, 13, 15, 17, 20, 22], "goodfellow": [6, 22, 23], "googl": [3, 6, 17, 23], "got": [3, 8], "gotten": 23, "gov": 8, "govern": 23, "gp": 22, "gpu": [3, 15, 17, 23], "grad": [4, 15], "grad_analyt": 15, "grade": 19, "gradient": [2, 5, 6, 9, 10, 11, 14, 17, 23], "gradientboostingclassifi": 12, "gradientboostingregressor": 12, "gradients_of_discrimin": 6, "gradients_of_gener": 6, "gradienttap": 6, "gradual": [3, 16], "grai": [6, 8], "graph": [1, 3, 11, 13, 14, 15], "graph_from_dot_data": 11, "graphic": [2, 3, 11, 23], "grasp": 2, "gray_r": [3, 5], "grayscal": 5, "great": [0, 7, 15], "greater": [3, 9, 20], "greatli": 15, "greedi": 11, "green": [2, 5, 11, 20], "grei": 6, "grid": [3, 5, 8, 9, 10, 14, 20], "grossli": 15, "ground": [2, 23], "group": [0, 2, 8, 9, 11, 16, 17, 19, 21, 23], "groupbi": [2, 23], "grow": [3, 5, 11, 12], "growth": [2, 23], "gru": 6, "guarante": [2, 6, 15, 20, 23], "guess": [3, 6, 12, 15, 16], "guestrin": 12, "guid": 3, "h": [0, 2, 3, 7, 8, 10, 15, 20, 21, 22, 23], "h1": 4, "h_": [2, 15, 23], "h_1": [4, 15], "h_2": [4, 15], "h_m": 12, "ha": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "haanen": [21, 23], "habit": 2, "had": [2, 3, 8, 9, 15, 23], "hadamard": [3, 14, 15], "half": [3, 10, 11], "halv": 12, "hand": [2, 3, 4, 5, 7, 13, 14, 15, 17, 18, 20, 21, 22, 23], "handi": 5, "handl": [0, 2, 3, 4, 7, 11, 13, 17], "handle_unknown": 11, "handsid": 14, "handwrit": 14, "handwritten": [3, 7], "happen": [3, 4, 5, 6, 7, 8, 12, 15, 20], "hard": [3, 9, 10, 12, 15], "hardcopi": [17, 23], "harder": [2, 3], "harmon": 5, "hasn": 23, "hassl": [2, 17, 23], "hast": [17, 23], "hasti": [1, 2, 8, 22, 23], "hat": [1, 2, 3, 7, 8, 9, 11, 12, 13, 14, 15, 18], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "haven": 3, "he": [9, 23], "head": [2, 6, 12, 20], "header": [2, 23], "heads_proba": 12, "health": 2, "hear": [2, 15, 23], "heart": [2, 9, 23], "heatmap": [2, 3, 5, 9, 23], "heavili": 2, "heavisid": 3, "height": [3, 5, 8], "held": 15, "help": [0, 1, 2, 3, 6, 14, 15, 23], "helper": [6, 16], "henc": [2, 7, 8, 10, 11, 12, 14, 15, 23], "henrik": [21, 23], "her": 9, "here": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hereaft": [2, 10, 14, 23], "hermitian": 18, "hessenberg": 18, "hessian": [2, 4, 7, 15], "heterogen": [11, 12], "hi": 9, "hidden": [3, 5, 6, 14], "hidden_bia": 3, "hidden_bias_gradi": 3, "hidden_layer_s": [2, 3, 23], "hidden_neuron": 6, "hidden_weight": 3, "hidden_weights_gradi": 3, "hierarch": 7, "high": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 23], "higher": [2, 3, 5, 7, 8, 10, 15, 23], "highest": [3, 4], "highli": [2, 5, 6, 12, 17, 18, 22, 23], "highwai": 2, "hing": 10, "hint": [0, 1, 15], "hip": 17, "hire": 2, "hist": [6, 8, 9, 20], "histogram": [2, 8, 9, 20], "histor": [9, 13], "histori": [0, 5, 6, 14], "hitherto": 7, "hjorth": [21, 23], "hobbi": 20, "hoc": 7, "hoff": 22, "hold": [3, 5, 8, 15, 16], "holder": [2, 23], "home": 2, "homepag": 23, "homework": [8, 15], "homogen": [3, 5, 11, 12, 15], "honchar": 4, "hopefulli": [0, 2, 13, 20, 23], "horizont": 13, "horlyk": [21, 23], "hors": [5, 9, 23], "hot": [3, 11], "hour": [3, 17, 19, 20, 21, 23], "how": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "howev": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hspace": [2, 6, 10, 12, 20, 23], "hstack": 3, "htf": 23, "html": [1, 2, 17, 19, 21, 22, 23], "http": [0, 1, 2, 5, 6, 8, 15, 17, 18, 19, 21, 22, 23], "huang": [2, 23], "huber": [2, 23], "huge": [3, 5, 6, 17], "human": [2, 3, 5, 8, 11, 14], "humid": 11, "hundr": 3, "hungri": 3, "hybrid": 19, "hydrogen": [2, 23], "hyperbol": [3, 6, 14], "hyperparam": 10, "hyperparamet": [5, 6, 7, 8, 11, 15], "hyperplan": 13, "h\u00f8rlyk": [21, 23], "i": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "i0": [2, 23], "i1": [2, 8, 10, 14, 23], "i2": [2, 10, 14, 23], "i3": [2, 14, 23], "i5": [2, 23], "i_": 15, "i_1": [7, 8], "i_2": [7, 8], "ian": 22, "ic": 3, "id": [9, 15], "ida": [21, 23], "idea": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 18], "ideal": [2, 4, 8, 10, 15, 20, 23], "idem": 8, "ident": [7, 8, 14, 15, 18], "identifi": [2, 3, 9, 11, 13, 14, 15, 16, 23], "ieor": 20, "ifi": 22, "ifs": [17, 23], "ignor": [0, 2, 3, 5, 11], "ii": [18, 20], "iii": [18, 23], "ij": [1, 2, 3, 5, 8, 10, 14, 16, 18, 20, 23], "ik": [2, 18, 23], "illustr": [7, 9, 12, 14, 15, 16, 17, 23], "im": 8, "imag": [3, 5, 6, 8, 11, 13, 14, 16, 22, 23], "image_at_epoch_": 6, "image_batch": 6, "image_height": 5, "image_path": [2, 8, 9, 11, 23], "image_width": 5, "imageio": 8, "images_from_seed_imag": 6, "imagin": 3, "immedi": [2, 5, 6, 8, 17, 23], "implement": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 20, 23], "impli": [5, 7, 8, 9, 15, 18], "implicit": 5, "implicitli": [13, 20], "import": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "importantli": 5, "impos": [2, 8, 13, 14, 23], "imposs": [2, 7, 23], "impress": [2, 14, 23], "improv": [0, 2, 6, 7, 11, 12, 13, 15], "impur": 11, "imread": 8, "imshow": [3, 5, 6, 8], "in3050": [22, 23], "in3310": 23, "in4080": [22, 23], "in4300": [22, 23], "in4310": 22, "in5400": 5, "in5550": 22, "in_out_neuron": 6, "inaccur": 15, "inact": 14, "inadequ": [2, 23], "inch": 8, "includ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 17, 20, 21, 22, 23], "include_bia": [8, 11], "incom": [1, 14], "incorrect": 3, "incoveni": 10, "increas": [2, 3, 5, 6, 7, 8, 11, 14, 15, 20, 23], "increasingli": 20, "ind": 8, "inde": [2, 4, 6, 7, 8, 15, 23], "indefinit": 6, "independ": [2, 7, 8, 9, 10, 14, 15, 20, 23], "index": [2, 3, 5, 6, 12, 16, 17, 18, 20, 22, 23], "index_col": [2, 23], "indic": [1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 23], "indispens": 8, "individu": [3, 8, 9, 12, 14, 20, 23], "indu": 2, "indx": 18, "indx1": 4, "indx2": 4, "indx3": 4, "ineffici": [5, 15], "inequ": [10, 15], "inertia": 15, "inf1000": [17, 23], "inf1100": [17, 23], "inf1100l": [17, 23], "inf1110": [17, 23], "inf3000": 23, "infeas": 11, "infer": [2, 3, 6, 8, 22, 23], "inferenc": 3, "infil": [2, 8, 9, 11, 23], "infin": [7, 8, 9, 13], "infinit": 5, "infinitesim": 20, "influenc": [8, 12], "influenti": 3, "info": 23, "inform": [2, 3, 5, 6, 8, 11, 13, 14, 15, 16, 18, 22, 23], "inforom": 0, "infti": [5, 8, 15, 20], "ingeni": 15, "ingredi": [2, 11, 23], "inher": 8, "inherit": [18, 23], "initi": [2, 3, 4, 8, 12, 15, 16, 18, 20, 23], "inject": 16, "inlin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "inner": [2, 15], "inp": 6, "inplac": 15, "input": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 20, 23], "input_dim": 3, "input_shap": [5, 6], "inputs": 3, "inputs_shuffl": [2, 3], "insert": [5, 7, 8, 10, 12, 20], "insid": [2, 6, 9], "insight": [2, 3, 7, 17, 23], "insist": [8, 15], "inspir": [2, 3, 14, 23], "instabl": 4, "instal": [0, 2, 3, 7, 8, 11], "instanc": [1, 2, 3, 4, 6, 8, 11, 13, 15, 23], "instanti": 12, "instead": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 20, 23], "institut": 3, "instruct": [2, 3], "int": [2, 3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 20], "int32": 12, "int_": [5, 8, 20], "int_0": 20, "int_a": 20, "intak": 2, "integ": [3, 4, 15, 16, 18, 20, 23], "integer_vector": 3, "integr": [5, 8, 20, 23], "intellig": [2, 16, 22, 23], "intend": 12, "intens": 3, "intention": 16, "interact": [2, 8, 11, 14, 17, 23], "intercept": [1, 2, 8, 10, 13, 15, 23], "intercept_": [2, 8, 10, 11, 15, 23], "interchang": [7, 14, 18], "interconnect": 3, "interest": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 20, 23], "interfac": [2, 3, 18], "interior": [2, 11, 23], "intermedi": 18, "intern": [3, 12, 14], "interpol": [3, 5, 6, 8, 14], "interpr": 7, "interpret": [0, 1, 2, 3, 8, 11, 12, 14, 15, 18, 20], "interv": [2, 5, 7, 8, 9, 15, 20, 23], "intial": 15, "intract": [2, 6], "intrins": [5, 13, 18, 20, 23], "intro": [17, 22, 23], "introduc": [2, 3, 7, 8, 10, 12, 14, 18, 20, 23], "introduct": [3, 4, 6, 15, 22], "introductori": [2, 6, 18, 22, 23], "intuit": [2, 7, 8, 10, 14, 15, 23], "inv": [2, 7, 15, 23], "invalu": [2, 15, 17, 23], "invari": 3, "invd": 7, "inver": 10, "invers": [2, 5, 8, 15, 23], "inverse_transform": 10, "invert": [1, 2, 7, 9, 12, 15, 23], "invh": 15, "invok": [2, 10], "involv": [2, 4, 8, 9, 13, 14, 23], "io": [2, 17, 19, 21, 22, 23], "ip": [2, 10, 20, 23], "ipca": 13, "ipynb": [17, 23], "ipython": [2, 7, 9, 11, 13, 16, 17, 23], "iq": 8, "iri": [10, 11], "irreduc": 8, "irrelev": 7, "irrespect": [2, 23], "isn": 7, "isnul": 2, "isomap": 13, "issu": [0, 3, 11, 18], "it_arrai": 15, "item": [2, 15, 23], "items": [18, 23], "iter": [3, 4, 6, 8, 10, 15, 16, 20, 23], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "itself": [7, 8, 14, 20, 23], "j": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 22, 23], "j1": 18, "j_": 8, "j_lasso_sk": 8, "j_ridge_sk": 8, "j_sk": 8, "jackknif": [8, 17, 23], "jacobian": [4, 15], "jason": 6, "jax": [17, 23], "jensen": [21, 23], "jerom": 22, "ji": [14, 18], "jit": 15, "jj": [2, 7, 8, 23], "jk": [2, 3, 8, 14, 18, 23], "jl": [2, 23], "jm": 18, "jnp": 15, "job": [0, 4, 10, 12], "join": [2, 6, 8, 9, 11, 23], "joint": [6, 7], "judg": 15, "judgement": 8, "julia": [17, 18], "jump": 20, "junk": 6, "jupit": 23, "jupyt": [0, 1, 2, 17, 22, 23], "just": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "justif": 2, "justifi": [5, 12], "k": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "k0": 9, "k1": 9, "kaggl": 8, "kappa_d": 20, "karl": [21, 23], "karush": 10, "katrin": [21, 23], "keep": [0, 2, 3, 6, 7, 8, 13, 15, 16, 18, 23], "keepdim": [3, 8, 12, 18], "kei": [2, 3, 5, 8, 14], "kept": [6, 8, 16], "kera": [2, 6, 17, 23], "kernel": [2, 3, 5, 17, 23], "kernel_regular": [3, 5], "kernel_s": 6, "kernelpca": 13, "kev": [2, 23], "kevin": [22, 23], "keyword": [18, 23], "kfold": 8, "kg": 3, "ki": 18, "kick": [3, 15], "kiener": 4, "kilomet": 8, "kind": [2, 4, 5, 6, 10, 14, 15, 16, 23], "kj": [8, 14, 18], "kjm": [17, 23], "kkt": 10, "kl": 20, "km": [14, 23], "kmean": 16, "kmeanspoint": 16, "kn_k": 16, "know": [0, 1, 2, 3, 4, 7, 8, 10, 15, 17, 23], "knowledg": [2, 17, 23], "known": [3, 5, 6, 7, 8, 9, 10, 11, 14, 18, 20, 22], "kondev": [2, 23], "kp": 20, "kpca": 13, "kroneck": 16, "kuhn": 10, "kvalsund": [21, 23], "kwown": [2, 23], "l": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "l0": 9, "l1": [2, 3, 5, 9, 23], "l1_l2": [3, 5], "l1regl": 7, "l2": [3, 5], "l_": 18, "l_1": 9, "l_2": [9, 15], "l_j": 14, "la": 15, "la_i": 14, "la_k": 14, "lab": [17, 23], "label": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 23], "labelencod": [9, 12], "labels": [8, 10, 11], "labels_shuffl": [2, 3], "laboratori": 19, "lack": [2, 23], "lagari": 4, "lagrang": [10, 13], "lambda": [2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 20, 23], "lambda_": 13, "lambda_0": 13, "lambda_1": [7, 10, 13], "lambda_2": [10, 13], "lambda_i": [10, 13], "lambda_iy_i": 10, "lambda_jy_iy_j": 10, "lambda_k": 10, "lambda_n": [7, 10], "lamda": 3, "land": [2, 10], "landmark": 10, "landscap": 15, "langl": [2, 8, 13, 20, 23], "languag": [2, 3, 6, 10, 17, 18, 22, 23], "lapack": [18, 23], "laplac": 7, "laptop": [0, 17], "larg": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 22, 23], "larger": [2, 5, 7, 8, 10, 12, 13, 15, 20, 23], "largest": [6, 10, 13], "lasso": [2, 9, 17, 23], "lasso_sk": 8, "last": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 18, 20, 21, 23], "latent": 6, "latent_dim": 6, "latent_point": 6, "latent_space_value_rang": 6, "later": [0, 2, 3, 6, 9, 10, 14, 15, 16, 17, 23], "latest": [0, 6, 17], "latest_checkpoint": 6, "latex": 23, "latter": [2, 5, 8, 9, 10, 13, 15, 18, 20, 23], "lattic": 14, "law": 2, "layer": [2, 6, 15, 23], "lbfg": [9, 11, 12], "lcc": [7, 8], "lda": 13, "ldot": [2, 8, 13, 23], "le": [7, 9, 12, 15, 20], "lead": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "leaf": 11, "leaki": 3, "leakyrelu": 6, "lear": 15, "learn": [5, 6, 7, 8, 9, 10, 11, 12, 14, 18, 21, 22], "learnabl": 5, "learner": 12, "learnig": 23, "learning_r": [10, 12], "learning_rate_init": [2, 3, 23], "learning_schedul": 15, "least": [2, 9, 10, 12, 13, 17, 18, 20], "leat": 15, "leav": [2, 3, 5, 7, 8, 11, 13, 23], "lectur": [2, 3, 7, 12, 13, 14, 15, 17, 18, 19, 21, 22], "lecturenot": [2, 17, 22, 23], "left": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "leftarrow": [10, 14], "legend": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 23], "leinonen": 23, "len": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 18, 23], "length": [1, 2, 3, 5, 6, 10, 11, 15, 17, 23], "length_of_sequ": 6, "leq": [2, 7, 9, 10, 15, 16, 20, 23], "less": [2, 3, 5, 6, 7, 8, 10, 11, 15, 17, 20, 23], "lessen": 3, "let": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "letter": [1, 2, 18, 20, 23], "level": [2, 3, 7, 8, 11, 17, 18, 19, 21, 23], "li": [10, 13, 23], "lib": 23, "liblinear": 12, "librari": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 18, 20, 22], "licens": [2, 3, 17, 23], "lie": [2, 8, 13, 20, 23], "life": [2, 3, 10, 14, 23], "lifetim": 15, "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23], "likelihood": [2, 3, 7, 11, 23], "lim_": 20, "limit": [2, 7, 8, 10, 14, 18, 23], "lin_clf": 10, "lin_model": 2, "lin_reg": 11, "linalg": [2, 4, 7, 8, 10, 13, 15, 18, 20, 23], "line": [0, 1, 2, 5, 8, 10, 13, 15, 23], "line1": 10, "line2": 10, "line3": 10, "line_model": 0, "line_ms": 0, "line_predict": 0, "linear": [1, 3, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20], "linear_model": [0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 15, 23], "linear_regress": 8, "linearli": 7, "linearloc": [8, 15], "linearregress": [0, 1, 2, 8, 9, 11, 23], "linearsvc": 10, "liner": [3, 5], "linerar": 12, "linewidth": [2, 4, 6, 8, 10, 11, 12], "link": [0, 2, 6, 11, 14, 17, 19, 21, 23], "linlag": 7, "linpack": [18, 23], "linreg": [2, 23], "linspac": [1, 2, 4, 5, 6, 8, 10, 11, 12, 15, 18, 20, 23], "linu": 6, "linux": [2, 3, 17, 23], "liquid": [2, 23], "list": [0, 2, 3, 4, 5, 6, 11, 17, 23], "listedcolormap": [11, 12], "literatur": [3, 9, 16, 22], "littl": [3, 5, 11, 14], "live": [1, 10], "ll": [2, 20, 23], "lle": 2, "lloyd": [6, 16], "lmb": [2, 4, 7, 8], "lmbd": [2, 3, 5, 23], "lmbd_val": [2, 3, 5, 23], "lmbda": 15, "ln": [3, 15], "load": [2, 3, 6, 8, 9, 11, 12], "load_boston": 2, "load_breast_canc": [3, 9, 11, 12, 13], "load_data": [5, 6], "load_digit": [3, 5], "load_iri": [10, 11], "loc": [2, 5, 8, 9, 10, 11, 12, 23], "local": [0, 2, 3, 5, 9, 14, 15], "locat": [0, 4, 5, 10], "log": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 18, 23], "log10": [2, 7, 8], "log_": [2, 23], "log_clf": 12, "logarithm": [2, 7, 9, 18, 23], "logic": [2, 3, 11, 23], "login": 0, "logist": [2, 3, 4, 10, 11, 12, 13, 14, 15, 17], "logisticregress": [9, 11, 12, 13], "logit": 9, "logreg": [9, 11, 12, 13], "logspac": [2, 3, 5, 7, 8, 23], "long": [2, 3, 5, 6, 14, 15, 23], "longer": [4, 5, 10, 12, 16, 18, 20, 23], "loocv": 8, "look": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "loop": [1, 3, 6, 8, 12, 14, 16, 17, 18, 23], "lose": 3, "loss": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 18, 23], "loss_fil": 6, "lossfil": 6, "lost": 6, "lot": [1, 2, 3, 6, 8], "low": [2, 8, 11, 12, 13, 23], "lower": [1, 2, 3, 5, 8, 11, 12, 18], "lowercas": [18, 23], "lowest": [11, 15, 20], "lr": [3, 5, 6, 12], "lstat": 2, "lstm": 6, "lstm_2layer": 6, "lstsq": [2, 23], "lt": 8, "lu": [2, 7, 23], "lubksb": 18, "luckili": 4, "ludcmp": 18, "lux": 18, "lvert": 3, "lw": [2, 23], "m": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23], "m_": [11, 14], "m_1": 16, "m_h": [2, 23], "m_k": 16, "m_l": 14, "m_n": [2, 23], "m_p": [2, 23], "m_t": 15, "ma": 13, "machin": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 22], "machinelearn": [1, 2, 8, 17, 19, 21, 22, 23], "mackai": 22, "made": [2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 23], "mae": [2, 23], "magic": 6, "magnitud": [3, 8, 9, 15], "mai": [2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "mail": [19, 21], "main": [2, 3, 5, 6, 7, 8, 9, 11, 18, 22], "mainli": [2, 7, 8, 9, 11, 23], "maintain": 8, "major": [3, 8, 11, 12, 15, 18, 23], "make": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 22, 23], "make_axes_locat": 8, "make_moon": [10, 11, 12], "make_pipelin": [2, 8, 12], "makedir": [2, 8, 9, 11, 23], "makeplot": 2, "malcondit": 18, "malign": [3, 9, 11], "mammographi": 7, "manag": [0, 2, 4, 5, 17, 23], "mandatori": [21, 23], "mani": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 22, 23], "manifold": 13, "manner": 5, "manual": 8, "map": [2, 3, 4, 8, 9, 10, 13, 14, 16, 20, 23], "margin": [2, 7, 10], "marit": [2, 23], "mark": 23, "marker": [2, 9, 18, 23], "markov": [17, 23], "marsaglia": 20, "mass": [2, 3, 7, 15], "massag": [2, 23], "masses2016": [2, 23], "masses2016ol": [2, 23], "masses2016tre": 2, "masseval2016": [2, 23], "master": [19, 21], "mat": [17, 23], "mat1100": [17, 23], "mat1110": [17, 23], "mat1120": [17, 23], "match": [0, 3, 6, 7, 15, 16], "materi": [0, 6, 7, 9, 15, 18, 19, 21], "math": [5, 9, 14, 15, 18, 20, 22, 23], "mathbb": [2, 6, 7, 8, 9, 10, 13, 14, 15, 16, 18, 20, 23], "mathbf": [2, 7, 8, 9, 10, 15, 18, 23], "mathcal": [3, 7, 8, 9, 15], "matheemat": 5, "mathemat": [2, 8, 13, 14, 15, 17, 18, 20, 22, 23], "mathemati": 23, "mathrm": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "matmul": [3, 4, 7], "matnat": 22, "matplotlib": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "matric": [1, 2, 3, 5, 6, 8, 9, 10, 13, 15, 17], "matrix": [2, 4, 5, 6, 8, 9, 10, 12, 15, 20], "matshow": 3, "matter": [4, 5, 15], "max": [2, 3, 4, 5, 6, 11, 12, 14, 15, 21, 23], "max_depth": [2, 11, 12], "max_diff": 4, "max_diff1": 4, "max_diff2": 4, "max_it": [2, 3, 10, 15, 23], "max_iter": 16, "max_leaf_nod": 12, "max_sampl": 12, "maxdegre": [2, 8, 12], "maxdepth": 12, "maxim": [3, 6, 7, 9, 10, 13], "maximum": [2, 4, 5, 7, 9, 10, 11, 12, 15, 16, 23], "maxpolydegre": [7, 8], "maxpooling2d": 5, "mbox": [7, 8], "mcculloch": 14, "md": 13, "mdoel": 6, "mean": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "mean_absolute_error": [2, 23], "mean_divisor": 16, "mean_i": 20, "mean_matrix": 16, "mean_squared_error": [0, 2, 6, 8, 9, 12, 23], "mean_squared_log_error": [2, 23], "mean_vector": 16, "mean_x": 20, "meaning": [2, 6, 9, 23], "meansquarederror": [2, 23], "meant": [5, 9, 12, 15], "measur": [1, 2, 3, 4, 7, 8, 11, 13, 14, 16, 20, 23], "mechan": [2, 6, 20, 23], "median": [2, 23], "medicin": 14, "medium": [6, 10, 15], "medv": 2, "meet": [2, 21], "mehta": [2, 23], "memori": [5, 6, 13, 14, 15, 18], "mention": [2, 14, 15, 20, 23], "mere": 2, "meshgrid": [4, 7, 8, 10, 11, 12, 13], "mess": 0, "messag": [7, 15], "messi": 4, "met": [2, 5, 10], "meteorolog": 11, "meter": 8, "method": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 16, 17, 18, 20, 22], "metion": 8, "metric": [0, 2, 3, 5, 8, 9, 11, 12, 16, 23], "metropoli": [17, 23], "mev": [2, 20, 23], "mgd": 15, "mglearn": [17, 23], "mgrid": 15, "mhjensen": 23, "mi": 12, "mia": [21, 23], "microsoft": 22, "mid": 3, "midel": 6, "midnight": 0, "midpoint": 11, "might": [0, 2, 3, 4, 6, 8, 11, 15], "mild": 11, "millimet": 8, "million": [2, 23], "mimic": 14, "min": [2, 4, 7, 10, 11], "min_": [2, 4, 7, 16, 23], "min_samples_leaf": 11, "mind": [0, 2, 8, 15, 23], "mindboard": 6, "mine": [17, 23], "mini": [3, 13, 14, 15], "minibatch": [3, 13, 15], "minibathc": 15, "miniforge3": 23, "minim": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "minima": [2, 3, 9, 15, 23], "minimum": [2, 3, 4, 8, 10, 11, 13, 15], "minmaxscal": 2, "minor": 20, "minst": 3, "minu": 9, "mirjalili": 23, "mirror": 11, "misc": 8, "misclassif": [10, 11, 12], "misclassifi": [10, 12], "miser": 2, "mismatch": 3, "miss": [2, 9, 12], "mistak": 6, "mit": 22, "mix": [3, 4, 23], "mixtur": 15, "mk": [11, 18], "mkdir": [2, 8, 9, 11, 23], "ml": [2, 3, 12, 15, 18], "mlab": 20, "mle": [7, 9], "mlp": 3, "mlpclassifi": 3, "mlpregressor": [2, 23], "mm": 18, "mn": [14, 20], "mnist": [3, 13], "mod": 20, "mode": [19, 21, 23], "model": [1, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 20, 22], "model_select": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "moder": 12, "modern": [2, 8, 9, 17, 23], "modif": [4, 14, 15], "modifi": [2, 3, 5, 7, 9, 10, 12, 14, 15, 23], "modul": [1, 2, 18, 23], "modular": 20, "modulo": 20, "moe": 13, "moment": [7, 8, 15, 20], "mondai": [21, 23], "monitor": 15, "monoton": [7, 14, 20], "mont": [2, 8, 17, 20, 22, 23], "montli": 1, "moor": [7, 8], "more": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "moreov": [2, 5], "morten": [21, 23], "mortenhj": 23, "most": [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "mostli": [3, 13], "motion": [2, 15], "motiv": [3, 6], "move": [0, 1, 2, 6, 7, 8, 9, 11, 14, 15, 16, 20], "mpl": [2, 9, 23], "mpl_toolkit": [4, 8, 15], "mplot3d": [4, 8, 15], "mplregressor": 3, "mse": [0, 1, 2, 6, 7, 8, 11, 12, 23], "mse_simpletre": 12, "mselassopredict": 7, "mselassotrain": 7, "mseownridgepredict": 8, "msepredict": 7, "mseridgepredict": [2, 7, 8], "msetrain": 7, "msle": [2, 23], "mt": [9, 14], "mu": [2, 8, 13, 15, 20, 23], "mu0": 20, "mu1": 20, "mu2": 20, "mu_": [8, 20], "mu_i": 8, "mu_n": 13, "mu_x": 20, "much": [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "multi": [2, 3, 5, 9, 17, 23], "multiclass": [3, 9], "multidimension": [13, 14, 23], "multilay": 3, "multinomi": 9, "multipl": [0, 4, 6, 7, 8, 9, 14, 15, 20], "multipli": [5, 7, 8, 13, 15, 18, 20], "multiplum": 10, "multivari": [2, 4, 12, 13, 17, 20, 23], "multivariate_norm": [13, 16], "multpli": 1, "murphi": [13, 22, 23], "must": [0, 3, 4, 7, 8, 10, 12, 14, 15, 16, 20], "mutat": 9, "mutual": [3, 5, 8, 15], "mx_": 20, "my": 23, "myenv": 23, "myriad": [2, 17, 23], "mz1": 20, "mz2": 20, "m\u00f8svatn": 8, "n": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "n1": 18, "n2": 18, "n_": [3, 4, 5, 10, 14, 20], "n_0": [14, 20], "n_boostrap": [8, 12], "n_bootstrap": 8, "n_categori": [3, 5], "n_cluster": 16, "n_compon": 13, "n_epoch": 15, "n_estim": 12, "n_examples_to_gener": 6, "n_featur": 3, "n_filter": 5, "n_hidden": 4, "n_hidden_neuron": [2, 3, 23], "n_i": 20, "n_input": [2, 3, 5], "n_instanc": 11, "n_job": 12, "n_k": 16, "n_l": [14, 20], "n_layer": 3, "n_m": 11, "n_neuron": 3, "n_neurons_connect": 5, "n_neurons_layer1": 3, "n_neurons_layer2": 3, "n_point": 16, "n_sampl": [8, 10, 11, 12, 16], "n_split": 8, "n_step": 6, "n_t": 4, "n_x": 4, "nabla": [3, 15], "nabla_": [4, 15], "nabla_w": 15, "nag": 15, "naimi": [2, 23], "naiv": 9, "naive_kmean": 16, "name": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23], "narrow": 15, "nation": [3, 7], "nativ": [17, 23], "natur": [2, 3, 6, 10, 11, 14, 15, 20, 22, 23], "navier": 14, "navig": 0, "nb": 20, "nb_": 18, "nbconvert": 23, "nd": 16, "ndarrai": 8, "ne": [11, 12, 18, 20], "nearest": [3, 5, 8, 13], "nearli": 15, "neat": 23, "neccesari": 8, "necess": 4, "necessari": [2, 3, 5, 6, 10, 16, 23], "necessarili": [2, 6, 13, 20, 23], "necesserali": 7, "neck": 9, "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "neg": [2, 3, 5, 7, 8, 9, 12, 15, 18, 20, 23], "neg_mean_squared_error": 8, "neglect": 20, "neglig": 20, "neighbor": [5, 8, 13], "neither": [6, 15], "neq": [15, 16, 20], "nervou": 14, "nest": [11, 14], "nesterov": 15, "net": [4, 6, 14], "netlib": [18, 23], "network": [2, 11, 15, 17, 22], "neural": [2, 15, 17, 22], "neural_network": [2, 3, 4, 23], "neuralnetwork": 3, "neuron": [3, 4, 5, 6, 14], "neutral": [2, 23], "neutron": [2, 23], "never": [3, 6, 8, 11, 20], "new": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "new_chang": 15, "new_hobbit": 23, "newaxi": [2, 5, 8, 11], "newli": [2, 23], "newton": [3, 9, 10, 15, 20], "next": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 23], "next_guess": 15, "next_input": 6, "ng": 3, "ni": 16, "nice": [2, 3, 7, 13, 23], "niter": 15, "nitric": 2, "nlambda": [2, 7, 8], "nlp": 22, "nm": 20, "nm_n": [2, 23], "nmse": 8, "nn": [4, 7, 8, 14, 18, 23], "nn_model": 3, "nnmin": 4, "node": [3, 5, 11, 12, 14], "nois": [2, 6, 7, 8, 10, 11, 12, 15, 23], "noise_dimens": 6, "noisi": [3, 8], "non": [2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "none": [2, 3, 4, 6, 7, 11, 12, 15, 20, 23], "nonlinear": [5, 8, 10, 11, 13, 14], "nonneg": [8, 11, 15], "nonparametr": 8, "nonsens": 20, "nonsingular": 18, "nonumb": [5, 9, 10, 15, 18], "nor": [3, 6, 15], "norm": [2, 3, 7, 8, 10, 13, 15, 23], "normal": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "normali": [18, 23], "norwai": [8, 23], "notat": [2, 4, 7, 8, 15, 16, 20, 23], "note": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 22, 23], "notebook": [0, 1, 2, 3, 5, 11, 17, 23], "noth": [3, 4, 7, 10, 14, 16, 20], "notic": [6, 7, 14, 15, 18, 20, 23], "notion": 5, "novel": [5, 8, 12, 23], "novemb": [3, 21, 23], "now": [0, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23], "nowadai": [2, 3, 5, 11, 17, 23], "nox": 2, "np": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "npr": 4, "nsampl": 8, "nt": 4, "nu": 20, "nuclear": 7, "nuclei": [2, 20, 23], "nucleon": [2, 23], "nucleu": [2, 23], "num": 6, "num_coordin": 4, "num_hidden_neuron": 4, "num_it": 4, "num_neuron": 4, "num_neurons_hidden": 4, "num_point": 4, "num_tre": 12, "num_valu": 4, "number": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 23], "numberid": 9, "numberparamet": 5, "numer": [2, 7, 8, 11, 12, 13, 14, 15, 17, 18, 22, 23], "numpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "nunmpi": 7, "nx": 4, "ny": 20, "o": [2, 3, 6, 7, 8, 9, 10, 11, 13, 18, 21, 22, 23], "obei": [8, 13, 15], "object": [0, 2, 3, 6, 10, 12, 18, 23], "obliqu": 7, "observ": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "obtain": [2, 3, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "obviou": [7, 8, 13, 20], "obviouli": 23, "obvious": [2, 6, 7, 8, 18, 23], "occupi": 2, "occur": [2, 8, 10, 11, 18, 20, 23], "octob": [21, 23], "od": 2, "odd": [2, 5, 9, 23], "odenum": 4, "odesi": 4, "oen": 2, "off": [3, 5, 6, 7, 11, 15, 20], "offer": [8, 13, 17, 18, 19, 21, 23], "offic": [21, 23], "offici": [19, 23], "often": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "ofter": [18, 23], "ol": [2, 15], "old": [0, 3, 7, 12, 15], "ols_paramet": 1, "ols_sk": 8, "ols_svd": 8, "olsbeta": [2, 7], "omega": [4, 5, 8], "omega_0": 5, "omit": [2, 7, 23], "onc": [3, 8, 11, 13, 15], "one": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 23], "onehot": 3, "onehot_vector": 3, "onehotencod": 11, "ones": [1, 2, 4, 7, 8, 10, 11, 12, 13, 15, 18, 23], "ones_lik": 6, "onl": 5, "onli": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "onlin": [0, 13, 19], "onto": [7, 13], "open": [0, 2, 3, 6, 8, 9, 11, 17, 19, 21, 23], "oper": [0, 1, 2, 3, 5, 7, 8, 12, 13, 14, 15, 17, 20, 23], "operation": 20, "oplu": 20, "opmiz": 15, "opportun": 2, "oppos": [8, 15], "opposit": [3, 7, 10], "opt": [3, 7, 23], "optim": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16], "optimis": [3, 5], "option": [0, 2, 3, 5, 7, 8, 10, 13, 18], "optmiz": [3, 10, 15], "oral": 23, "orang": 2, "order": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "ordinari": [2, 4, 5, 9, 13, 15, 17], "oreilli": [22, 23], "org": [1, 2, 5, 6, 17, 18, 22, 23], "organ": [8, 9, 12, 18], "orient": [3, 7, 20], "origin": [0, 2, 5, 7, 8, 10, 13, 14, 15, 18, 23], "orthogn": 7, "orthogon": [2, 7, 8, 10, 13, 15, 18, 23], "orthonorm": 7, "os": [21, 23], "oscar": 3, "oscil": [5, 15], "oskar": 23, "oskarlei": 23, "oslo": [2, 17, 19, 21, 23], "osx": [2, 17, 23], "other": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 15, 16, 17, 19, 20, 21, 22], "otherwis": [2, 3, 6, 9, 15, 18, 23], "ouput": [7, 9, 14], "our": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20], "ourmodel": 2, "ourselv": [2, 7, 8, 10, 13, 15, 23], "out": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "out_fil": 11, "outcom": [2, 9, 11, 12, 14, 20], "outdoor": 11, "outer": [8, 14, 15], "outfil": 6, "outlier": [2, 10, 23], "outlin": [8, 12, 13], "outlook": 11, "outperform": 12, "output": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "output_bia": 3, "output_bias_gradi": 3, "output_shap": 6, "output_weight": 3, "output_weights_gradi": 3, "outputlayer1": 14, "outputlayer2": 14, "outsid": 6, "over": [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 14, 15, 18, 23], "over1": 15, "overal": [3, 12], "overcast": 11, "overcom": [14, 15], "overdetermin": [2, 23], "overfit": [2, 3, 5, 8, 11, 12, 15], "overflow": 7, "overhead": 14, "overlap": [5, 9, 10, 11], "overlin": [2, 7, 8, 11, 12, 13, 16, 18, 23], "overst": 2, "overtrain": 6, "overview": 5, "own": [1, 6, 7, 8, 10, 14, 15, 17, 18], "owner": 2, "ownmsepredict": 2, "ownmsetrain": 2, "ownridgebeta": [2, 8], "ownypredictridg": 2, "ownytilderidg": 2, "oxid": 2, "p": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "p0": 4, "p1": 4, "p_": [4, 6, 10, 11], "p_hidden": 4, "p_i": [7, 20], "p_j": 20, "p_n": 20, "p_output": 4, "p_x": 20, "pack": [2, 23], "packag": [0, 2, 3, 5, 6, 7, 10, 13, 15, 17, 20], "packtpub": 23, "packtpublish": 23, "pad": [5, 6], "page": [2, 17, 23], "pai": [0, 2, 3, 11, 15], "pair": [2, 4, 5, 11, 17, 20, 23], "paltform": 0, "panda": [2, 6, 7, 8, 9, 11, 13, 17], "panel": 23, "paper": 3, "paradigm": [2, 23], "parallel": [12, 15, 17, 18, 23], "param": 4, "paramat": 4, "paramet": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20], "parameter": [2, 8, 12, 23], "parametr": [2, 8, 23], "paramt": [5, 7], "part": [2, 3, 5, 7, 8, 12, 18, 19, 20, 21, 23], "partial": [1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "particip": [0, 17, 19, 21, 23], "particl": [2, 6, 15, 20, 23], "particular": [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 20, 22, 23], "particularli": [7, 8, 10, 13, 15, 20], "partit": [3, 6, 11], "partli": [8, 23], "partner": 0, "pass": [4, 5, 14, 16], "past": [12, 20], "patch": [8, 20], "path": [2, 6, 8, 9, 11, 17, 23], "patient": 9, "patter": 6, "pattern": [2, 5, 6, 14, 22, 23], "pauli": [2, 23], "pc": [13, 17], "pca": [2, 9, 17, 23], "pd": [2, 6, 7, 8, 9, 11, 13, 23], "pde": 4, "pdf": [0, 1, 2, 5, 6, 7, 8, 11, 22, 23], "pedagog": [2, 23], "penal": 8, "penalti": [8, 15], "penros": [7, 8], "pentagon": 15, "peopl": [2, 3, 11, 15, 17], "per": [2, 3, 8, 19, 21, 23], "percentag": [2, 12, 13, 21], "perceptron": [2, 3, 9, 23], "peregrin": 23, "perfect": [2, 3, 15, 23], "perfectli": [6, 8], "perform": [1, 2, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 20, 23], "performac": 6, "perhap": [2, 7, 15, 23], "perimet": 3, "period": [3, 6, 20], "permiss": 0, "permut": 13, "persist": 15, "person": [1, 7, 8, 9, 19, 21, 23], "perspect": 22, "pertin": [14, 23], "petal": [10, 11], "peter": 22, "phantom": 20, "phase": [8, 14], "phenomena": 20, "phi": 10, "phi_k": 10, "philosophi": 15, "phone": [21, 23], "photo": [6, 23], "phrase": [2, 23], "physic": [2, 3, 6, 9, 14, 15, 20, 21, 22, 23], "pi": [4, 5, 7, 8, 9, 11, 14, 15, 20], "pick": [3, 11, 12, 13, 15, 16], "pickl": 3, "pictur": [2, 23], "pie": [17, 23], "piec": [13, 16], "pillow": [2, 17, 23], "pinv": [7, 8, 15], "pip": [0, 2, 3, 17, 23], "pip3": [2, 3, 23], "pipelin": [2, 8, 10, 12], "pippin": 23, "pit": 6, "pitfal": 8, "pitt": 14, "pixel": [3, 5, 6, 23], "pixel_height": [3, 5], "pixel_width": [3, 5], "place": [0, 2, 6, 8, 10, 15, 18, 23], "plai": [2, 5, 6, 7, 8, 10, 13, 17, 23], "plain": [10, 12, 14, 15, 16], "plan": [8, 11, 21, 22, 23], "plane": [10, 11], "plateau": 7, "platform": [17, 23], "plausibl": 14, "pleas": [15, 21, 23], "plenti": 3, "plethora": [5, 14], "plot": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "plot_confusion_matrix": [9, 12], "plot_count": 8, "plot_cumulative_gain": [9, 12], "plot_data": 3, "plot_dataset": 10, "plot_decision_boundari": [11, 12], "plot_import": 12, "plot_max": 6, "plot_min": 6, "plot_model": 6, "plot_numb": 6, "plot_predict": 10, "plot_regression_predict": 11, "plot_result": 6, "plot_roc": [9, 12], "plot_surfac": [4, 8, 15], "plot_train": 11, "plot_tre": [11, 12], "plt": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "plu": [2, 5, 7, 9, 23], "pm": 10, "pmatrix": 4, "pml": 22, "pn": 5, "png": [2, 6, 8, 9, 11, 23], "point": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "point_1": 6, "point_2": 6, "poisson": [17, 20, 23], "poli": [8, 10], "poly100_kernel_svm_clf": 10, "poly3": 2, "poly3_plot": 2, "poly_featur": [0, 10, 11], "poly_features10": 11, "poly_fit": 11, "poly_fit10": 11, "poly_kernel_svm_clf": 10, "poly_model": 0, "poly_ms": 0, "poly_predict": 0, "polydegre": [2, 7, 8, 12], "polygon": 15, "polym": 14, "polynomi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 23], "polynomial_featur": [0, 1, 8], "polynomial_svm_clf": 10, "polynomialfeatur": [0, 1, 2, 8, 10, 11], "polytrop": [2, 8], "pool": 5, "pool_siz": 5, "poor": [3, 15], "poorli": 2, "popul": [2, 7, 23], "popular": [0, 2, 3, 5, 8, 9, 10, 11, 13, 14, 17, 18, 20], "popularli": [2, 23], "portabl": 12, "portion": [13, 15], "pose": [2, 6, 7, 8, 13, 20, 23], "posit": [2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 18, 20, 23], "possibl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 23], "possibli": [8, 10, 15], "posterior": 7, "postpon": 2, "postul": 7, "potenti": [2, 5, 7, 8, 14, 15], "pott": 14, "power": [2, 3, 7, 8, 10, 11, 14, 15, 23], "pp": [7, 8], "practic": [1, 2, 7, 8, 9, 10, 20], "practition": [2, 3, 5, 23], "pre": 23, "preced": [3, 13, 14, 20], "preceed": 6, "preceq": 10, "precis": [2, 4, 7, 13, 15, 18, 20, 23], "pred": 8, "predicit": 2, "predict": [0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 17, 22, 23], "predict_prob": 3, "predict_proba": [9, 12], "predictor": [2, 7, 8, 9, 11, 12, 13, 23], "prefer": [2, 3, 8, 10, 11, 13, 15, 17, 23], "prepar": [2, 8, 18, 23], "preprocess": [0, 1, 2, 6, 8, 9, 10, 11, 12, 13], "prerequisit": 2, "presenc": 15, "present": [2, 7, 8, 9, 11, 14, 15, 18, 20, 23], "preserv": [5, 13, 18], "press": [0, 15, 22], "pretrain": [3, 6], "pretti": [2, 6, 10, 11, 17, 23], "prev_centroid": 16, "prevent": [15, 20], "previou": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20], "previous": [4, 5, 11, 12, 20], "price": [2, 6, 11, 15], "primal": 10, "primari": [2, 9, 23], "prime": 20, "princip": [2, 7, 9, 17, 23], "principl": [2, 8, 9, 10, 16, 23], "print": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "print_funct": [10, 11], "printout": [2, 23], "prior": [2, 7, 8, 23], "privat": 2, "prob": [3, 20], "probabilist": [2, 22, 23], "probabl": [2, 3, 5, 6, 8, 9, 12, 15, 17, 23], "problem": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20], "probml": 22, "proce": [2, 7, 8, 9, 10, 11, 12, 13, 15, 18, 23], "procedur": [4, 6, 7, 8, 10, 12, 13, 15], "proceed": 18, "process": [2, 4, 6, 8, 11, 12, 14, 15, 17, 18, 20, 22, 23], "prod": 22, "prod_": [3, 7, 9], "produc": [2, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 23], "product": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 18, 23], "profess": [2, 23], "program": [0, 2, 3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 20, 21, 23], "programm": 18, "progress": [3, 6, 16], "prohibit": 8, "project": [0, 2, 3, 4, 5, 7, 13, 15, 17, 19], "project_root_dir": [2, 8, 9, 11, 23], "promin": 14, "promis": 10, "promot": [21, 23], "prone": [0, 11], "pronounc": [15, 17, 23], "proof": [2, 13, 14, 15, 23], "propag": [4, 5, 15], "proper": [2, 4, 8, 9], "properli": [3, 8, 10, 12, 15], "properti": [1, 2, 3, 5, 14, 15, 18, 23], "proport": [2, 3, 7, 11, 13, 15, 20, 23], "propos": [3, 6, 8, 12, 23], "propto": [7, 15], "proton": [2, 23], "prove": [5, 15], "provid": [2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 17, 18, 20, 23], "proxi": [3, 15], "prune": 11, "pseudo": [18, 20], "pseudoinv": 7, "pseudoinvers": [7, 8], "pseudorandom": [8, 20], "psychologi": [2, 23], "pt": 15, "public": [0, 2, 17, 23], "pull": 0, "punish": [2, 3, 23], "pure": [5, 11, 20], "purest": 11, "puriti": 11, "purpos": [2, 5, 12, 14, 16, 23], "push": 0, "put": 3, "py": [7, 23], "pycod": 23, "pydata": 17, "pydot": 11, "pyhton2": 23, "pylab": [2, 9, 23], "pypi": 17, "pyplot": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "pythagora": 7, "python": [3, 4, 5, 7, 8, 10, 13, 14, 15, 16, 20], "python2": 2, "python3": [2, 17, 23], "pytorch": [2, 17, 23], "q": [7, 8, 10, 13, 20], "qp": 10, "qquad": [4, 13, 15, 18], "qr": [7, 8, 18], "quad": [3, 15, 18], "quadrat": [2, 10, 11, 15, 23], "qualit": [6, 11, 20], "qualiti": [2, 11, 17, 23], "quantifi": 3, "quantil": 12, "quantit": [2, 8, 11, 23], "quantiti": [1, 2, 4, 7, 8, 9, 11, 12, 13, 14, 16, 18, 20, 23], "quantum": [6, 14, 22, 23], "quartil": 2, "quench": 7, "queri": 11, "question": [2, 7, 8, 11, 13, 14, 15, 21, 23], "qugan": 6, "quick": [6, 20], "quickli": [3, 5, 11, 13, 15], "quit": [0, 3, 7, 8, 11, 12, 14], "quot": 6, "r": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "r2": [2, 7, 8, 23], "r2_score": [2, 23], "r2score": [2, 23], "r_1": 11, "r_2": 11, "r_j": 11, "r_m": 11, "rad": 2, "radial": [2, 10, 14], "radioact": 20, "radiu": [2, 3], "rain": 11, "ramp": 3, "ran0": 20, "ran1": 20, "ran2": 20, "ran3": 20, "rand": [0, 2, 6, 7, 8, 11, 12, 15, 18, 23], "randint": [8, 11, 15], "randn": [0, 2, 3, 4, 7, 8, 11, 13, 15, 23], "random": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 17, 18, 23], "random_forest_model": 12, "random_index": 15, "random_indic": [3, 5], "random_st": [2, 9, 10, 11, 12, 13], "randomforestclassifi": 12, "randomli": [3, 8, 11, 15, 16], "rang": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "rangl": [2, 8, 13, 20, 23], "rangle_x": 20, "rank": 7, "rankdir": 6, "raphson": [3, 10, 15], "rapidli": 2, "rare": [3, 15], "raschka": 23, "rasckha": 23, "rate": [2, 3, 4, 5, 6, 10, 11, 12, 14, 15], "rather": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "ratio": [6, 9, 11, 12, 13], "rational": [2, 23], "ravel": [7, 8, 9, 10, 11, 12, 13, 15, 18], "raw": 5, "rbf": [10, 13, 14], "rbf_kernel_svm_clf": 10, "rbf_pca": 13, "rc": [2, 20], "rcond": [2, 23], "rcparam": [2, 3, 5, 9, 10, 11, 12, 20, 23], "re": [0, 4, 6, 15], "reach": [3, 6, 7, 8, 11, 12, 14, 15, 16, 23], "read": [1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 18, 20, 22], "read_csv": [2, 8, 9, 11], "read_fwf": [2, 23], "reader": [2, 8, 18, 20, 23], "readi": [2, 3, 7, 8, 10, 12, 13, 14, 18, 23], "readili": 3, "readm": 0, "readthedoc": 17, "real": [1, 2, 3, 6, 9, 12, 13, 14, 18], "real_loss": 6, "real_output": 6, "realist": [10, 23], "realiti": 20, "realiz": [3, 14], "realli": [2, 3, 23], "rearrang": 15, "reason": [2, 3, 5, 6, 12, 15, 22, 23], "reassign": 3, "recal": [7, 8, 11, 12, 13, 14, 18, 20, 23], "recast": 5, "receiv": [3, 5, 12, 14, 20], "recent": [2, 8, 15, 22], "recept": [5, 14], "receptive_field": 5, "recip": [2, 8, 9, 18, 23], "reciproc": 7, "recogn": [2, 6, 7, 12, 23], "recognit": [2, 3, 5, 14, 22, 23], "recommen": 23, "recommend": [0, 2, 4, 5, 6, 7, 8, 10, 15, 17, 18, 22], "reconsid": 11, "reconstruct": 13, "record": [12, 19, 21, 23], "recreat": 0, "rectangl": [11, 15], "rectangular": 7, "rectifi": [3, 5, 14], "recur": [2, 17, 23], "recurr": [2, 3, 17, 23], "recurs": [11, 17, 18, 23], "red": [2, 5, 6, 8, 10, 11], "redefin": [2, 12, 23], "reduc": [3, 5, 7, 8, 11, 12, 13, 15, 23], "reduct": [2, 12, 13, 17, 20, 23], "refer": [2, 3, 4, 5, 7, 8, 13, 14, 15, 16, 18, 22, 23], "referenc": 4, "refin": 14, "refit": 8, "reflect": [2, 3, 6, 7, 20, 23], "refresh": [17, 23], "refreshprogrammingskil": 23, "reg": [12, 13], "regard": [3, 11, 15], "regardless": [1, 14], "region": [5, 6, 8, 11, 14], "regist": [8, 20], "reglasso": 7, "regr_1": [2, 11], "regr_2": [2, 11], "regr_3": [2, 11], "regress": [1, 3, 10, 13, 14, 17, 18], "regressor": [2, 9, 12], "regridg": [2, 7, 8], "regular": [2, 5, 6, 7, 8, 9, 11, 15, 21, 23], "regularli": 0, "reilli": [2, 22, 23], "reinforc": [2, 10, 17, 23], "reiter": 3, "reject": 9, "rel": [2, 6, 8, 9, 11, 14, 15, 20, 23], "relat": [2, 3, 5, 6, 7, 13, 15, 16, 18, 20, 23], "relationship": [2, 6, 11, 23], "relativeerror": [2, 23], "releas": [3, 17, 23], "relev": [2, 3, 7, 9, 13, 17, 20, 23], "reli": [2, 8, 10], "reliabl": [9, 20], "relu": [5, 6, 23], "remain": [3, 4, 6, 8, 14, 18, 20], "remaind": 20, "reman": 4, "remark": 3, "rememb": [2, 10, 15, 18, 23], "remind": [2, 7, 13, 15, 18, 20], "remot": 0, "remov": [2, 6, 7, 8], "renam": 0, "render": [2, 23], "reorder": [7, 9], "reorgan": [2, 23], "repeat": [2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 16, 18, 20, 23], "repeated": 23, "repeatedli": [2, 8, 12, 15], "repet": 5, "repetit": [8, 23], "rephras": 15, "replac": [2, 3, 5, 6, 7, 8, 12, 14, 16, 17, 23], "replica": 8, "repo": 0, "report": 23, "repositori": [2, 6, 23], "repres": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23], "represent": [2, 3, 5, 8, 20, 23], "representd": 5, "reproduc": [0, 1, 2, 7, 8, 11, 14, 17, 20, 23], "repuls": [2, 23], "request": [2, 15], "requir": [0, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 18, 23], "res1": 4, "res2": 4, "res3": 4, "res_analyt": 4, "res_analytical1": 4, "res_analytical2": 4, "res_analytical3": 4, "resaml": 8, "resampl": [2, 9, 12, 17, 23], "rescal": [2, 13, 14], "rescu": 7, "reseach": 8, "research": [2, 6, 15, 17, 22, 23], "resembl": [8, 20], "reserv": [3, 7, 8, 20], "reshap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 18, 23], "residenti": 2, "residu": [2, 7, 15, 23], "resiz": 7, "resourc": 23, "respect": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 20, 23], "respond": 14, "respons": [2, 9, 11, 14, 23], "rest": [2, 7], "restat": [2, 14, 23], "restor": 6, "restored_discrimin": 6, "restored_gener": 6, "restrict": [2, 5, 11, 14, 23], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "retail": 2, "retain": [7, 8], "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 20, 23], "return_data": 16, "return_sequ": 6, "return_x_i": 11, "reus": [3, 5, 8], "reveal": [2, 14, 23], "revers": [3, 18], "review": [17, 18], "revisit": 16, "revolut": 23, "reward": [2, 6, 23], "rewrit": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "rewritten": [4, 8, 10, 12, 20], "rewrot": 15, "rf": 12, "rgb": 5, "rgoj5yh7evk": 17, "rh": 8, "rho": [2, 12, 15], "rho_1": 12, "rho_2": 12, "rho_m": 12, "rich": [2, 23], "ride": 11, "rideclass": 11, "ridedata": 11, "ridg": [9, 13, 15, 17, 23], "ridge_sk": 8, "ridgebeta": 7, "right": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "right_sid": 4, "rightarrow": [2, 3, 7, 8, 10, 13, 14, 15, 20, 23], "rigor": [2, 23], "ring": 8, "rise": [2, 23], "risk": [2, 15, 23], "rival": 6, "river": 2, "rlm": 23, "rm": [2, 20], "rmse": 2, "rmsporp": 15, "rmsprop": [3, 5, 6, 15], "rnd_clf": 12, "rng": 20, "rnn": [6, 14], "rnn1": 6, "rnn2": 6, "rnn_2layer": 6, "rnn_input": 6, "rnn_output": 6, "rnn_train": 6, "rntrick1": 20, "rntrick2": 20, "rntrick3": 20, "rntrick4": 20, "ro": [2, 15, 23], "robert": 22, "robust": [2, 23], "robustscal": 2, "roc": [9, 12], "role": [2, 4, 7, 8, 10, 17, 23], "roll": 8, "room": [2, 21, 23], "root": [0, 2, 7, 11, 15, 20], "rot": 23, "rotat": [3, 10, 11, 12], "rotation_matrix": 11, "roughli": [3, 5], "round": [2, 9, 11, 15], "routin": [15, 18, 23], "row": [1, 2, 3, 4, 7, 8, 11, 13, 18, 23], "rr": 7, "rrr": 7, "rug": 15, "rule": [2, 3, 7, 8, 15, 23], "run": [0, 2, 3, 4, 6, 7, 8, 10, 11, 13, 15, 17, 23], "runtim": [0, 3, 8, 16], "rust": [2, 17, 18, 23], "rvert": 3, "rvert_2": 3, "s_": [5, 8], "s_1": 8, "s_i": [8, 9], "s_j": 8, "s_k": 8, "saddl": 15, "sai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "said": [8, 11, 15], "sake": [2, 7, 9, 13, 23], "sale": [2, 23], "sam": 23, "same": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 23], "samm": 12, "sampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 23], "sample_vari": 16, "sampleexptvari": 20, "samwis": 23, "sastri": 13, "satisfactori": [2, 23], "satisfi": [3, 4, 5, 8, 10, 15, 18, 20], "satur": [3, 8], "save": [2, 6, 8, 9, 11, 15, 23], "save_fig": [2, 8, 9, 11, 12, 23], "savefig": [2, 6, 8, 9, 11, 20, 23], "savetxt": 6, "saw": 7, "scalabl": 12, "scalar": [4, 7, 8, 12], "scale": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 23], "scale_mean": 6, "scale_std": 6, "scaler": [2, 9, 10, 11, 12, 13], "scan": [7, 9], "scari": 7, "scatter": [0, 2, 3, 8, 9, 10, 11, 16, 23], "scenario": [8, 15], "schedul": 15, "scheme": [3, 15], "schrage": 20, "sch\u00f8yen": 8, "scienc": [2, 3, 12, 14, 15, 17, 19, 20, 21, 22], "scientif": [2, 17, 23], "scientist": [2, 23], "scikit": [0, 1, 5, 7, 8, 10, 11, 12, 15, 17, 18, 22], "scikit_learn": 2, "scikitlearn": 23, "scikitplot": [9, 12], "scipi": [2, 5, 7, 8, 15, 17, 18, 23], "scl": 8, "scm": 0, "score": [0, 1, 2, 3, 5, 8, 9, 11, 12, 13, 21, 23], "scores_kfold": 8, "scratch": [1, 3, 15], "sdg": 15, "seaborn": [2, 3, 5, 8, 9, 23], "seamless": [2, 17, 23], "search": [0, 2, 3, 5, 7, 11, 15, 23], "sebastian": 23, "sebastianraschka": 23, "sec": 8, "second": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 23], "secondeigvector": 13, "secondli": 14, "section": [1, 6, 13, 18, 20], "sector": 2, "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 23], "seed": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 20, 23], "seed_imag": 6, "seek": [3, 4, 10], "seem": [3, 5, 6], "seemingli": [2, 23], "seen": [2, 3, 5, 7, 12, 14, 20], "segment": 15, "seismic": 8, "seldomli": [2, 23], "select": [0, 3, 7, 8, 10, 11, 12, 13, 19, 20, 21, 22, 23], "selevet": 0, "self": [3, 7], "sell": 6, "semest": [9, 19], "semi": [10, 15], "semilogx": 8, "send": [7, 14, 15, 21, 23], "senior": [19, 21], "sens": [2, 6, 8, 10, 23], "sensibl": 5, "sensit": [2, 7, 8, 11, 15, 23], "sent": 4, "sentenc": [6, 14], "separ": [2, 3, 4, 6, 8, 10, 11, 14, 16, 17, 20, 23], "septemb": 23, "sequenc": [5, 6, 9, 11, 12, 14, 15, 17, 18, 20, 23], "sequenti": [3, 5, 6, 12, 14, 20], "seri": [2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 18, 23], "serif": [2, 9, 20, 23], "serv": [2, 3, 4, 5, 7, 9, 15, 22, 23], "session": [0, 3, 19, 21, 23], "set": [1, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 20, 21], "set_major_formatt": 8, "set_major_loc": 8, "set_tick": [3, 10], "set_ticklabel": 3, "set_titl": [2, 3, 4, 5, 9, 14, 16, 23], "set_xlabel": [2, 3, 4, 5, 9, 14, 23], "set_xlim": [9, 14], "set_xticklabel": 3, "set_ylabel": [2, 3, 4, 5, 9, 23], "set_ylim": [9, 14], "set_ytick": 9, "set_yticklabel": [3, 8], "set_zlim": 8, "seth": 6, "setminu": 8, "setosa": [10, 11], "setosa_or_versicolor": 10, "setp": 8, "setup": [3, 6, 8, 10, 17, 23], "sever": [1, 2, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "sgd": [3, 5], "sgd_clf": 10, "sgdclassifi": 10, "sgdreg": 15, "sgdregressor": 15, "sgn": 7, "shallow": 15, "shape": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "share": [0, 3, 5, 23], "shareabl": 0, "she": 9, "shift": [0, 3, 8, 14, 20], "ship": 5, "shire": 23, "short": [6, 7], "shortcom": 15, "shorten": 6, "shorter": 20, "shorthand": 23, "shortli": [18, 23], "should": [0, 2, 4, 5, 7, 8, 10, 11, 13, 14, 18, 20, 23], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "show_shap": 6, "shown": [2, 6, 7, 10, 14, 15, 18], "shrink": [5, 7, 8, 10, 13], "shrinkag": [7, 8], "shrunk": 13, "shuffl": [2, 3, 6, 8, 15], "side": [2, 4, 7, 10, 14, 15, 18, 23], "sigh": [17, 23], "sigma": [2, 3, 7, 8, 9, 12, 13, 14, 15, 18, 20, 23], "sigma0": 20, "sigma1": 20, "sigma2": 20, "sigma_": [7, 18, 23], "sigma_0": 7, "sigma_1": 7, "sigma_2": 7, "sigma_fn": [9, 14], "sigma_i": [2, 7, 23], "sigma_j": 7, "sigma_m": [8, 20], "sigma_n": [13, 20], "sigma_t": 15, "sigma_x": 20, "sigmoid": [3, 4, 6, 9, 10, 12, 14], "sigmundson": 8, "sign": [3, 4, 9, 10, 12, 20, 21], "signal": [3, 5, 12, 14], "signifi": 6, "signific": 3, "significantli": [3, 15, 20], "sim": [6, 7, 8, 15, 20], "similar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 23], "similarli": [2, 3, 5, 7, 10, 12, 15, 20, 23], "simpl": [1, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20], "simplepredict": 12, "simpler": [1, 2, 3, 7, 8, 9, 15, 17, 23], "simplernn": 6, "simplest": [2, 3, 5, 6, 11, 12, 14, 16, 23], "simpletre": 12, "simpli": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 23], "simplic": [4, 7, 8, 9, 10, 11, 12, 13, 14, 16], "simplicti": 7, "simplifi": [2, 8, 11, 17, 23], "simplist": [5, 8, 20], "simul": 8, "simultan": 8, "sin": [2, 3, 4, 5, 6, 11, 14, 15, 18, 23], "sinc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 22, 23], "sine": [5, 14], "singl": [2, 3, 4, 5, 7, 8, 9, 10, 11, 14, 15, 18, 20, 23], "singular": [2, 8, 15, 18, 23], "sinusoid": 5, "site": [2, 19, 23], "situat": [2, 6, 7, 9, 15, 20, 23], "six": [5, 20], "size": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 18, 20, 23], "sketch": 12, "ski": 11, "skill": 2, "skip": 13, "skl": [2, 8, 23], "sklearn": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 23], "skplt": [9, 12], "sl": 8, "slack": 10, "slice": [4, 18, 23], "slide": [1, 2, 5, 20, 23], "slight": [8, 15], "slightli": [3, 4, 5, 7, 8, 9, 12, 20], "slope": [10, 13, 14], "slow": [2, 4, 10, 15], "slower": [7, 18, 23], "slowest": 18, "slowli": 14, "slp": 3, "small": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "smaller": [2, 3, 4, 7, 8, 10, 11, 13, 15, 20, 23], "smallest": [2, 6, 16, 23], "smallest_row_index": 16, "smooth": [2, 5, 8, 15, 23], "sn": [2, 3, 5, 8, 9, 23], "sne": 13, "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "soar": 8, "social": 2, "soft": [3, 9, 12, 14], "soften": 10, "softmax": [5, 9], "softwar": [2, 10, 17, 18], "sol": 10, "sole": [2, 8, 23], "solid": [2, 9], "solut": [2, 3, 4, 5, 7, 8, 10, 12, 13, 15, 18, 20, 23], "soluton": 4, "solv": [1, 2, 3, 5, 7, 8, 10, 12, 13, 14, 15, 18, 23], "solve_expdec": 4, "solve_ode_deep_neural_network": 4, "solve_ode_neural_network": 4, "solve_pde_deep_neural_network": 4, "solveod": 4, "solveode_popul": 4, "solver": [4, 9, 10, 11, 12, 18, 23], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "some_model": 8, "somehow": 6, "someon": 1, "someth": [0, 2, 3, 5, 6, 9, 11, 13, 20, 23], "sometim": [2, 3, 13, 14, 15, 16], "soon": [18, 21], "sophist": [2, 23], "sopt": 15, "sort": [7, 8, 11, 13, 20], "sound": [5, 7], "sourc": [2, 3, 5, 8, 17, 18, 20, 23], "space": [2, 3, 6, 7, 10, 11, 13, 14, 15, 16, 20], "span": [2, 5, 7, 11, 13, 18, 23], "spare": 3, "spars": [5, 8, 18, 23], "sparse_mtx": [18, 23], "sparsecategoricalcrossentropi": 5, "sparsiti": 12, "spatial": [3, 4, 5, 14], "speak": 20, "special": [8, 9, 12, 14, 15, 18, 20, 23], "specif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20, 22, 23], "specifi": [2, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "specifici": [2, 12, 23], "spectacular": 5, "spectral": 3, "speech": [2, 3, 5, 6, 14], "speed": [3, 4, 6, 15], "spend": [1, 20], "sphere": 2, "spin": 8, "spite": 2, "spline": 10, "split": [1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 20, 23], "splite": 2, "splitter": [3, 12], "spontan": 20, "spot": 5, "spread": [2, 13, 20, 23], "springer": [22, 23], "spuriou": 15, "sqrsignal": 5, "sqrt": [2, 5, 6, 7, 8, 10, 12, 13, 15, 20], "squar": [0, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 17, 18, 20], "squarederror": 12, "squaredeuclidean": 16, "squash": 14, "srtm": 8, "srtm_data_norway_1": 8, "stabil": 7, "stabl": [1, 2, 6, 7, 8, 11, 17, 23], "stack": [5, 6], "stage": [0, 7, 15], "stai": [2, 4, 6, 7, 13, 23], "stand": [2, 7, 11, 14, 23], "standard": [2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 20, 23], "standardscal": [2, 8, 9, 10, 11, 12, 13], "stanford": 15, "start": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "start_tim": 16, "stat": 8, "state": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "statement": [2, 9, 18, 23], "statist": [2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 16, 18, 22], "statu": [0, 2, 9, 23], "stavang": 8, "std": [2, 6, 8, 23], "steep": 15, "step": [0, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 16, 18, 23], "step_fn": [9, 14], "step_length": 15, "steps_list": 11, "stereo": 5, "still": [2, 4, 5, 7, 8, 13, 15, 20], "stimuli": 14, "stk": [22, 23], "stk2100": [22, 23], "stk3155": [0, 19, 21], "stk4021": [22, 23], "stk4051": [22, 23], "stk4155": [19, 21], "stk5000": 22, "stochast": [2, 3, 7, 8, 10, 13, 14, 23], "stock": 6, "stoke": 14, "stone": [2, 9], "stop": [3, 6, 11, 15, 16], "storag": 7, "store": [2, 3, 4, 5, 8, 13, 15, 20, 23], "storehaug": [21, 23], "str": [3, 5, 6], "straight": [2, 8, 10, 15, 23], "straightforward": [2, 4, 5, 7, 8, 10, 11, 12, 15, 18, 23], "strategi": [2, 3, 11, 23], "stratifi": 8, "strength": [2, 7, 16], "stretch": 13, "strict": [10, 15], "strictli": [10, 15], "stride": [6, 18], "strike": 8, "string": 3, "stroke": 9, "strong": [5, 8, 11, 12, 14, 18, 20], "strongli": [0, 2, 10, 17, 18], "stronli": 2, "structur": [2, 3, 4, 5, 8, 11, 12, 14, 17, 23], "stuck": [3, 15], "student": [0, 2, 19, 21, 22, 23], "studi": [2, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 22, 23], "studier": 22, "style": [2, 9, 11, 18, 23], "st\u00f8land": 21, "sub": [11, 14], "subdivid": [2, 18, 23], "subfield": 2, "subject": [8, 10, 20], "submit": 23, "subplot": [2, 3, 5, 6, 8, 9, 10, 11, 12, 16, 23], "subplots_adjust": [10, 20], "subprogram": [18, 23], "subract": 2, "subroutin": [2, 23], "subscript": 3, "subsequ": [3, 6, 7, 8, 14, 18, 20], "subset": [3, 8, 11, 14, 15, 17, 23], "subspac": [2, 10, 13], "substanti": [11, 12], "substep": 13, "substitut": [1, 5, 8, 14, 18], "subsubset": 11, "subtask": 8, "subtl": 3, "subtract": [2, 6, 7, 8, 13, 15, 18, 20], "subtre": 11, "succeed": [2, 6, 23], "success": [5, 9, 11, 15, 20], "successfulli": [6, 11], "sudo": [2, 17, 23], "suffer": [2, 3, 4, 7, 12, 23], "suffici": [3, 8, 10, 13, 15], "suggest": [3, 15, 22], "suit": [10, 14], "suitabl": [0, 2, 20], "sum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_i": [2, 4, 7, 8, 10, 15], "sum_j": 8, "sum_ja_": 2, "sum_k": [8, 10, 14, 18], "sum_logist": 15, "sum_m": 5, "sum_n": 5, "sum_nx_": 5, "summar": [7, 8, 11], "summari": [3, 5, 6, 12, 19], "summat": [1, 2, 5], "sunni": 11, "super": 7, "superfici": 5, "superscript": [3, 14], "supervis": [2, 7, 8, 9, 11, 14, 17, 23], "supplement": 9, "support": [2, 3, 11, 12, 13, 15, 17, 23], "suppos": [2, 7, 8, 9, 10, 12, 13, 14, 15, 18, 23], "suppress": [7, 15], "sure": [1, 2, 3, 6, 8], "surf": 8, "surfac": [2, 8, 23], "surpass": 8, "surpris": [2, 23], "surround": [5, 17], "survei": [2, 7, 8, 23], "svc": [10, 11, 12], "svd": [2, 8, 13, 23], "svdinv": 7, "svm": [10, 11, 12, 13], "svm_clf": [10, 12], "swath": 7, "switch": 2, "sy": 15, "symbol": [3, 7, 13, 15, 17, 20, 23], "symmeteri": 3, "symmetr": [2, 7, 10, 13, 14, 15, 18, 23], "symmetri": 8, "sympi": [2, 17, 23], "synonim": 20, "syntax": 15, "system": [0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 23], "systemat": [6, 8], "t": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "t0": [5, 8, 15], "t1": [4, 15], "t2": 4, "t3": 4, "t_": 4, "t_0": [4, 11, 15], "t_1": 15, "t_b": 12, "t_i": [3, 4, 7, 14], "t_j": 14, "t_k": 11, "tabl": [11, 20, 21, 23], "tabul": [2, 23], "tabular": 23, "tackl": 6, "tag": [4, 5, 6, 7, 8, 9, 14, 15, 16, 18, 20], "taht": [2, 23], "tail": 20, "tailor": [4, 10, 13, 23], "taiwan": [2, 23], "take": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "taken": [2, 3, 5, 8, 12, 15, 18], "tan": 5, "tangent": [3, 6, 14, 15], "tanh": [3, 6, 9, 10, 14], "target": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 23], "target_nam": 11, "task": [2, 3, 5, 8, 11, 13, 14, 16, 23], "tau": [5, 7, 20], "taught": 23, "tax": 2, "taylor": [4, 15], "taylornr": 15, "tc": 10, "teach": [0, 19, 23], "team": 3, "teaser": 2, "technic": [2, 7, 8, 15], "techniqu": [2, 3, 10, 12, 15, 17, 20, 22, 23], "technologi": [2, 3], "tell": [1, 2, 6, 8, 12, 13, 15, 20], "temp": 3, "temp1": 3, "temp2": 3, "temperatur": [2, 11, 23], "temporarili": 3, "ten": [5, 23], "tend": [5, 7, 8, 10, 11, 12, 14, 15, 16], "tendenc": [2, 23], "tension": 8, "tensor": 5, "tensorflow": [2, 4, 6, 10, 16, 17, 18, 22, 23], "term": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "term1": [7, 8, 13], "term2": [7, 8, 13], "term3": [7, 8, 13], "term4": [7, 8, 13], "termin": [0, 2, 6, 7, 11, 12, 15], "terrain": 8, "terrain1": 8, "test": [1, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 20, 23], "test_acc": 5, "test_accuraci": [3, 5], "test_error": 8, "test_imag": [5, 6], "test_ind": 8, "test_input": 6, "test_label": [5, 6], "test_loss": 5, "test_pr": 3, "test_predict": 3, "test_rnn": 6, "test_scor": [9, 12], "test_siz": [0, 2, 3, 5, 7, 8, 12], "test_split": 11, "testerror": [2, 8], "testi": 6, "testpredict": 6, "testx": 6, "text": [2, 3, 4, 6, 7, 10, 11, 13, 15, 18, 20, 22], "textbook": 1, "textual": 11, "textur": 3, "tf": [3, 5, 6, 15, 16], "th": [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 20, 23], "than": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20, 23], "thank": [6, 8], "theano": [3, 17, 23], "thei": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "them": [2, 3, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18, 23], "theme": [0, 2, 23], "themselv": [2, 20, 23], "thenc": 8, "theorem": [4, 8, 9], "theoret": [2, 6, 12], "theori": [2, 3, 5, 10, 11, 14, 15, 17, 22, 23], "thereaft": [2, 7, 8, 13, 14, 18, 23], "therebi": [2, 7, 9, 13, 23], "therefor": [2, 3, 4, 5, 6, 8, 9, 10, 13, 15, 20, 23], "therein": 13, "thereof": [2, 8, 15, 23], "theta": [3, 6, 15, 20, 23], "theta_": [3, 15, 23], "theta_0": 23, "theta_0x_": 23, "theta_1": 23, "theta_1x_": 23, "theta_1x_0": 23, "theta_1x_1": 23, "theta_1x_2": 23, "theta_2": 23, "theta_2x_": 23, "theta_2x_0": 23, "theta_2x_1": 23, "theta_2x_2": 23, "theta_i": [3, 23], "theta_j": 23, "theta_linreg": 15, "theta_t": 15, "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22], "thing": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 23], "think": [2, 3, 5, 6, 8, 11, 14, 15, 16, 20, 23], "third": [2, 5, 8, 15, 21, 23], "thirti": 9, "thorughout": 23, "those": [2, 5, 7, 8, 10, 11, 12, 13, 18, 23], "though": [1, 3, 4, 5, 6, 15, 18, 20], "thought": [8, 16, 20], "thousand": [2, 3], "three": [2, 3, 5, 7, 8, 10, 11, 14, 18, 19, 20, 21, 23], "threshold": [3, 5, 11, 12, 13, 14, 15], "through": [0, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 20, 23], "throughout": [0, 2, 6, 7, 16, 17, 18, 20, 23], "throw": [5, 8, 20], "thu": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 21, 23], "thumb": [2, 8], "tibshirani": [8, 22, 23], "tick_param": 8, "ticker": [8, 15, 20], "tif": 8, "tight_layout": [3, 9], "tightli": 13, "tild": [2, 7, 8, 9, 13, 20, 23], "till": [2, 6, 9, 10, 11, 12, 14, 18, 23], "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "timeit": 6, "timer": 6, "tini": 3, "tip": 5, "titl": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 20, 23], "tmp": 15, "tn": [4, 5, 9], "to_categor": [3, 5, 6], "to_categorical_numpi": 3, "to_numer": [2, 8, 23], "todai": 5, "togeth": [2, 5, 8, 10, 13, 15, 17, 23], "toi": 16, "told": 15, "toler": [4, 16], "tolist": 6, "tomographi": 14, "too": [2, 4, 6, 7, 8, 11, 13, 15, 20, 22], "took": [10, 23], "tool": [0, 2, 3, 5, 8, 15, 17], "toolbox": 10, "top": [2, 5, 7, 8, 11, 12, 17, 23], "topic": [2, 7, 8, 9, 10, 17], "topolog": [5, 14], "topologi": [3, 14], "torkjellsdatt": [21, 23], "toss": [12, 20], "total": [2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 21, 23], "total_loss": 6, "totalclustervari": 16, "totalscatt": 16, "toward": [0, 3, 4, 9, 14, 15], "town": 2, "tp": [6, 9], "tpng": 11, "tpu": [15, 17, 23], "tqdm": 8, "track": [0, 5, 15, 16, 18], "tract": 2, "tractabl": [2, 23], "trade": [7, 11], "tradeoff": [2, 7, 23], "tradit": [2, 3, 6, 8, 23], "train": [1, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15], "train_accuraci": [2, 3, 5, 23], "train_dataset": 6, "train_end": [2, 3], "train_error": 8, "train_imag": [5, 6], "train_ind": 8, "train_label": [5, 6], "train_pr": 3, "train_siz": [2, 3, 5], "train_step": 6, "train_test_split": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "train_test_split_numpi": [2, 3], "trainable_vari": 6, "trained_model": 8, "trainerror": 2, "traini": 6, "training_checkpoint": 6, "training_dataset": 6, "training_gradi": 15, "trainingerror": 8, "trainpredict": 6, "trainscor": 6, "trainx": 6, "trait": [2, 23], "trajectori": 6, "transfer": [11, 23], "transform": [2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "transit": [8, 14], "translat": [3, 6, 8, 12, 23], "transpos": [3, 7, 13, 18], "travers": [2, 7], "treat": [2, 3, 5, 8, 14, 15, 20, 23], "tree": [2, 3, 17, 23], "tree_clf": [11, 12], "tree_clf_": 11, "tree_clf_sr": 11, "tree_reg": 11, "tree_reg1": 11, "tree_reg2": 11, "trend": 20, "treue": 9, "trevor": 22, "tri": [1, 4, 5, 6, 11, 15], "triain": 2, "trial": [2, 4, 6, 8, 15, 20, 23], "triangl": 15, "triangular": 18, "trick": [5, 6, 10, 13, 15, 20], "trickier": 20, "tridiagon": 18, "trillion": 17, "trivial": [2, 3, 7, 13, 20, 23], "troubl": [0, 2, 10, 14], "truck": 5, "true": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "true_beta": 8, "true_fun": 8, "truli": 23, "try": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23], "tucker": 10, "tuesdai": [21, 23], "tumor": [9, 11], "tumour": 9, "tunabl": 3, "tune": [6, 11, 15, 18, 23], "turn": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "tutori": [3, 6], "tv": 4, "tveito": 4, "tweak": [3, 6, 12, 20], "twice": 15, "twist": 13, "two": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23], "tx": 15, "tx_1": 15, "txt": [0, 6], "ty": 15, "type": [2, 3, 5, 8, 10, 12, 15, 18, 20], "typic": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 20, 23], "u": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "u_": 18, "u_i": 14, "u_m": 12, "ua": [2, 23], "ubuntu": [2, 17, 23], "uci": 2, "uio": [0, 21, 22], "un": 16, "unabl": 0, "unari": [18, 23], "unbalanc": [8, 11], "unbias": [2, 7, 8, 23], "uncent": 8, "uncertainti": [2, 7, 23], "uncertitud": 20, "unchang": [3, 5], "uncorrel": [12, 20], "undefin": 7, "under": [2, 3, 7, 8, 12, 15, 17, 23], "underdetermin": [2, 23], "underfit": [3, 8], "underflowproblem": 7, "undergo": 7, "undergradu": [19, 21], "underli": [2, 3, 11, 15, 20, 23], "underset": [6, 16], "understand": [0, 2, 3, 5, 7, 8, 12, 15, 16, 17, 23], "understood": [10, 15], "undesir": 10, "undetermin": [7, 10], "undo": 6, "unexpect": 8, "unexpected": 20, "unfair": 8, "unfortun": [3, 10, 11, 12], "unicode_liter": [10, 11], "uniform": [2, 3, 7, 8, 13, 15, 20, 23], "uniformli": [15, 20], "unifrompdf": 20, "unimport": 15, "union": [7, 8], "uniqu": [2, 4, 8, 15, 16, 18, 23], "unique_cluster_label": 16, "unit": [2, 3, 5, 6, 7, 12, 14, 20, 23], "unitari": [7, 8, 18], "unitarili": [18, 23], "uniti": 20, "univari": 20, "univers": [2, 3, 4, 15, 17, 19, 21, 23], "unix": 3, "unknow": [2, 18, 23], "unknown": [2, 3, 5, 6, 7, 8, 10, 12, 15, 18, 23], "unknowwn": 14, "unlabel": 3, "unless": [2, 5, 8, 13, 15, 23], "unlik": [3, 5, 10, 15], "unnecessarili": 11, "unord": 5, "unravel": 3, "unrol": [5, 13], "unseen": [0, 2, 9, 11], "unstabl": 3, "unsupervis": [2, 3, 6, 14, 17, 23], "unsymmetr": [18, 23], "until": [3, 4, 6, 11, 14, 15, 16], "untouch": 2, "unusu": 14, "up": [1, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21], "updat": [0, 3, 4, 12, 14, 15, 16], "uploa": 23, "upload": [0, 17, 22], "upon": [2, 3, 8, 9, 13, 18], "upper": [1, 2, 10, 11, 18], "uppercas": [18, 23], "upsampl": 6, "upscal": 6, "url": 23, "us": [0, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 22], "usag": [2, 10, 17, 23], "usd": 2, "usd10000": 2, "use_bia": 6, "usecol": [2, 23], "useless": 3, "user": [2, 3, 4, 6, 8, 9, 17, 18, 23], "usernam": 0, "usetex": 20, "usg": 8, "usr": 20, "usual": [2, 5, 6, 9, 14, 15, 16, 23], "ut": 7, "util": [3, 5, 6, 8, 9, 12, 16, 23], "ux": 18, "v": [0, 2, 4, 6, 7, 8, 13, 15, 17], "v0": 20, "v1": 20, "v2": 20, "v_0": 13, "va": 3, "vahid": 23, "val": 15, "val_accuraci": 5, "val_loss": 6, "vale": 4, "valid": [2, 3, 6, 9, 11, 12, 15, 17, 20, 23], "validation_data": 5, "validation_split": 6, "valu": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23], "valuat": 11, "valy": 6, "van": [2, 23], "vandenbergh": [10, 15], "vandermond": [2, 23], "vanilla": [2, 8, 13, 16], "vanish": [3, 6, 15, 20], "var": [7, 8, 12, 13, 20], "var_x": 20, "varabl": 10, "varepsilon": [7, 8], "varepsilon_": [7, 8], "varepsilon_i": [7, 8], "vari": [2, 3, 5, 7, 8, 12, 23], "variabl": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 23], "varianc": [2, 3, 7, 9, 11, 12, 13, 15, 16, 17, 18, 20, 23], "variance_i": [7, 13], "variance_x": [7, 13], "variant": [2, 3, 8, 10, 14, 15, 23], "variat": [5, 6, 13, 23], "varieti": [2, 5, 14, 17, 23], "variou": [1, 3, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "varydimens": 6, "vastli": 5, "vaue": 3, "vault": 2, "vdot": [4, 15], "vec": 8, "vector": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17], "vector_mean": 16, "ventur": [2, 10, 17, 23], "venv": 0, "verbos": [3, 5, 6], "veri": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 22, 23], "verifi": [5, 13, 18, 23], "versatil": [10, 23], "versicolor": [10, 11], "version": [0, 2, 5, 12, 15, 16, 17, 18, 20, 23], "versu": 3, "vert": [1, 2, 3, 7, 8, 9, 10, 11, 13, 15, 23], "vert_1": [7, 8], "vert_2": [7, 8, 13], "via": [2, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 23], "vidal": 13, "video": [2, 3, 14, 17, 19, 21, 23], "view": [3, 5, 7, 8, 14, 15, 20, 22, 23], "violat": 10, "virginica": 11, "viridi": [2, 3, 4, 5, 23], "virtual": 3, "viscos": 15, "viscou": 15, "visibl": 0, "vision": [2, 5], "visual": [2, 5, 13, 14, 17, 23], "visualis": 3, "visualstudio": [0, 1], "viz": [8, 10, 20], "vmap": 15, "vmax": [3, 8], "vmin": [3, 8], "voic": 5, "volum": [2, 5, 23], "vote": [12, 23], "voting_clf": 12, "votingclassifi": 12, "votingsimpl": 12, "vstack": [7, 13, 18, 20, 23], "vt": 7, "w": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "w1": 10, "w2": [10, 13], "w3": 10, "w_": [3, 14], "w_1": [10, 18], "w_1x_": 10, "w_1x_1": 10, "w_2": [10, 18], "w_2x_": 10, "w_2x_2": 10, "w_3": 18, "w_4": 18, "w_hidden": 4, "w_i": [3, 4, 12], "w_ix_i": 14, "w_j": 18, "w_m": 18, "w_output": 4, "w_px_": 10, "w_px_p": 10, "wa": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 16, 18, 23], "wai": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "walk": 11, "walker": 20, "wang": [2, 23], "want": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "warn": [6, 23], "warrant": 8, "wast": 5, "watch": 17, "wave": 5, "wavelet": 10, "we": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "weak": [11, 12, 16], "weather": [3, 14], "web": [17, 19, 21, 23], "webpag": 23, "websit": [8, 18, 19, 23], "wedg": [10, 20], "wednesdai": [21, 23], "wee": 13, "week": [2, 7, 8, 9, 19, 21], "weekli": [0, 1, 17, 19, 21, 22, 23], "weekss": 0, "weight": [2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 20], "weigth": 4, "welcom": [0, 10, 17], "well": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 22, 23], "went": 10, "were": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 20, 23], "wessel": [2, 23], "what": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "whatev": 5, "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "whenev": [0, 15, 20], "where": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "wherea": [8, 20], "wherein": [3, 14], "whether": [2, 5, 7, 9, 11, 20, 23], "which": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "whichev": [3, 5], "while": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20, 23], "white": 11, "who": [0, 2], "whole": [3, 5, 6, 7, 11, 13, 15], "whose": [2, 8, 12, 20], "whow": 13, "why": [0, 1, 2, 3, 5, 8, 15], "wide": [2, 3, 5, 8, 9, 14, 17, 18, 23], "widehat": 8, "width": [2, 5, 10, 11, 23], "wieringen": [2, 23], "win": 12, "wind": 11, "wing": [21, 23], "winther": 4, "wiothout": 8, "wiscons": 9, "wisconsin": 12, "wisdom": 8, "wise": [2, 3, 7, 14, 15], "wish": [2, 4, 7, 9, 10, 13, 15, 16, 18, 23], "with_std": 2, "wither": 8, "within": [2, 4, 5, 6, 9, 11, 14, 15, 16, 20, 22, 23], "withinclust": 16, "without": [0, 2, 3, 7, 8, 10, 11, 13, 14, 15, 23], "won": [0, 2, 23], "wonder": 10, "word": [2, 3, 5, 6, 7, 8, 9, 16, 20, 23], "work": [0, 1, 2, 3, 6, 8, 9, 10, 11, 15, 17, 19, 20, 21, 23], "workshop": 23, "world": [1, 2, 10], "worldwid": [2, 23], "worri": 0, "wors": [2, 3, 5, 6, 8, 23], "worth": 11, "would": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "wrap": [8, 18, 23], "write": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 18, 23], "written": [1, 2, 4, 5, 7, 13, 14, 15, 17, 18, 20, 23], "wrong": [0, 3, 10], "wrongli": 12, "wrote": [7, 13], "wrt": [12, 15], "wth": [12, 15], "www": [17, 18, 22, 23], "wx_1": 10, "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x0": 10, "x1": [6, 10, 11, 12, 15], "x1_exampl": 10, "x1d": 10, "x2": [10, 11, 12, 15], "x2d": [10, 13], "x2d_train": 13, "x2dsl": 13, "x3": 10, "x_": [2, 4, 5, 7, 8, 10, 12, 13, 15, 16, 18, 20, 23], "x_0": [2, 7, 13, 18, 23], "x_1": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_2": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_3": [10, 18, 20], "x_4": 18, "x_center": 13, "x_data": 3, "x_data_ful": 3, "x_hidden": 4, "x_i": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x_input": 4, "x_ix_": [2, 23], "x_iy_i": 10, "x_j": [1, 2, 4, 10, 11, 14, 20], "x_jy_j": 10, "x_k": [14, 16, 18, 20], "x_l": 20, "x_m": [8, 14, 18, 20], "x_n": [2, 4, 5, 8, 10, 13, 14, 15, 18, 20, 23], "x_new": [11, 12], "x_offset": 8, "x_output": 4, "x_p": [5, 9, 11], "x_poli": 11, "x_poly10": 11, "x_pred": 6, "x_prev": 4, "x_reduc": 13, "x_scale": 10, "x_small": 15, "x_test": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13], "x_test_own": 8, "x_test_scal": [2, 8, 9, 11, 12, 13], "x_tot": 6, "x_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "x_train_mean": 8, "x_train_own": 8, "x_train_scal": [2, 8, 9, 11, 12, 13], "x_val": 3, "xarrai": [17, 23], "xavier": 3, "xbnew": 15, "xcode": [2, 17, 23], "xdclassiffierconfus": 12, "xdclassiffierroc": 12, "xg_clf": 12, "xgb": 12, "xgbclassifi": 12, "xgboost": 11, "xgboot": 12, "xgbregressor": 12, "xgparam": 12, "xgtree": 12, "xi": [10, 15], "xi_": 10, "xi_1": 10, "xi_i": 10, "xk": 10, "xla": [15, 17, 23], "xlabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "xlim": [8, 12], "xm": 11, "xmesh": 15, "xnew": [2, 15, 23], "xp": 20, "xpanda": 2, "xpd": [7, 13], "xplot": 2, "xscale": 2, "xsr": 11, "xt_x": 15, "xtest": 8, "xtick": [5, 8, 10, 11], "xtrain": 8, "xu": [2, 23], "xx": [2, 18, 23], "xy": [2, 8, 10, 18, 23], "xytext": 10, "xz": [18, 23], "y": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "y1": 6, "y2": 6, "y3": 6, "y_": [2, 3, 7, 8, 12, 13, 18, 23], "y_0": [2, 7, 13, 18, 23], "y_1": [2, 7, 10, 11, 13, 15, 18, 23], "y_1y_1": 10, "y_1y_1k": 10, "y_1y_2": 10, "y_1y_2k": 10, "y_1y_n": 10, "y_1y_nk": 10, "y_2": [2, 7, 10, 11, 13, 18, 23], "y_2y_1": 10, "y_2y_1k": 10, "y_2y_2": 10, "y_2y_2k": 10, "y_3": [2, 11, 18], "y_4": 18, "y_data": [2, 3, 7, 8, 23], "y_data_ful": 3, "y_decis": 10, "y_fit": 2, "y_i": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "y_if_": 12, "y_ix_": [2, 23], "y_ix_i": [9, 10, 15], "y_iy_jk": 10, "y_j": [8, 10, 14], "y_k": 14, "y_m": 18, "y_model": [2, 6, 7, 8, 23], "y_n": [10, 15], "y_ny_1": 10, "y_ny_1k": 10, "y_ny_2": 10, "y_ny_2k": 10, "y_ny_n": 10, "y_ny_nk": 10, "y_offset": 8, "y_plot": 11, "y_pred": [2, 3, 6, 8, 9, 10, 11, 12], "y_pred1": 11, "y_pred2": 11, "y_pred_rf": 12, "y_pred_tre": 12, "y_proba": [9, 12], "y_scaler": 8, "y_test": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13], "y_test_onehot": 3, "y_test_predict": 2, "y_tot": 6, "y_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_train_mean": 8, "y_train_onehot": 3, "y_train_predict": 2, "y_train_scal": 8, "y_val": 3, "ye": [5, 8, 9], "year": [2, 17, 23], "yet": [2, 3, 8, 10, 13, 15, 23], "yi": 15, "yield": [2, 4, 7, 8, 10, 12, 14, 15, 16, 18, 20, 23], "yk": 10, "ylabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "ylim": [5, 8], "ym": 11, "ymesh": 15, "yn": 2, "yo": [10, 11, 12], "yoshua": [3, 22], "you": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 21, 22, 23], "young": 2, "your": [0, 3, 4, 6, 7, 8, 10, 13, 15, 17, 18, 23], "your_model_object": 1, "yourself": [13, 15, 23], "youtub": 17, "ypred": 8, "ypredict": [2, 15, 23], "ypredict2": 15, "ypredictlasso": 7, "ypredictol": [2, 7], "ypredictown": 8, "ypredictownridg": 8, "ypredictridg": [2, 7, 8], "ypredictskl": 8, "ytest": 8, "ytick": [5, 8, 10, 11], "ytild": [2, 8, 23], "ytildelasso": 7, "ytildenp": [2, 23], "ytildeol": [2, 7], "ytildeownridg": 8, "ytilderidg": [7, 8], "ytrain": 8, "yuxi": 23, "yx": [18, 23], "yy": [18, 23], "yz": [18, 23], "z": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "z_": [3, 4, 14, 18, 23], "z_0": [18, 23], "z_1": [18, 23], "z_2": [18, 23], "z_c": 3, "z_h": 3, "z_hidden": 4, "z_i": [3, 14], "z_j": [3, 14], "z_k": 14, "z_m": 3, "z_mod": 11, "z_o": 3, "z_output": 4, "zaman": 20, "zaxi": 8, "zero": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "zeros_lik": 6, "zfill": 6, "zip": [6, 8], "zm_h": [2, 23], "zn": 2, "zone": 2, "zoom": 23, "zx": [18, 23], "zy": [18, 23], "zz": [18, 23], "\u00f8yvind": 8}, "titles": ["Exercises week 34", "Exercises week 35", "3. Linear Regression", "14. Building a Feed Forward Neural Network", "15. Solving Differential Equations with Deep Learning", "16. Convolutional Neural Networks", "17. Recurrent neural networks: Overarching view", "4. Ridge and Lasso Regression", "5. Resampling Methods", "6. Logistic Regression", "8. Support Vector Machines, overarching aims", "9. Decision trees, overarching aims", "10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "11. Basic ideas of the Principal Component Analysis (PCA)", "13. Neural networks", "7. Optimization, the central part of any Machine Learning algortithm", "12. Clustering and Unsupervised Learning", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Course setting", "1. Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities"], "titleterms": {"": [10, 12], "1": [0, 1, 2], "2": [0, 1, 2, 23], "2023": 21, "3": [0, 1, 2], "34": [0, 23], "35": 1, "4": [0, 1, 2], "5": [1, 2], "A": [2, 3, 6, 10, 11, 23], "And": 23, "In": 21, "Ising": 8, "The": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 17, 23], "To": 23, "With": 6, "about": 23, "activ": [3, 14], "ad": [2, 8, 23], "adaboost": 12, "adagrad": 15, "adam": 15, "adapt": 12, "adjust": 3, "adversari": 6, "again": [5, 11], "ai": 23, "aim": [10, 11, 23], "aka": 23, "algebra": [18, 23], "algorithm": [11, 12, 13, 14, 23], "algortithm": 15, "all": 10, "an": [0, 2, 6, 12, 23], "analys": 7, "analysi": [2, 7, 8, 13, 17, 20, 23], "analyt": [1, 2], "ani": 15, "anoth": 11, "appli": 17, "approach": [2, 10, 16, 23], "approxim": 14, "architectur": 3, "arrai": [18, 23], "assist": 21, "autocorrel": 20, "autograd": [4, 15], "automat": 15, "back": [3, 13, 14], "background": 17, "bag": 12, "base": 15, "basic": [2, 7, 9, 11, 12, 13, 18], "batch": 3, "bay": 7, "befor": 13, "better": 10, "bia": 8, "binari": 3, "bind": 23, "bird": 12, "boost": 12, "bootstrap": [8, 12], "boston": 2, "breast": 3, "brief": 23, "bring": 14, "build": [3, 5, 11], "c": 23, "can": 23, "cancer": [3, 9, 11, 13], "cart": 11, "case": [10, 12, 20], "central": [15, 17, 20], "chain": 14, "chang": 12, "channel": 23, "chi": [2, 23], "choos": 3, "cifar01": 5, "classic": 13, "classif": [3, 11, 12], "classifi": 10, "clip": 3, "cluster": 16, "cnn": 5, "code": [0, 1, 2, 3, 4, 7, 11, 13, 14, 15, 16, 23], "collect": [3, 5], "commun": 23, "compar": [1, 4, 12], "complex": [2, 8], "complic": 8, "compon": 13, "comput": 11, "computerlab": 23, "con": 11, "concept": 20, "conjug": 15, "contn": 23, "convex": [10, 15], "convolut": [5, 14], "correl": 13, "cost": [3, 12], "cours": [17, 19, 22, 23], "covari": [7, 13, 20], "cover": 23, "creat": 1, "cross": 8, "cython": 23, "data": [0, 2, 3, 5, 8, 9, 11, 13, 17, 20, 23], "dataset": [3, 5], "david": 23, "deadlin": 23, "deadllin": 21, "decai": 4, "decis": [11, 12], "decomposit": [7, 13, 18], "deeep": 23, "deep": [3, 4, 23], "defin": [3, 23], "degre": 2, "deliver": [0, 1], "dens": 2, "deriv": [1, 7, 14], "descent": [4, 12, 15], "detail": [5, 23], "develop": 3, "diagon": 13, "differ": 10, "differenti": [4, 15], "diffus": 4, "dimension": [4, 5, 10], "disadvantag": 11, "discret": 20, "discrimin": 23, "distribut": [7, 20], "do": 3, "domain": 20, "down": 3, "dropout": 3, "element": [2, 20, 23], "elimin": 18, "energi": 23, "ensembl": 12, "entropi": 11, "environ": [0, 2], "equat": [2, 4, 14], "error": [2, 12, 23], "essenti": 23, "etc": 23, "euler": 4, "evalu": 3, "exampl": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "exercis": [0, 1, 2, 8], "expect": 20, "experi": 20, "explor": 2, "exponenti": 4, "express": 1, "extrapol": 6, "extrem": [12, 23], "ey": 12, "fall": 21, "famili": [3, 23], "famou": 18, "featur": [1, 11, 18], "feed": [3, 14], "final": 14, "find": 1, "fine": 3, "first": [6, 14, 23], "fit": [0, 1, 2, 12, 23], "forc": 5, "forest": 12, "format": 23, "forward": [3, 4, 14], "foster": 23, "fourier": 5, "frank": 8, "freedom": 2, "frequentist": [2, 23], "from": [7, 12, 14, 23], "full": 4, "function": [2, 3, 8, 9, 10, 12, 13, 14, 15, 20, 23], "further": [5, 7], "gan": 6, "gaussian": 18, "gd": 15, "gener": [6, 11, 23], "geometr": 13, "gini": 11, "github": 0, "goal": [0, 1], "good": [2, 23], "grade": [21, 23], "gradient": [3, 4, 12, 15], "growth": 4, "ha": 17, "handl": [18, 23], "hidden": 4, "hous": 2, "how": 1, "hyperparamet": 3, "hyperplan": 10, "i": [2, 3, 23], "id3": 11, "idea": 13, "ii": 23, "implement": [1, 3], "implic": 7, "import": [7, 18, 23], "improv": 3, "includ": 15, "increment": 13, "index": 11, "inform": 21, "input": 4, "instal": [17, 23], "instructor": 21, "interpret": [7, 13, 23], "introduc": [13, 15], "introduct": [2, 8, 17, 18, 23], "invers": [7, 18], "iter": 12, "jax": 15, "julia": 23, "jungl": 12, "kera": [3, 5], "kernel": [10, 13], "lagrangian": 10, "lasso": [7, 8], "later": 7, "layer": [3, 4, 5, 14], "learn": [0, 1, 2, 3, 4, 13, 15, 16, 17, 23], "least": [1, 7, 8, 23], "lectur": 23, "level": 12, "librari": [17, 23], "likelihood": 9, "limit": [3, 15, 20], "linear": [0, 2, 10, 15, 18, 23], "link": [7, 13, 22], "logist": [9, 23], "lu": 18, "machin": [2, 10, 15, 17, 23], "main": [20, 23], "make": [2, 11, 12], "mani": [12, 14], "mass": 23, "materi": 23, "math": 7, "mathemat": [5, 7, 10], "matric": [7, 18, 23], "matrix": [1, 3, 7, 13, 14, 18, 23], "matter": 2, "mean": 2, "meet": [7, 12, 20, 23], "mercer": 10, "method": [8, 11, 12, 15, 23], "minim": 23, "ml": 23, "mlp": 14, "mnist": [5, 6], "model": [0, 2, 3, 6, 8, 14, 23], "momentum": 15, "moon": [10, 11], "more": [5, 8, 18, 23], "multilay": 14, "multipl": [3, 5], "multipli": 10, "need": 23, "network": [3, 4, 5, 6, 9, 14, 23], "neural": [3, 4, 5, 6, 9, 14, 23], "new": 6, "non": 10, "normal": [2, 3], "notat": 14, "now": [3, 11, 15], "nuclear": [2, 23], "numba": 23, "number": [2, 4, 20], "numer": [4, 20], "numpi": [18, 23], "object": 5, "obtain": 13, "od": 4, "off": 8, "ol": [0, 1, 7, 8], "one": [4, 14], "oper": 18, "optim": [3, 10, 15, 17, 23], "order": 15, "ordinari": [1, 7, 8, 23], "organ": [2, 23], "oslo": 22, "other": [6, 11, 13, 14, 18, 23], "our": [2, 6, 7, 13, 15, 23], "outcom": [17, 23], "output": 4, "overarch": [2, 6, 10, 11, 23], "overview": [12, 23], "own": [2, 12, 13, 23], "packag": [18, 23], "panda": 23, "paramet": 23, "part": [15, 17], "partial": 4, "pass": 3, "pca": 13, "pdf": 20, "perceptron": 14, "perform": [3, 11], "period": 5, "perspect": 3, "plethora": 23, "point": 6, "poisson": 4, "polynomi": [1, 5], "popul": 4, "popular": 23, "practic": [15, 21, 23], "pre": [3, 5], "predict": 6, "prerequisit": [5, 17, 23], "princip": 13, "principl": 5, "pro": 11, "probabl": [7, 20], "problem": [3, 4, 15, 23], "procedur": [11, 23], "process": [3, 5], "program": [4, 15], "project": [8, 21, 23], "prop": 15, "propag": [3, 14], "properti": [7, 20], "python": [0, 2, 11, 17, 18, 23], "quick": 10, "r": 23, "random": [12, 13, 20], "read": [11, 23], "real": [8, 23], "recommend": 23, "recurr": [6, 14], "reduc": 2, "reduct": 5, "reformul": 4, "regress": [0, 2, 7, 8, 9, 11, 12, 15, 23], "regular": 3, "relev": 22, "relu": 3, "remark": 5, "remind": [8, 10, 23], "replac": 15, "repositori": 0, "requir": [4, 17], "resampl": 8, "rescal": 8, "resourc": 4, "revisit": 15, "rewrit": 23, "ridg": [2, 7, 8], "rm": 15, "rule": 14, "same": 15, "sampl": 13, "schedul": 23, "schemat": 11, "scheme": 4, "scienc": 23, "scikit": [2, 3, 13, 23], "second": 15, "semest": 21, "set": [0, 2, 4, 5, 11, 14, 19, 23], "setup": 0, "sgd": 15, "should": 3, "similar": 15, "simpl": [2, 6, 11, 15, 23], "singl": 12, "singular": [7, 13], "sklearn": 1, "soft": 10, "softmax": 3, "softwar": 23, "solv": 4, "solver": 15, "some": [15, 18], "specifi": 4, "split": [0, 2], "squar": [1, 2, 7, 8, 12, 23], "standard": 15, "state": 2, "statist": [7, 8, 17, 20, 23], "steepest": [12, 15], "stochast": [15, 20], "strongli": 23, "suggest": 23, "summari": [21, 23], "superposit": 5, "supervis": 3, "support": 10, "svd": 7, "systemat": 5, "take": 1, "taken": 23, "teach": 21, "teacher": [21, 23], "techniqu": [8, 13], "technologi": 17, "tensorflow": [3, 5], "tent": [21, 23], "test": [0, 2, 3], "text": 23, "textbook": [22, 23], "theorem": [7, 10, 13, 14, 20], "theori": 20, "thi": 23, "tip": 15, "togeth": 14, "tool": 23, "top": 3, "topic": 23, "toward": 13, "trade": 8, "tradeoff": 8, "train": [0, 2, 3, 6, 23], "transform": 5, "tree": [11, 12], "tune": 3, "two": [5, 10, 17], "type": [4, 6, 14, 23], "uio": 23, "univers": [14, 22], "unsupervis": 16, "up": [0, 2, 4, 11, 14, 23], "us": [1, 2, 3, 4, 5, 9, 15, 17, 23], "v": 5, "valid": 8, "valu": [7, 13, 20], "variabl": 20, "varianc": 8, "variou": 2, "vector": [1, 10, 14, 18, 23], "versu": 23, "view": [2, 6, 12], "virtual": 0, "visual": [3, 11], "wai": 11, "wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12]}}) \ No newline at end of file +Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Discriminative Modeling": [[23, "discriminative-modeling"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example of discriminative modeling, taken from Generative Deeep Learning by David Foster": [[23, "example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Generative Versus Discriminative Modeling": [[23, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[3, "layers"]], "Layers used to build CNNs": [[5, "layers-used-to-build-cnns"]], "Learning goals": [[0, "learning-goals"], [1, "learning-goals"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[3, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[2, null]], "Linear Regression, basic elements": [[2, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[7, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[7, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[7, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[9, null], [9, "id1"]], "MNIST and GANs": [[6, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[11, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[12, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[7, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[10, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[5, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[7, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[13, "other-techniques"]], "Other types of networks": [[14, "other-types-of-networks"]], "Other ways of visualizing the trees": [[11, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[13, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[4, "partial-differential-equations"]], "Practical tips": [[15, "practical-tips"]], "Practicalities": [[21, "practicalities"], [21, "id1"]], "Predicting New Points With A Trained Recurrent Neural Network": [[6, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[5, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[15, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[11, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[15, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[12, "random-forests"]], "Randomized PCA": [[13, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[14, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[6, null]], "Reducing the number of degrees of freedom, overarching view": [[2, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[4, "reformulating-the-problem"]], "Regression Case": [[12, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[3, "regularization"]], "Reminder on Statistics": [[8, "reminder-on-statistics"]], "Replace or not": [[15, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[8, null]], "Resampling methods": [[8, "id1"]], "Resources on differential equations and deep learning": [[4, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[15, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[7, null], [7, "id1"]], "Same code but now with momentum gradient descent": [[15, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[11, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[14, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[4, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[15, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[11, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[2, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[4, null]], "Solving the one dimensional Poisson equation": [[4, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[4, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[15, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[2, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[15, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[15, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[15, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[10, null]], "Systematic reduction": [[5, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[21, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[2, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[13, "the-algorithm-before-theorem"]], "The Boston housing data example": [[2, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[3, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[11, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[11, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[5, "the-cifar01-data-set"]], "The MNIST dataset again": [[5, "the-mnist-dataset-again"]], "The RELU function family": [[3, "the-relu-function-family"]], "The Softmax function": [[3, "the-softmax-function"]], "The \\chi^2 function": [[2, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[8, "the-bias-variance-tradeoff"]], "The code for solving the ODE": [[4, "the-code-for-solving-the-ode"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[9, "the-logistic-function"]], "The moons example": [[10, "the-moons-example"]], "The multilayer perceptron (MLP)": [[14, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[4, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The plethora of machine learning algorithms/methods": [[23, "the-plethora-of-machine-learning-algorithms-methods"]], "The singular value decomposition": [[7, "the-singular-value-decomposition"]], "The two-dimensional case": [[10, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[13, "towards-the-pca-theorem"]], "Train and test datasets": [[3, "train-and-test-datasets"]], "Two-dimensional Objects": [[5, "two-dimensional-objects"]], "Type of problem": [[4, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[15, "using-autograd"]], "Using forward Euler to solve the ODE": [[4, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[15, "using-gradient-descent-methods-limitations"]], "Visualization": [[3, "visualization"], [3, "id1"]], "Visualizing the Tree, Classification": [[11, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "What Is Generative Modeling?": [[23, "what-is-generative-modeling"]], "What is Machine Learning?": [[2, "what-is-machine-learning"]], "What is a good model?": [[2, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[23, "what-is-a-good-model-can-we-define-it"]], "Which activation function should I use?": [[3, "which-activation-function-should-i-use"]], "Why Linear Regression (aka Ordinary Least Squares and family)": [[23, "why-linear-regression-aka-ordinary-least-squares-and-family"]], "Wisconsin Cancer Data": [[9, "wisconsin-cancer-data"]], "Writing Our First Generative Adversarial Network": [[6, "writing-our-first-generative-adversarial-network"]], "Writing our own PCA code": [[13, "writing-our-own-pca-code"]], "XGBoost: Extreme Gradient Boosting": [[12, "xgboost-extreme-gradient-boosting"]], "scikit-learn implementation": [[3, "scikit-learn-implementation"]]}, "docnames": ["E1", "E2", "chapter1", "chapter10", "chapter11", "chapter12", "chapter13", "chapter2", "chapter3", "chapter4", "chapter5", "chapter6", "chapter7", "chapter8", "chapter9", "chapteroptimization", "clustering", "intro", "linalg", "schedule", "statistics", "teachers", "textbooks", "week34"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["E1.ipynb", "E2.ipynb", "chapter1.ipynb", "chapter10.ipynb", "chapter11.ipynb", "chapter12.ipynb", "chapter13.ipynb", "chapter2.ipynb", "chapter3.ipynb", "chapter4.ipynb", "chapter5.ipynb", "chapter6.ipynb", "chapter7.ipynb", "chapter8.ipynb", "chapter9.ipynb", "chapteroptimization.ipynb", "clustering.ipynb", "intro.md", "linalg.ipynb", "schedule.md", "statistics.ipynb", "teachers.md", "textbooks.md", "week34.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 23], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "00": [2, 3, 7, 13, 23], "000": [3, 5], "00000000e": 23, "001": [4, 10, 15], "004": 7, "00727646693": [2, 23], "0086649156": [2, 23], "01": [2, 3, 4, 7, 11, 13, 15, 22, 23], "0110": 20, "01719003e": 23, "02": [2, 6, 9, 14, 23], "02334824": 23, "02857": 6, "02f": 8, "03077640549": 6, "03097597e": 23, "031": 7, "04": 13, "0458": 11, "05": [6, 8, 23], "062292565": 6, "062435": 23, "06730814": 23, "07": 23, "0713": [2, 23], "07285": 5, "08": 20, "08078025e": 23, "08336233266": 6, "0917": 11, "0n": [2, 23], "1": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23], "10": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23], "100": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "1000": [2, 3, 4, 6, 7, 10, 13, 15, 16, 17, 20, 23], "10000": [4, 7, 8, 12, 13, 15, 20], "100000": 10, "10001": 12, "1001": 20, "1002": 20, "1003": 20, "1005": 20, "1009": 20, "101": 1, "1011": 20, "1013": 20, "1013904243": 20, "1015": 20, "102": 1, "1023": 20, "1024": 5, "1026": 20, "1027": 20, "103": 3, "1030": 20, "1037": 20, "1038": 20, "1040": 20, "1047": 20, "107": 1, "108": 23, "10th": 11, "10x": [2, 23], "11": [1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 22, 23], "110": 23, "1100": 20, "1101": 20, "111": [3, 9, 14], "112": 1, "11340253": 23, "11590451": 23, "116": 1, "117": 1, "118": 1, "12": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 18, 20, 22, 23], "120": 5, "121": [1, 10, 11, 12], "1215pm": [21, 23], "122": [10, 11, 12, 23], "124": [2, 23], "125": 1, "127": [1, 6], "128": [5, 6, 15], "129": 1, "1298": 11, "12pm": [21, 23], "13": [2, 4, 11, 14, 18, 20, 23], "131": 1, "133": 9, "135": 1, "136": 1, "14": [2, 4, 6, 8, 10, 11, 12, 14, 18, 20, 22], "141": 1, "143": 1, "1446729567": 6, "149": 1, "14g": 8, "15": [2, 4, 6, 8, 9, 10, 11, 14, 15, 20, 23], "150": [6, 10], "152": [1, 23], "153760": 23, "156": [1, 23], "157": 23, "158": 23, "159": [1, 23], "15g": 8, "15pm": 23, "16": [3, 4, 5, 6, 7, 10, 11, 12, 20, 23], "160": [1, 23], "1603": 5, "161": 1, "162": 1, "16231451": 6, "163": 1, "16384": 5, "164": 1, "167": 1, "17": [3, 4, 10, 20, 23], "172": 1, "173": 1, "176": 1, "178": 1, "179": 1, "1797": 3, "18": [4, 8, 9, 10, 11, 12, 20, 23], "1807": 6, "18392847": 23, "19": [4, 20, 23], "1940": 2, "1943": 14, "1970": [18, 23], "1973": 11, "1979": 8, "1_1": 14, "1_2": 14, "1_3": 14, "1cm": [2, 10, 12, 20, 23], "1d": [3, 4, 5], "1e": [4, 6, 15, 16], "1e10": 16, "1e4": 8, "1f": 3, "1k": 18, "1n": [2, 23], "1x": [2, 23], "2": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "20": [1, 2, 3, 4, 8, 9, 10, 20, 21, 23], "200": [2, 4, 5, 6, 10, 11, 12], "2000": 2, "2004": 15, "2006": 22, "20072279": 23, "2008": 23, "2010": 3, "2011": 3, "2014": 6, "2015": 3, "2016": [2, 23], "2018": [2, 8], "2021": [8, 16], "2022": 23, "2025": 23, "21": [2, 3, 7, 9, 11, 14, 18, 23], "2116753732": 6, "215pm": [21, 23], "2167072": 23, "22": [2, 3, 7, 14, 15, 18, 23], "221": 10, "225": 6, "22948497": 23, "23": [3, 14, 18, 23], "24": [2, 3, 18, 23], "25": [4, 5, 6, 7, 8, 10, 11, 13], "250": [4, 6, 9, 11], "25000": 2, "250154": 23, "253775": 23, "255": 5, "256": 6, "26": 23, "26303845": 23, "264": 23, "265": 23, "265109911": 6, "266": 23, "269": 23, "27": [2, 3], "270": 23, "27n_": 20, "28": [3, 5, 6], "2830637392": 6, "2861": 20, "2873": 11, "2882": 20, "2886": 20, "2890": [2, 23], "2892": 20, "29": 23, "2915": 20, "2931": 23, "29364655": 23, "294399745619595": 23, "296247": 23, "2968": 23, "2980": 23, "298273": 23, "298375": 23, "2990": 23, "2_": 14, "2_1": 14, "2_2": 14, "2_3": 14, "2_i": 14, "2_m": [8, 20], "2_t": 15, "2_x": 20, "2b": 20, "2cm": 10, "2d": [3, 5, 13, 14, 17, 23], "2e": 8, "2f": [2, 9, 11, 12, 13, 14, 23], "2g": 4, "2g_i": 4, "2k": 5, "2m": 8, "2n": [2, 4, 5, 23], "2nd": 11, "2p": 20, "2pt": 6, "2x": [2, 5, 10, 15, 23], "2x_ix_jy_iy_j": 10, "2x_j": 10, "2y_i": 12, "2y_j": 10, "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "30": [2, 3, 6, 8, 9, 12, 15, 21], "30000": [2, 23], "3072": 5, "31": [14, 18, 20], "315": 8, "3155": [2, 7, 8], "32": [5, 6, 8, 14, 15, 18, 20], "3200": 3, "3250": 3, "3297": 23, "33": [14, 18, 21], "3303": 23, "3310": 23, "332331": 23, "333": 9, "3331": 23, "3337": 23, "34": 18, "3436": [2, 23], "3437": [2, 23], "35": [2, 8, 23], "3581341341": 6, "359": 7, "36": [2, 7, 8, 20], "370782966": 6, "38": 20, "39": [2, 21, 23], "3d": [1, 4, 5, 6, 8, 15], "3f": [3, 5, 11], "3n": 18, "3x": [4, 10], "3x_i": 4, "3y": 10, "4": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "40": [3, 8, 21, 23], "400": 6, "4000": 23, "4050": [22, 23], "41": 18, "4155": [0, 4], "41589548": 23, "42": [3, 6, 10, 11, 12, 18, 23], "43": [2, 9, 18], "4310": 23, "436462435": 6, "44": [2, 18], "45": [21, 23], "46": [21, 23], "462": 9, "47": [21, 23], "479465113": 6, "47958494": 23, "48": 23, "48257387": [21, 23], "49": [7, 8, 13], "49152": 5, "4940954": [2, 23], "4990": 20, "4992": 20, "4997": 20, "4c4c7f": [11, 12], "4d": 5, "4f": 8, "4pm": [21, 23], "4y": 10, "4y_i": 12, "5": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "50": [3, 4, 5, 6, 8, 9, 10, 12, 15, 23], "500": [3, 5, 6, 8, 11, 12, 15], "5018": 20, "506": 2, "507d50": [11, 12], "50j": 15, "50x10": 3, "51": [12, 23], "510": 3, "512132": 23, "5177783846": 6, "53": 11, "54": [8, 20, 23], "5411205": 23, "54894451": 23, "55": [3, 23], "56": 3, "56536": [2, 23], "569": 3, "57": [2, 10, 21, 23], "571": 7, "58": [12, 21, 23], "591317992": 6, "5cm": 20, "5f": 10, "5x": 10, "5y": 10, "6": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 21, 23], "60": [3, 5], "60000": 6, "6019067271": 6, "606439": 23, "625": 9, "63": [2, 3], "64": [3, 5, 6, 15, 18, 23], "64x50": 3, "65": [3, 10, 11], "6887363571": 6, "69": [1, 20], "69069n_": 20, "691": 23, "6n_": 20, "7": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "70": [3, 9], "70653767": 6, "71": 3, "724": 5, "73": 23, "7304881": 23, "75": [7, 8, 10, 13], "76": [21, 23], "765": 9, "77": [21, 23], "7718": 11, "7782028952": 6, "77893972": 23, "78": 23, "7d7d58": [11, 12], "8": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 21, 23], "80": [2, 3, 7, 10], "800": [6, 9], "81": 3, "815am": [21, 23], "85": 3, "8702784034": 6, "88": 23, "8f": 8, "8g": 8, "8n": 18, "8x8": 3, "9": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "90": 3, "9040": 11, "91": [21, 23], "92": [21, 23], "93": 1, "931": [2, 23], "933": 7, "937": 20, "938": 20, "939": [2, 20, 23], "94": 20, "95": [3, 13, 23], "954": 20, "955820c21e8b": 6, "96": 8, "960": 20, "961": 20, "962": 20, "9649652536": 6, "96611194e": 23, "9780387310732": 22, "9780387848570": 22, "9781098134174": 23, "9781492032632": 22, "9781801819312": 23, "98": [1, 2, 3], "985": 20, "986": 20, "989": 20, "9898ff": [11, 12], "99": [1, 15], "991": 20, "992": 20, "993": 20, "996": 7, "999": [11, 20], "9x": 8, "9y": 8, "A": [0, 1, 4, 5, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22], "AND": 4, "And": [2, 5, 6, 7, 8, 11, 15, 17, 20], "As": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 18, 20, 23], "At": [2, 6, 8, 15, 23], "BE": [2, 23], "Be": [4, 17, 23], "Being": 15, "But": [1, 2, 3, 4, 5, 7, 8, 11, 12, 20], "By": [2, 5, 7, 8, 14, 15, 18, 23], "For": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "IF": 8, "IN": 22, "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "Ising": [7, 14], "It": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "Its": [3, 4, 6, 13], "No": [8, 11, 23], "Not": [2, 3, 7, 8], "OR": 20, "Of": 20, "On": [2, 5, 20, 21, 22, 23], "One": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 20], "Or": [2, 3, 8, 23], "Such": [1, 2, 8, 14, 20], "That": [2, 7, 9, 12, 13, 14, 16, 20, 23], "The": [1, 6, 12, 15, 16, 18, 19, 20, 21, 22], "Then": [0, 1, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "There": [0, 2, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 21, 23], "These": [2, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20], "With": [1, 2, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 23], "_": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "_0": [7, 10, 12, 13, 15], "_1": [4, 7, 8, 10, 12, 13, 14, 15, 16, 18], "_2": [4, 7, 10, 13, 14, 15, 18], "_3": 18, "_4": 18, "_9": 15, "__class__": 12, "__doc__": 8, "__future__": [10, 11], "__init__": 3, "__main__": 4, "__name__": [4, 12], "_auto1": [4, 5, 6, 7, 8, 9, 14, 15, 18, 20], "_auto10": [8, 14], "_auto11": 8, "_auto12": 8, "_auto2": [4, 5, 6, 7, 8, 14, 15, 18, 20], "_auto3": [5, 6, 7, 8, 14, 15, 18], "_auto4": [6, 8, 14, 15, 18], "_auto5": [6, 8, 14, 15, 18], "_auto6": [6, 8, 14, 18], "_auto7": [6, 8, 14, 18], "_auto8": [8, 14], "_auto9": [8, 14], "_build": [2, 17, 22, 23], "_c": 3, "_compon": 13, "_depth": 11, "_export": [0, 1], "_fraction": 11, "_i": [2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 23], "_j": [2, 3, 4, 5, 7, 8, 10, 15], "_k": 15, "_l": 14, "_lambda": 8, "_leaf": 11, "_m": 12, "_multilayer_perceptron": 23, "_n": [4, 7, 10, 13, 15], "_node": 11, "_p": [7, 10], "_ratio": 13, "_sampl": 11, "_split": [8, 11], "_t": 15, "_test": 8, "_varianc": 13, "_weight": 11, "a0": 5, "a0faa0": [11, 12], "a1": [2, 23], "a2": [2, 23], "a3": [2, 23], "a4": [2, 23], "a_": [1, 2, 3, 18, 23], "a_0": [2, 23], "a_1a": [2, 23], "a_2a": [2, 23], "a_3": [2, 23], "a_3a": [2, 23], "a_4": [2, 23], "a_4a": [2, 23], "a_h": 3, "a_i": [2, 3, 4, 14, 23], "a_j": [3, 14], "a_k": [2, 3, 14], "aaron": 22, "ab": [2, 4, 7, 15, 16, 23], "ab_channel": 17, "abandon": 3, "abid": 20, "abil": [2, 12], "abl": [1, 2, 3, 6, 7, 8, 9, 12, 14, 15], "about": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21], "abov": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "abovement": [8, 23], "abscissa": 15, "absolut": [2, 4, 7, 8, 15, 23], "abstract": 3, "acceler": 15, "accept": [2, 5, 8, 11], "access": [2, 5, 13, 20, 23], "accid": [6, 8], "accompani": [2, 23], "accomplish": [10, 11, 15], "accord": [2, 3, 4, 7, 8, 11, 14, 15, 16, 20, 23], "accordingli": 13, "account": [0, 1, 2, 5, 7, 15, 20, 23], "accumul": [14, 15, 20], "accur": [2, 5, 6, 8, 12, 15], "accuraci": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 23], "accuracy_scor": [2, 3, 12, 23], "accuracy_score_numpi": 3, "achiev": [2, 3, 7, 8, 10, 14, 18, 23], "aco": 20, "acquaint": 17, "acquir": [3, 17, 23], "acr": 2, "across": [3, 5, 8, 11, 17, 23], "act": [3, 5, 18], "action": 20, "activ": [0, 2, 4, 5, 6, 11, 19, 21, 23], "actual": [0, 1, 2, 3, 6, 7, 8, 10, 13, 18, 20, 23], "ad": [0, 1, 3, 5, 6, 7, 10, 15, 18], "ada_clf": 12, "adaboostclassifi": 12, "adadelta": 15, "adam": [3, 5, 6, 23], "adapt": [6, 8, 15, 22], "add": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 20, 21, 23], "add_subplot": [3, 9, 14, 16], "addendum": 7, "addit": [0, 2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23], "addition": [14, 15], "address": [3, 11, 13, 15, 23], "adjac": [5, 14], "adjoint": 7, "adjust": [2, 7, 14, 15], "admir": [2, 23], "advanc": [6, 8, 14, 22, 23], "advantag": [3, 5, 7, 8, 12, 15, 18], "adversari": 23, "afecionado": 23, "affect": [0, 5], "affin": [2, 5, 10, 13], "afford": 5, "aficionado": 23, "aforement": 16, "african": 2, "after": [0, 1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 17, 18, 20, 23], "afterward": [2, 23], "ag": [2, 9, 23], "ag_0": 4, "again": [2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "against": [3, 6, 9, 12], "agegroup": 9, "agegroupmean": 9, "aggreg": [11, 12], "agorithm": 12, "agre": [7, 8, 20], "agreement": 15, "ahead": 11, "ai": [2, 22], "aid": 13, "aim": [1, 2, 3, 6, 8, 9, 13, 16, 17, 18], "ainv": 7, "airplan": 5, "aka": 7, "al": [1, 2, 4, 6, 22, 23], "alarm": [7, 9], "algebra": [2, 5, 7, 15, 17], "algorithm": [1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 16, 17, 18, 20, 22], "align": [2, 4, 7, 8, 9, 10, 15, 20, 23], "all": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "allevi": [3, 15], "alloc": [5, 18], "allow": [2, 3, 4, 5, 7, 8, 10, 12, 15, 17, 18, 23], "almost": [2, 3, 8, 10, 13, 15, 20], "alon": [4, 11], "along": [0, 4, 5, 6, 7, 8, 11, 12, 13, 17, 18, 23], "alpha": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 23], "alpha_": 12, "alpha_0": 5, "alpha_1": 5, "alpha_2": 5, "alpha_i": [5, 15], "alpha_k": 15, "alpha_m": 12, "alpha_n": 5, "alpha_opt": 15, "alreadi": [0, 4, 5, 6, 7, 8, 12, 14, 17, 18, 20, 23], "also": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "alter": 3, "altern": [2, 3, 6, 7, 8, 10, 11, 13, 15, 18, 23], "although": [1, 2, 3, 7, 8, 10, 12, 15, 23], "alwai": [1, 2, 5, 7, 8, 14, 15, 20, 23], "am": 6, "ame2016": [2, 23], "american": 2, "among": [2, 5, 7, 11, 12, 14, 18, 23], "amongst": 7, "amount": [2, 3, 5, 6, 8, 10, 12, 16, 17], "an": [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22], "an_": 20, "anaconda": [2, 3, 17, 23], "analogi": 15, "analys": 8, "analysi": [3, 5, 6, 9, 16, 18, 22], "analyt": [4, 5, 7, 8, 9, 14, 15, 17, 23], "analyz": [1, 2, 3, 5, 6, 7, 8, 20], "andrew": 3, "angl": [2, 5, 11], "anharmon": 5, "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 23], "anim": [6, 14], "ann": 14, "annot": [2, 3, 5, 9, 10, 23], "announc": 23, "anoth": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "ansatz": [2, 23], "answer": [2, 3, 5, 7, 8, 18, 21, 23], "antialias": [4, 8], "anticip": 6, "anymor": [3, 10], "anyon": [0, 6, 10], "anyth": [0, 1, 3, 20], "anytim": [21, 23], "apach": 3, "apart": [13, 15], "api": [3, 17, 23], "appar": 4, "appear": [2, 3, 5, 15, 18, 20], "append": [3, 5, 6, 10, 11, 15, 23], "appli": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 20, 22, 23], "applic": [1, 2, 3, 5, 6, 7, 8, 9, 11, 14, 15, 18, 20, 22, 23], "apply_gradi": 6, "approach": [0, 1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 22], "appropri": [4, 8, 11, 14, 15, 17, 20], "approv": 23, "approx": [2, 4, 5, 8, 12, 13, 15, 20, 23], "approxim": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 15, 20, 23], "apt": [2, 17, 23], "aq": 20, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "aragorn": 23, "arang": [3, 5, 6, 8, 9, 11, 12, 14, 15, 23], "arbitrari": [3, 6, 8, 10, 14, 15, 20], "arbitrarili": [2, 3, 13, 23], "arc": 8, "architectur": [5, 6, 14], "area": [2, 5, 8, 22, 23], "argmax": [3, 13], "argmin": [6, 12, 16], "argsort": 13, "argu": [3, 15], "argument": [2, 4, 5, 7, 13, 14, 15, 23], "aris": [2, 8, 14, 15, 20, 23], "arithmet": [2, 15, 18, 23], "arm": 8, "armadillo": 18, "around": [2, 3, 6, 7, 8, 13, 20, 23], "arrai": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20], "arrang": [5, 23], "arraybox": 15, "arriv": [2, 8, 11, 13, 18, 20, 23], "arrow": 14, "arrowprop": 10, "art": [2, 3, 17], "articl": [2, 5, 6, 8, 12, 23], "artifici": [2, 4, 9, 14, 22, 23], "artificialneuron": 14, "arug": 15, "arxiv": [5, 6], "asarrai": [2, 8, 11], "ask": [0, 7, 8, 13, 14], "aspect": [2, 8, 17, 23], "assembl": 5, "assembli": [2, 23], "assert": 6, "assess": [2, 8, 23], "assici": 6, "assign": [0, 2, 9, 10, 11, 14, 15, 16, 19, 21, 22, 23], "associ": [2, 8, 11, 14, 16, 20, 23], "assum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "assumpt": [2, 5, 7, 8, 11, 13, 20, 23], "ast": [2, 7, 8, 23], "astyp": [6, 11, 12], "asymmetri": [2, 23], "asymptot": [6, 8], "atom": [2, 23], "attempt": [2, 6, 8, 9, 10, 12, 23], "attend": 23, "attent": [2, 18, 23], "attract": [2, 12, 23], "attribut": [2, 11, 23], "audi": [2, 23], "audio": [5, 6], "august": 23, "aurelien": [2, 22, 23], "austfjel": 8, "auth": 0, "authent": 0, "author": [2, 3, 12, 20], "authour": 23, "auto": [11, 12, 20], "autocor": 20, "autocorrelation_tim": 20, "autocorrelform": 20, "autocovari": 20, "autoencod": [6, 17, 23], "autoencond": 17, "autograd": [17, 23], "autom": [2, 17, 22, 23], "automac": 18, "automag": 23, "automat": [1, 2, 3, 4, 5, 6, 13, 17, 18, 23], "automobil": 5, "autonom": 6, "avail": [2, 3, 6, 8, 12, 13, 17, 18, 19, 21, 22, 23], "averag": [2, 3, 5, 8, 11, 12, 15, 16, 20, 21, 23], "avoid": [2, 6, 7, 8, 11, 13, 15, 18], "awai": [4, 5, 8], "awar": [4, 12], "award": [21, 23], "ax": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 23], "axes3d": [4, 8, 15], "axes_grid1": 8, "axhlin": 10, "axi": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "axiom": 7, "axlabel": 2, "axvlin": [6, 10], "axvspan": 6, "b": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 20, 21, 23], "b1": 10, "b2": 10, "b3": 10, "b_": [2, 3, 18], "b_0": 2, "b_1": [2, 4, 14, 15], "b_2": [2, 15], "b_5": 15, "b_group": 11, "b_i": [2, 3, 4, 14, 23], "b_ia_": [2, 23], "b_ia_i": 2, "b_index": 11, "b_j": [3, 14], "b_k": [2, 3, 14, 15], "b_m": 14, "b_score": 11, "b_valu": 11, "babcock": 23, "bachelor": [19, 21], "back": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 18, 20, 23], "backbon": 18, "backend": [3, 6], "background": [22, 23], "backpropag": 3, "backtrack": 11, "backup": 18, "backward": [3, 4, 6, 14, 18], "bad": 8, "badli": 20, "bag": [11, 17, 23], "bag_clf": 12, "baggin": 23, "baggingboot": 12, "baggingclassifi": 12, "baggingtre": 12, "balanc": 8, "band": 18, "bandwidth": 18, "bar": [2, 8, 13, 23], "barber": 22, "bare": [6, 12], "base": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23], "basi": [7, 9, 10, 12, 13, 14, 15, 18], "basic": [0, 8, 10, 14, 15, 16, 17, 20, 23], "batch": [5, 6, 13, 14, 15], "batch_shap": 6, "batch_siz": [3, 5, 6], "batchnorm": 6, "bay": 9, "bayesian": [7, 17, 22, 23], "becaus": [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 23], "becom": [2, 3, 4, 7, 8, 9, 11, 14, 15, 20, 23], "been": [2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 17, 18, 23], "befor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 18, 20, 23], "beforehand": [2, 20, 23], "begin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 20, 21, 23], "behav": [3, 8, 15], "behavior": [2, 3, 15, 23], "behaviour": 14, "behind": [2, 3, 8, 10, 15, 23], "being": [2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 20, 23], "believ": [11, 18], "belong": [9, 10, 11, 15, 16], "below": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "benchmark": 12, "benefici": [3, 15], "benefit": [2, 3, 6, 13, 15, 17, 23], "bengio": [3, 22, 23], "benign": [3, 9], "besid": [6, 7], "bessel": 7, "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 21, 23], "beta": [1, 2, 3, 5, 7, 8, 9, 12, 13, 15, 23], "beta_": [2, 5, 8, 9, 15], "beta_0": [1, 2, 3, 5, 7, 8, 9, 15], "beta_0x_": 2, "beta_1": [2, 3, 5, 7, 8, 9, 12, 15], "beta_1x_": 2, "beta_1x_0": 2, "beta_1x_1": [2, 9], "beta_1x_2": 2, "beta_1x_i": [9, 15], "beta_2": [2, 5, 15], "beta_2x_": 2, "beta_2x_0": 2, "beta_2x_1": 2, "beta_2x_2": [2, 9], "beta_3": 5, "beta_i": [2, 5, 7], "beta_j": [2, 7, 8, 15], "beta_k": 15, "beta_linreg": 15, "beta_m": 12, "beta_mg_m": 12, "beta_n": 5, "beta_p": 9, "beta_px_p": 9, "betavalu": 7, "better": [2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 23], "between": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 23], "beyond": [2, 3, 7, 8, 10, 15, 23], "bf": [15, 16, 18, 20], "bg": 23, "bgd": 15, "bia": [2, 3, 4, 5, 7, 10, 11, 12, 14, 15, 23], "bias": [3, 4, 5, 7, 8, 11, 14], "big": [2, 3, 4, 7, 8, 16], "bigger": [3, 8], "bigr": 14, "bike": 11, "bilbo": 23, "billion": [5, 14, 17], "bin": [2, 9, 20], "binari": [2, 5, 7, 9, 11, 12, 14, 23], "binarycrossentropi": 6, "bind": 2, "binomi": [17, 20, 23], "binsboot": 8, "bioinformat": 2, "biolog": [3, 14], "bios1100": [17, 23], "bird": [2, 5], "birth": 23, "bishop": [22, 23], "bit": [3, 6, 18, 20, 23], "bitwis": 20, "bivari": 4, "bk": [2, 15], "bla": [18, 23], "black": [10, 11, 16], "block": [8, 12, 17, 18, 20, 23], "blog": 23, "blogpost": 6, "blue": [2, 5], "bmatrix": [2, 3, 5, 7, 9, 10, 13, 15, 18, 23], "bmi": 3, "bodi": [2, 3, 6, 14], "bold": 3, "boldfac": [1, 2, 7], "boldsymbol": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 23], "boltzmann": [14, 17, 23], "book": [22, 23], "book1": 22, "boolean": 6, "boost": [3, 11, 17, 23], "boostrap": 12, "bootstrap": [3, 15, 17, 23], "borrow": 23, "boston_dataset": 2, "bot": 10, "both": [0, 1, 2, 3, 6, 7, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23], "bottl": 9, "bound": [2, 10, 14], "boundari": [4, 6, 10, 13, 14], "box": [6, 11], "boyd": [10, 15], "bracket": [6, 20], "brain": [3, 9, 14], "branch": [11, 23], "break": [2, 6, 8, 13, 16, 23], "breast": [7, 9, 13], "breviti": 15, "brew": [2, 17, 23], "brg": 10, "briefli": [1, 2, 23], "bring": [2, 7, 8, 12], "britt": [21, 23], "broad": 2, "broadli": 23, "brought": [15, 17, 23], "brownle": 6, "browser": [0, 23], "brute": [5, 7, 13], "buffer_s": 6, "bui": 6, "build": [1, 2, 6, 7, 8, 12, 18, 20, 23], "built": [2, 3, 5, 6, 8], "bunch": 13, "busi": 2, "byte": [18, 23], "c": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "c1": [10, 13], "c2": [10, 13], "c_": [2, 10, 11, 12, 15, 20], "c_0": 20, "c_1": 14, "c_2": 14, "c_3": 14, "c_4": 14, "c_i": [14, 15], "c_k": 20, "ca": [3, 23], "cach": 12, "cal": [2, 10, 12, 14, 15], "calcul": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "call": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "calor": 2, "cambridg": [15, 22], "can": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "cancel": [2, 15, 23], "cancer": [7, 12], "cancerpd": 9, "candid": [10, 11, 12], "cannot": [2, 3, 6, 7, 8, 9, 10, 11, 20], "canopi": [2, 17, 23], "canva": [0, 1, 23], "cap": 7, "capabl": [2, 3, 10, 15, 17, 23], "capac": [4, 21], "capita": 2, "captur": [6, 13, 14, 23], "car": [5, 6], "card": [2, 9, 23], "cardin": 3, "care": [0, 13], "carefulli": 15, "carlo": [2, 8, 17, 20, 22, 23], "carri": [4, 8, 9], "cart": 12, "case": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 17, 18, 23], "casella": 22, "cast": 3, "cat": [5, 6], "catch": 2, "categor": [2, 3, 5, 11, 13, 23], "categori": [2, 3, 5, 9, 12, 14, 16, 23], "categorical_crossentropi": [3, 5], "caus": [2, 7, 8, 20, 23], "causal": 2, "causat": [2, 23], "cax": 3, "cb": [8, 23], "cbar": 3, "cc": [2, 3, 7, 15, 23], "ccc": [7, 14], "cdf": 20, "cdot": [2, 4, 8, 14, 15, 16, 18, 20, 23], "celebr": 15, "cell": 6, "center": [2, 3, 8, 9, 10, 11, 13, 16, 20, 23], "central": [1, 2, 5, 7, 8, 10, 18, 23], "centroid": [16, 20], "centroid_differ": 16, "centuri": 5, "certain": [2, 5, 8, 9, 11, 20, 23], "cg": 15, "cha": 2, "chain": [2, 3, 15, 17, 20, 23], "challeng": 0, "chanc": [3, 7, 15, 20], "chang": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "channel": 5, "chapter": [1, 2, 8, 12, 13, 18, 22, 23], "chapter3": 2, "charact": [2, 5, 7, 23], "character": [10, 11, 12, 14, 20], "characterist": [2, 3, 5, 12, 15, 23], "charg": [2, 23], "charl": 2, "chase": 6, "chatgpt": 0, "chd": 9, "chddata": 9, "cheap": 7, "cheaper": [3, 15], "check": [0, 1, 2, 3, 5, 6, 7, 13, 15, 18, 23], "checkmark": 5, "checkpoint": 6, "checkpoint_dir": 6, "checkpoint_prefix": 6, "chen": 12, "chiaramont": 4, "childcar": 1, "children": 1, "choic": [2, 3, 4, 5, 6, 8, 11, 14, 15, 16, 18, 23], "choleski": [7, 18], "choos": [0, 4, 5, 8, 11, 12, 13, 15, 16], "chosen": [1, 2, 3, 4, 8, 10, 11, 12, 15, 20, 23], "chosen_datapoint": 3, "christian": 22, "christoph": [22, 23], "cifar": 5, "cifar10": 5, "circ": [3, 14], "circl": [2, 10, 14], "circuit": 5, "circumfer": 11, "circumv": [3, 7, 15], "ckpt": 6, "clariti": 20, "class": [2, 3, 5, 6, 8, 9, 10, 11, 13, 14, 15, 20, 23], "class_nam": [5, 11], "class_val": 11, "class_valu": 11, "classic": [9, 11, 15], "classif": [2, 5, 7, 8, 9, 10, 13, 14, 17, 22, 23], "classifi": [2, 3, 6, 9, 11, 12, 13, 23], "classificaton": 3, "classifii": 12, "clean": 3, "clear": [3, 7, 12, 14, 15], "clearli": [2, 5, 7, 8, 9, 10, 20], "clever": [3, 12], "clf": [2, 8, 10, 11, 12, 23], "clf3": 2, "clf_lasso": 8, "clf_ridg": 8, "cli": 0, "clip": [5, 20], "clone": [0, 21], "close": [2, 3, 4, 6, 8, 10, 11, 13, 14, 15, 16, 20, 22, 23], "closer": [5, 7, 15], "closest": [10, 13, 15, 16], "closur": [17, 23], "cloud": [17, 23], "cluster": [2, 3, 6, 8, 13, 17, 23], "cluster_label": 16, "cm": [3, 4, 5, 8, 10, 15], "cmap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 23], "cmap_arg": 8, "cmd": [0, 11], "cn_": 20, "cnn": 14, "cnn_kera": 5, "cntk": [17, 23], "co": [2, 4, 5, 8, 11, 15, 23], "code": [5, 6, 8, 9, 10, 17, 18, 20, 22], "coef": [2, 23], "coef0": 10, "coef_": [1, 2, 7, 8, 10, 11, 15, 23], "coeff": 7, "coeffici": [2, 5, 7, 8, 9, 10, 11, 15, 18, 23], "coerc": [2, 8, 23], "coin": [12, 20], "coin_toss": 12, "col": [2, 13, 23], "colab": [17, 23], "cold": 11, "colinear": 2, "collaps": 10, "collect": [2, 4, 8, 12, 13, 17, 20, 22, 23], "collinear": 7, "color": [2, 5, 6, 8, 10, 11, 12, 20], "color_channel": 5, "color_cod": 8, "colorbar": [3, 8], "colsample_bytre": 12, "colsaobject": 12, "column": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 18, 23], "columntransform": 11, "com": [0, 1, 6, 8, 17, 22, 23], "combin": [3, 4, 7, 8, 9, 12, 20], "come": [0, 2, 3, 5, 6, 7, 14, 15, 16, 23], "command": [0, 2, 3], "comment": [2, 6, 7, 8], "commerci": [2, 17, 23], "commit": 0, "commod": [2, 23], "common": [1, 2, 3, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "commonli": [2, 3, 6, 8, 9, 11, 15, 16], "commun": [2, 14], "commut": 5, "commutatitav": 5, "compact": [2, 3, 5, 7, 8, 9, 11, 13, 14, 15, 16, 23], "compair": 2, "compar": [2, 5, 6, 7, 8, 13, 15, 18, 23], "comparison": [4, 6, 15], "compat": 9, "compet": 2, "competit": 12, "compil": [2, 3, 5, 6, 15, 17, 18, 23], "complet": [0, 1, 2, 4, 5, 6, 11, 14, 23], "completenn": 14, "complex": [1, 3, 7, 10, 11, 13, 14, 15, 23], "complic": [2, 3, 11, 15, 23], "compon": [1, 2, 3, 5, 6, 7, 8, 9, 11, 16, 17, 23], "components_": 13, "compos": [11, 14, 15, 16, 17, 23], "compphys": [1, 2, 8, 17, 19, 21, 22, 23], "compress": [2, 23], "compris": 8, "compromis": 7, "compulsori": [17, 23], "comput": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23], "computation": [2, 5, 8, 11, 15, 20, 23], "computationalscienceuio": 23, "concaten": [4, 6, 8, 16], "concav": [3, 15], "concentr": [2, 12], "concept": [2, 4, 17, 23], "conceptu": [14, 15], "concern": [2, 3, 6, 9, 23], "concic": 23, "conclud": [2, 7, 15], "conclus": 3, "cond": 4, "conda": [2, 3, 17, 23], "condit": [2, 4, 6, 7, 8, 10, 11, 13, 15, 20, 23], "conduct": 17, "condwav": 4, "confid": [2, 7, 8, 9, 10, 23], "configur": 5, "confirm": [7, 14], "confus": [7, 8, 9, 12, 18], "confusion_matrix": 11, "congruenti": 20, "conjug": [6, 10], "conjugaci": 15, "conjunct": 5, "connect": [2, 3, 5, 6, 11, 13, 14, 15, 18, 23], "consequ": [7, 8, 10, 12, 14, 15], "conserv": [7, 16], "consid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "consider": [2, 3, 7, 15, 23], "consist": [2, 3, 4, 5, 6, 8, 14, 15, 20], "constant": [1, 2, 4, 6, 7, 8, 10, 14, 15, 20, 23], "constitu": [2, 23], "constitut": [4, 8], "constrain": [3, 5, 7, 9, 13], "constraint": [7, 8, 10, 15], "construct": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 20, 23], "contact": [2, 23], "contain": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "contemporari": 23, "content": [0, 3, 17, 18, 23], "context": [8, 12, 15], "contigu": 18, "continu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "contour": [11, 12, 15], "contourf": [10, 11, 12], "contrast": [3, 6, 11, 12, 14, 23], "contribut": [2, 5, 7, 15, 20, 23], "contributor": 2, "control": [0, 2, 3, 5, 11, 15, 17, 23], "conv": [5, 6], "conv2d": [5, 6], "conv2dtranspos": 6, "convei": 23, "conveni": [2, 7, 8, 14, 15, 18, 23], "convent": 14, "converg": [3, 4, 6, 7, 10, 15, 16, 23], "convergencewarn": 23, "convert": [2, 3, 6, 7, 11, 13, 15, 18, 23], "converttomatrix": 6, "convex": [6, 7, 9], "convinc": 15, "convolut": [3, 6, 17, 23], "cool": [6, 11], "coolwarm": 8, "coordin": [7, 14, 16], "coorel": 2, "copi": [0, 2, 3, 16], "core": 12, "corel": 23, "coronari": 9, "corr": [2, 7, 9, 13], "correalt": [13, 17], "correct": [0, 2, 3, 4, 5, 6, 7, 9, 15, 18, 20, 23], "correctli": [3, 4, 8, 9, 12], "correl": [2, 3, 5, 7, 8, 9, 12, 14, 15, 17, 20, 23], "correlation_matrix": [2, 7, 9, 13], "correspond": [2, 5, 7, 8, 10, 11, 13, 14, 17, 18, 20, 23], "cortex": 14, "cosin": [5, 8], "cost": [1, 2, 4, 5, 7, 8, 9, 10, 11, 14, 15, 23], "cost_deep_grad": 4, "cost_funct": 4, "cost_function_deep": 4, "cost_function_deep_grad": 4, "cost_function_grad": 4, "cost_grad": 4, "cost_sum": 4, "costol": 15, "could": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "coulomb": [2, 23], "count": [0, 2, 11, 19, 20, 21, 23], "counterpart": 23, "countor": 15, "coupl": [6, 7, 8], "cours": [0, 1, 2, 3, 5, 7, 13, 21], "coursework": 0, "courvil": [22, 23], "cov": [7, 8, 13, 18, 20, 23], "cov_xi": [7, 13], "cov_xx": [7, 13], "cov_yi": [7, 13], "covari": [2, 9, 17, 18, 23], "covariance_matrix": [7, 13, 16], "cover": [2, 7, 17, 21, 22], "covert": [2, 23], "covxi": 20, "covxx": 20, "covxz": 20, "covyi": 20, "covyz": 20, "covzz": 20, "cpu": 3, "craft": 5, "creat": [0, 3, 5, 6, 7, 11, 12, 13, 14, 17, 23], "create_biases_and_weight": 3, "create_convolutional_neural_network_kera": 5, "create_neural_network_kera": 3, "create_x": [7, 13], "credit": [2, 9, 21, 23], "crim": 2, "crime": 2, "criteria": [2, 6, 11, 12, 16, 20, 23], "criterion": [11, 12, 15], "critic": 8, "cross": [0, 2, 3, 5, 9, 11, 12, 15, 17, 20, 23], "cross_entropi": 6, "cross_val_scor": 8, "cross_valid": [9, 12], "crossvalid": 8, "crucial": [3, 20], "cs231": 5, "csr_matrix": [18, 23], "csv": [2, 6, 8, 9, 11], "ctnk": 3, "cubic": 2, "cumbersom": 7, "cumsum": [12, 13, 23], "cumul": [9, 12, 20], "cumulative_heads_ratio": 12, "cup": 7, "current": [0, 1, 3, 4, 5, 6, 15, 16, 22], "curs": 2, "curv": [8, 9, 12, 14], "curvatur": 15, "custom": [8, 16], "custom_cmap": [11, 12], "custom_cmap2": [11, 12], "cutpoint": 11, "cv": [8, 9, 12], "cvxbook": 15, "cvxopt": [7, 10], "cycl": [3, 14], "d": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "d2_g_t": 4, "d_f": 15, "d_g_t": 4, "d_net_out": 4, "da": 5, "dagger": [7, 18], "dai": [3, 11, 17], "damp": 5, "darget": 11, "darkr": 20, "dat": [2, 23], "dat_id": [2, 8, 9, 11, 23], "data": [1, 4, 6, 7, 10, 12, 14, 15, 16, 18, 22], "data1": 16, "data2": 16, "data3": 16, "data4": 16, "data_id": [2, 8, 9, 11, 23], "data_indic": 3, "data_panda": 23, "data_path": [2, 8, 9, 11, 23], "databas": 3, "datafil": [2, 8, 9, 11, 23], "datafram": [2, 6, 7, 9, 11, 13, 23], "datapoint": [1, 3, 7, 8, 9, 13, 15], "datasci": [0, 1], "dataset": [1, 2, 6, 8, 9, 10, 11, 12, 13, 15, 16, 23], "datatyp": 6, "date": [0, 23], "daughter": 12, "david": 22, "dbh": 3, "dbo": 3, "dcomposit": 18, "ddot": 4, "dead": 3, "deadlin": 0, "deal": [2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 20, 23], "dealt": 2, "debt": 9, "debug": [2, 7, 8], "decad": [2, 5], "decai": [2, 15, 20, 23], "decemb": [21, 23], "decent": 12, "decid": [2, 4, 5, 7, 8, 11], "decim": [2, 23], "decis": [2, 3, 10, 13, 17, 22, 23], "decision_funct": 10, "decision_tre": 11, "decisiontreeclassifi": [11, 12], "decisiontreeregressor": [2, 11, 12], "declar": [2, 6, 18, 23], "decompos": [7, 8, 18], "decomposit": [2, 8, 14, 23], "decompost": 7, "deconvolut": 5, "decorrel": [12, 15], "decreas": [3, 4, 6, 7, 8, 12, 13, 15], "deduc": [2, 23], "deep": [5, 9, 14, 15, 17, 22], "deep_neural_network": 4, "deep_param": 4, "deep_tree_clf": [11, 12], "deep_tree_clf1": 11, "deep_tree_clf2": 11, "deepen": [7, 17, 23], "deeper": [2, 5, 6, 23], "deeplearningbook": [22, 23], "deer": 5, "def": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 20, 23], "def_covari": 20, "default": [2, 3, 4, 6, 8, 9, 18, 23], "default_tim": 6, "defect": 7, "defici": 7, "defin": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "definit": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "defint": 20, "degre": [0, 1, 5, 7, 8, 10, 11, 12, 13, 20, 23], "del": 3, "delet": [0, 8], "delimit": 6, "deliv": [0, 19, 23], "delta": [2, 4, 5, 8, 10, 14, 15, 16, 23], "delta_": [3, 18], "delta_0": 5, "delta_1": 5, "delta_2": 5, "delta_3": 5, "delta_4": 5, "delta_5": 5, "delta_h": [2, 3, 23], "delta_j": [5, 14], "delta_k": 14, "delta_l": [3, 5], "delta_momentum": 15, "delta_n": [2, 5, 23], "delug": 17, "delv": 2, "demand": 15, "demonstr": [2, 5, 7, 8, 9, 13, 14, 17, 23], "den": 6, "denomin": [3, 7], "denot": [3, 4, 8, 9, 15, 20], "dens": [3, 5, 6], "densiti": [2, 4, 8, 20], "depart": [21, 23], "depend": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 23], "depict": 20, "deploy": [2, 17, 23], "depth": [2, 5, 11, 12, 18], "deriv": [2, 3, 4, 8, 9, 10, 12, 13, 15, 17, 23], "derivati": 15, "derivative_fn": 15, "descend": [7, 11, 13], "descent": [2, 3, 5, 9, 10, 14, 23], "describ": [2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 18, 23], "descript": [2, 10, 11, 23], "design": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 23], "designmatrix": [2, 23], "desir": [2, 4, 6, 7, 15, 16, 23], "desktop": 0, "despit": [3, 14], "destroi": 18, "det": [7, 18], "detail": [2, 8, 13, 15, 16, 18], "detect": [5, 10, 14], "determin": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "determinist": [9, 15, 20], "dev": 3, "develop": [2, 5, 7, 10, 12, 13, 14, 17, 18, 23], "deviat": [2, 3, 4, 6, 7, 8, 20, 23], "devis": 14, "df": [6, 10, 13, 15, 23], "df1": 23, "di": 2, "diag": [7, 10], "diagnost": [3, 12], "diagon": [2, 7, 9, 15, 18, 20, 23], "diagonaliz": 7, "diagram": 12, "diagsvd": 8, "dice": [8, 20], "dict": [8, 10], "dictionari": 2, "did": [1, 2, 3, 7, 8, 9, 12, 13, 16, 23], "die": 3, "diff": 4, "diff1": 4, "diff2": 4, "diff_ag": 4, "diffeent": 10, "differ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "differenti": [1, 2, 5, 17, 18, 23], "difficult": [2, 3, 8, 12, 15, 20, 23], "difficulti": [2, 3, 15, 23], "diffonedim": 4, "digit": [2, 3, 5, 6, 8, 21, 23], "dilemma": 15, "dilut": 3, "dim": [6, 13, 16, 18], "dimens": [1, 2, 3, 4, 5, 6, 7, 10, 13, 16, 18, 23], "dimension": [2, 6, 7, 8, 11, 13, 15, 16, 17, 18, 23], "dimensionless": [2, 5, 23], "diment": 18, "dimnsion": 6, "diod": 5, "direct": [2, 3, 4, 6, 13, 14, 15, 16, 23], "directli": [3, 6, 7, 8, 20], "disadvantag": [2, 23], "disappear": [5, 8], "disc_loss": 6, "disc_tap": 6, "discard": [8, 13], "disciplin": [2, 5, 14], "disclaim": 20, "discord": 23, "discourag": [0, 15], "discov": [2, 23], "discover": 7, "discret": [3, 5, 7, 9, 15], "discrimin": [6, 9, 12, 13], "discriminator_loss": 6, "discriminator_loss_list": 6, "discriminator_model": 6, "discriminator_optim": 6, "discuss": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "diseas": 9, "disguis": 8, "disord": [3, 9], "displai": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "displaystyl": [2, 7, 23], "disregard": [2, 23], "dissimilar": [13, 16], "dist": 16, "distanc": [2, 10, 11, 13, 16, 20], "distance_list": 11, "distinct": [5, 9, 10, 11, 12, 16], "distinctli": 10, "distinguish": [2, 6, 9, 10, 20, 23], "distplot": 2, "distribut": [2, 3, 6, 8, 9, 12, 13, 15, 16, 17, 18, 23], "distrubut": [2, 17, 23], "dive": [2, 10, 18, 23], "diverg": [3, 15], "divid": [2, 3, 5, 7, 8, 9, 10, 11, 13, 14, 20, 23], "divis": [8, 10, 11, 15, 18, 20], "dna": 9, "dnn": [2, 3, 4, 6, 14, 23], "dnn1": 6, "dnn2_gru2": 6, "dnn_kera": 3, "dnn_model": 3, "dnn_numpi": 3, "dnn_scikit": [2, 3, 23], "do": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "doc": [0, 1, 2, 17, 19, 21, 22, 23], "document": [0, 6, 15], "doe": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20, 23], "doesn": [5, 11, 14, 23], "dog": [3, 5, 6], "domain": [7, 10, 15], "domin": [2, 23], "don": [0, 1, 2, 3, 5, 7, 8, 10, 13, 15, 17, 23], "done": [1, 2, 4, 5, 6, 7, 8, 11, 12, 13, 15, 18, 23], "dot": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "doubl": [1, 5, 6, 18, 23], "doubli": 3, "down": [2, 5, 8, 11, 13, 14, 15], "download": [0, 2, 3, 5, 7, 8, 18, 22, 23], "downsampl": 5, "dozen": 3, "dq": 8, "drag": 15, "dramat": 13, "drastic": 6, "draw": [6, 8, 12, 15], "drawback": [2, 3, 5, 15], "drawn": [3, 6, 8, 9, 13, 20, 23], "drive": [5, 6], "driven": 5, "drop": [2, 3, 7, 8, 13, 15, 20, 23], "dropna": [2, 8, 23], "dropout": 6, "dt": [4, 5, 15, 20], "dtype": [2, 3, 5, 6, 16, 18, 23], "dub": [2, 23], "due": [3, 4, 7, 8, 10, 12, 14, 15, 21, 23], "dummi": 2, "dure": [2, 3, 5, 6, 10, 11, 13, 17, 23], "dwell": 2, "dwh": 3, "dwo": 3, "dx": [4, 5, 10, 20], "dx_1": 20, "dx_1p": 8, "dx_2p": 8, "dx_mp": 8, "dx_n": 20, "dxp": 8, "dy": [3, 10, 20], "dynam": 6, "dz": 10, "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 21, 23], "e_": [2, 4, 23], "each": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "eapprox": [2, 23], "earli": [3, 15], "earlier": [2, 7, 9, 10, 11, 13, 14, 15, 23], "earthexplor": 8, "eas": [8, 11, 16], "easi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "easier": [0, 7, 8, 10, 11, 15, 20, 23], "easiest": 15, "easili": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "eastern": [21, 23], "ebind": [2, 23], "eblock": 11, "econometr": 23, "economi": 7, "ecosystem": [17, 23], "ect": 19, "edg": 5, "edgecolor": 8, "edu": 15, "educ": [2, 23], "eff": 20, "effect": [1, 3, 6, 12, 15, 20], "effic": 3, "effici": [2, 5, 12, 15, 17, 18, 20, 23], "efron": 8, "egrad": 15, "eig": [7, 13, 15, 18, 20, 23], "eigen": 20, "eigenpair": [7, 13], "eigenvalu": [2, 7, 10, 13, 15, 18, 23], "eigenvector": [7, 13, 15], "eight": [18, 23], "eigval": [18, 20, 23], "eigvalu": [13, 15], "eigvec": [18, 20, 23], "eigvector": [13, 15], "eir": [21, 23], "eispack": [18, 23], "either": [3, 7, 8, 9, 10, 11, 12, 13, 15, 20, 23], "eivind": 21, "eivinsto": 21, "ekstr\u00f8m": 6, "elabor": 20, "elarn": 5, "electr": [2, 5, 14, 23], "electron": 23, "eleg": 13, "element": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 22], "elementari": [12, 15, 18], "elementwis": [5, 15], "elementwise_grad": [4, 15], "elessar": 23, "elif": 16, "elim": 18, "elimin": [5, 10], "elin": [21, 23], "els": [1, 3, 5, 6, 9, 11, 14, 15, 18], "elu": 3, "elus": [2, 23], "email": [19, 21, 23], "embed": [2, 13], "embodi": 8, "emit": 20, "emner": 22, "emphas": [2, 12, 17, 23], "emphasi": [2, 17, 22, 23], "empir": [3, 13, 20], "emploi": [2, 3, 7, 8, 13, 15, 20, 23], "employ": 2, "empti": [0, 8, 12], "emul": 14, "en": [17, 22], "enabl": 13, "enbodi": 8, "encod": [2, 5, 7, 11, 13, 16, 23], "encompass": [2, 20], "encount": [0, 2, 3, 7, 9, 15, 20, 23], "encourag": 0, "end": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "endpoint": [5, 8], "energi": [2, 6, 8], "enforc": 14, "eng": 22, "engin": [2, 3, 5, 6, 17, 23], "enorm": 5, "enough": [2, 8, 15, 23], "ensembl": [3, 11, 23], "ensur": [2, 3, 4, 5, 7, 8, 13, 15, 20], "entail": 23, "enter": [7, 8], "enthought": [2, 17, 23], "entir": [3, 5, 9, 11, 17, 20, 23], "entiti": [11, 14, 18, 23], "entri": [2, 7, 10, 13, 14, 18, 23], "entropi": [3, 5, 9, 12, 15, 23], "enumer": [2, 3, 4, 5, 6, 8, 10, 23], "env": [20, 23], "environ": [4, 17, 23], "environemnt": 0, "eo": [2, 8], "eol": 2, "eosfit": 2, "epoch": [2, 3, 5, 6, 14, 15, 23], "epsilon": [2, 7, 8, 9, 15, 23], "epsilon_": [2, 23], "epsilon_0": [2, 23], "epsilon_1": [2, 23], "epsilon_2": [2, 23], "epsilon_i": [2, 23], "eq": [5, 15, 16, 18, 20], "eqnarrai": [5, 7, 8], "equal": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "equat": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "equilibrium": [4, 14], "equiv": [5, 15, 18, 20], "equival": [2, 3, 7, 9, 10, 13, 15, 17, 18, 23], "erf": 20, "eriador": 23, "err": [2, 12], "err_": 8, "err_sqr": 4, "errat": 15, "erron": 4, "error": [0, 1, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20], "error_estimate_corr_tim": 20, "error_hidden": 3, "error_output": 3, "escap": 15, "especi": [0, 3, 5, 11, 14, 15], "essenti": [0, 2, 7, 8, 11, 12, 14, 16, 20], "establish": [1, 2, 8, 12, 13], "estim": [2, 3, 7, 8, 9, 12, 13, 15, 17, 20, 23], "estimated_mse_fold": 8, "estimated_mse_kfold": 8, "estimated_mse_sklearn": 8, "et": [1, 2, 4, 6, 22, 23], "eta": [2, 3, 5, 10, 14, 15, 23], "eta0": [10, 15], "eta_": 15, "eta_t": 15, "eta_v": [2, 3, 5, 23], "etc": [2, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20], "ethic": 17, "euclidean": [2, 16], "evalu": [0, 1, 2, 4, 5, 6, 7, 8, 11, 15, 20, 23], "evalut": 15, "even": [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "evenli": 6, "event": [7, 9, 12, 20], "eventu": [2, 7, 8, 13, 14, 15, 21], "everi": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23], "everyth": [1, 6, 14], "everywher": [6, 15], "evolv": 2, "exact": [2, 7, 13, 14, 15, 18, 20, 23], "exactli": [2, 5, 6, 8, 14, 17], "exam": 23, "examin": 8, "exampl": [0, 1, 7, 13, 14, 15, 17, 18, 20, 22], "exce": [3, 14, 15], "excel": [2, 3, 6, 7, 12, 23], "except": [5, 6, 8, 10, 11, 18], "excess": [2, 23], "excit": 2, "exclud": [3, 8, 14], "exclus": [2, 3, 5, 8, 20, 23], "execut": [0, 4, 7, 15], "exemplifi": 15, "exercic": [21, 23], "exercis": [7, 17, 19, 21, 23], "exhaust": 8, "exhibit": [2, 7, 8, 10, 23], "exist": [2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 18, 23], "exit": [7, 18], "exp": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 20], "exp_term": 3, "expand": [7, 9, 13, 15], "expans": [2, 5, 7, 10, 12, 14, 15, 23], "expect": [0, 2, 3, 7, 8, 9, 13, 14, 15, 17, 23], "expectation_value_of_h_wrt_p": 20, "expens": [1, 8, 12, 15], "experi": [0, 2, 3, 8, 10, 15, 17, 23], "experiment": [2, 6, 8, 11, 20, 23], "expert": [3, 11], "explain": [1, 2, 8, 11, 12, 13, 15, 23], "explained_variance_ratio_": 13, "explanatori": [2, 23], "explicit": [2, 5, 8, 15, 18, 23], "explicitli": [2, 6], "explod": 3, "exploit": [2, 5, 14, 15, 23], "explor": [3, 6, 8, 10, 15, 17, 23], "expon": 3, "exponenti": [2, 3, 7, 8, 12, 15, 20, 23], "export": [0, 1, 11], "export_graphviz": 11, "export_text": 11, "exporttext": 11, "expos": 17, "express": [2, 4, 5, 7, 8, 9, 12, 14, 15, 18, 20, 23], "exptmean": 20, "exptvari": 20, "extend": [2, 4, 9, 13, 15, 17, 23], "extens": [0, 2, 14, 17, 23], "extent": [2, 3, 8, 22], "extern": [5, 8, 11], "extra": [0, 3, 5, 7, 21, 23], "extract": [1, 2, 5, 7, 8, 9, 10, 13, 15, 18, 23], "extrapol": [2, 23], "extrem": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 18], "extremum": 15, "extrins": 13, "ey": [2, 7, 8, 15, 16, 18, 23], "f": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 23], "f1": 15, "f11": [2, 23], "f12": [2, 23], "f13": [2, 23], "f1_grad": 15, "f1d": 15, "f2": 15, "f2_grad_x1": 15, "f2_grad_x1_analyt": 15, "f2_grad_x2": 15, "f2_grad_x2_analyt": 15, "f3": 15, "f3_grad": 15, "f3_grad_analyt": 15, "f4": 15, "f4_grad": 15, "f4_grad_analyt": 15, "f5": 15, "f5_grad": 15, "f6": 15, "f6_for": 15, "f6_for_grad": 15, "f6_grad_analyt": 15, "f6_while": 15, "f6_while_grad": 15, "f7": 15, "f7_grad": 15, "f7_grad_analyt": 15, "f8": 15, "f8_grad": 15, "f9": [2, 15, 23], "f9_altern": 15, "f9_alternative_grad": 15, "f9_grad": 15, "f_": 12, "f_0": [5, 12], "f_1": [12, 15], "f_2": [14, 15], "f_3": 14, "f_d": 20, "f_grad": 15, "f_grad_analyt": 15, "f_i": [1, 2, 8, 14], "f_m": [5, 12], "f_n": 5, "f_vec": 4, "face": [15, 23], "facecolor": [8, 10, 20], "facil": [2, 17], "facilit": 14, "fact": [2, 3, 5, 7, 11, 13, 14, 15, 23], "factor": [2, 3, 5, 7, 8, 11, 12, 13, 15, 18, 20, 23], "factori": 15, "fade": 8, "fafab0": [11, 12], "fail": [2, 8, 15, 21, 23], "failur": 9, "fairli": [3, 4, 20], "faisal": 1, "fake": 6, "fake_loss": 6, "fake_output": 6, "fall": [10, 11, 19], "fals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 16, 18, 23], "famili": [2, 9, 10, 20], "familiar": [0, 2, 5, 7, 8, 10, 17, 18, 20, 23], "famou": [8, 14], "far": [1, 2, 5, 6, 7, 8, 10, 13, 14, 15, 16, 23], "fashion": [2, 11, 12, 23], "fast": [3, 5, 8, 12, 14, 15, 17, 20, 23], "faster": [3, 13, 15], "fastest": [15, 18], "favor": 9, "favorit": 20, "fc": 5, "featur": [0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "feature_nam": [2, 3, 9, 11], "feautur": 11, "fed": 3, "feed": [2, 4, 5, 13, 17, 23], "feed_forward": 3, "feed_forward_out": 3, "feed_forward_train": 3, "feedback": [6, 23], "feeddorward": 6, "feedforward": [3, 6, 14], "feel": [0, 1, 2, 7, 8, 13, 15, 17, 21, 23], "feet": 2, "fetch": [0, 8], "few": [3, 5, 6, 7, 11, 20, 23], "fewer": [2, 11, 13, 23], "ffnn": [3, 14], "field": [2, 5, 8, 14, 17], "fifth": [2, 8, 23], "fig": [2, 3, 4, 5, 6, 8, 9, 14, 15, 16, 23], "fig_id": [2, 8, 9, 11, 23], "figaxi": 20, "figsiz": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "figur": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 23], "figure_id": [2, 8, 9, 11, 23], "figurefil": [2, 8, 9, 11, 23], "file": [0, 2, 6, 7, 8, 9, 11, 23], "file_prefix": 6, "filenam": 23, "fill": [7, 11], "filter": [5, 6], "final": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 23], "financ": 2, "find": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "fine": [2, 16], "finish": 4, "finit": [5, 7, 8, 14, 15, 20], "finnicki": 0, "first": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 22], "firsteigvector": 13, "fit": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20], "fit_beta": 8, "fit_intercept": [1, 2, 7, 8], "fit_mod": 11, "fit_transform": [0, 2, 8, 10, 11, 13], "fiti": [2, 23], "five": [2, 11, 23], "fix": [2, 5, 6, 8, 12, 13, 14, 15, 23], "flag": 6, "flat": [14, 15], "flatten": [3, 5, 6, 7, 18], "flexibl": [3, 8, 10, 12, 14, 23], "flip": [21, 23], "float": [2, 5, 6, 7, 11, 13, 15, 16, 18, 23], "float32": [6, 11], "float64": [6, 18, 23], "flop": [7, 18], "flow": [3, 6, 14], "fluctuat": 7, "fly": 13, "fm": 2, "fmax": 5, "fmesh": 15, "fn": 9, "focu": [0, 2, 5, 6, 7, 8, 17, 22, 23], "focus": [3, 8, 9, 18], "fold": [8, 11], "folder": [0, 2, 6, 8, 23], "follow": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "font": [2, 9, 20, 23], "fontdict": 20, "fontsiz": [3, 8, 10, 11, 12, 20], "fontweight": 3, "footprint": 5, "foral": 10, "forc": [2, 7, 8, 12, 13], "forcast": 6, "forecast": [6, 14], "forest": [2, 3, 11, 17, 23], "forget": 13, "form": [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "formal": [5, 6, 16, 20], "format": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 17, 20, 22], "format_data": 6, "formatstrformatt": [8, 15], "formul": [6, 8, 13, 16], "formula": [5, 15, 20], "forth": [6, 14], "fortran": [2, 17, 18, 23], "fortran2003": [17, 23], "fortran90": 20, "fortun": [2, 13], "forward": [2, 5, 8, 17, 18, 23], "found": [3, 4, 6, 7, 8, 14, 15, 23], "foundat": [17, 23], "four": [6, 7, 8, 10, 14, 18, 19, 21, 23], "fourier": [2, 23], "fourierdef1": 5, "fourierdef2": 5, "fourierseriessign": 5, "fourth": [14, 23], "fp": 9, "frac": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "fraction": 11, "frame": 9, "framework": [3, 10, 12, 20], "frank": [7, 13], "frankefunct": [7, 8, 13], "fredli": [21, 23], "free": [0, 1, 2, 8, 13, 15, 17, 18, 20, 21, 22, 23], "freecodecamp": 17, "freedom": 7, "freeli": 2, "freez": 0, "frequenc": [5, 8, 9, 20], "frequent": [2, 10, 11, 15], "frequentist": 17, "fresh": 12, "fridai": [0, 21, 23], "friedman": [8, 22, 23], "friendli": 6, "frodo": 23, "frog": 5, "from": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 22], "from_cod": 11, "from_logit": [5, 6], "from_tensor_slic": 6, "front": [2, 6, 7, 23], "frustrat": 0, "fulfil": [4, 7, 14], "full": [2, 3, 5, 7, 9, 11, 12, 15, 20, 23], "full_matric": 7, "fulli": [5, 8, 14, 20], "fun": [17, 23], "func": 4, "function": [0, 1, 4, 5, 6, 7, 11, 16, 17, 18], "functionali": 13, "fundament": [2, 8, 17, 23], "funtion": 4, "further": [4, 9, 11, 23], "furthermor": [2, 5, 7, 8, 9, 13, 14, 15, 17, 23], "futur": [2, 6, 10, 11, 23], "fy": [0, 19, 21, 22, 23], "fys5419": [22, 23], "fys5429": [22, 23], "f\u00f8470": [21, 23], "g": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 20, 23], "g0": 4, "g_": [4, 11, 12], "g_0": 4, "g_1": [4, 12], "g_2": [4, 12], "g_analyt": 4, "g_dnn_ag": 4, "g_euler": 4, "g_i": 4, "g_m": [5, 12], "g_n": 5, "g_re": 4, "g_t": 4, "g_t_d2t": 4, "g_t_d2x": 4, "g_t_dt": 4, "g_t_hessian": 4, "g_t_hessian_func": 4, "g_t_jacobian": 4, "g_t_jacobian_func": 4, "g_trial": 4, "g_trial_deep": 4, "g_vec": 4, "gain": [3, 7, 9, 11, 12, 15], "galleri": [2, 23], "game": 6, "gamge": 23, "gamma": [2, 4, 10, 11, 12, 13, 15, 23], "gamma1": 10, "gamma2": 10, "gamma_": [2, 23], "gamma_0": 12, "gamma_1": 12, "gamma_1x": 12, "gamma_i": [2, 10, 20, 23], "gamma_j": 15, "gamma_k": 15, "gamma_m": 12, "gamma_x": [2, 23], "gap": 10, "gate": [6, 14], "gather": [2, 3, 14], "gaug": 14, "gaussbacksub": 18, "gaussian": [6, 7, 8, 10, 16, 20, 23], "gaussian_point": 16, "gaussian_rbf": 10, "gave": 15, "gavra": 23, "gbc": 23, "gca": [4, 8, 10, 15], "gd": 3, "gd_clf": 12, "gdclassiffiercgain": 12, "gdclassiffierconfus": 12, "gdclassiffierroc": 12, "gdm": 15, "gdregress": 12, "ge": [3, 7, 9, 20], "gen_loss": 6, "gen_tap": 6, "gender": [2, 23], "genener": 6, "gener": [0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22], "generaliz": 1, "generallay": 14, "generate_and_save_imag": 6, "generate_imag": 6, "generate_latent_point": 6, "generate_simple_clustering_dataset": 16, "generated_imag": 6, "generator_loss": 6, "generator_loss_list": 6, "generator_model": 6, "generator_optim": 6, "genom": 17, "geodes": 13, "geometr": [2, 15, 23], "geometri": 7, "georg": 22, "geotif": 8, "geq": [4, 7, 10, 11, 15], "geron": [2, 22, 23], "get": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20, 21, 23], "get_dummi": 11, "get_paramet": 4, "get_split": 11, "get_yaxi": 10, "get_yticklabel": 8, "gh": 0, "gibb": [17, 23], "gif": 6, "gini": 12, "gini_index": 11, "ginvers": 15, "git": [0, 2, 17, 23], "giter": 15, "github": [2, 17, 19, 21, 22, 23], "gitignor": 0, "gitlab": [2, 17, 23], "give": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 23], "given": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "global": [8, 9, 15], "glorot": 3, "gnew": 15, "go": [0, 1, 2, 3, 5, 7, 8, 10, 11, 13, 14, 15, 23], "goal": [2, 9, 11, 23], "goe": [0, 2, 3, 4, 7, 8, 15, 16, 18, 23], "golden": 15, "gone": 7, "gong": 3, "good": [0, 3, 5, 6, 7, 8, 11, 12, 13, 15, 17, 20, 22], "goodfellow": [6, 22, 23], "googl": [3, 6, 17, 23], "got": [3, 8], "gotten": 23, "gov": 8, "govern": 23, "gp": 22, "gpu": [3, 15, 17, 23], "grad": [4, 15], "grad_analyt": 15, "grade": 19, "gradient": [2, 5, 6, 9, 10, 11, 14, 17, 23], "gradientboostingclassifi": 12, "gradientboostingregressor": 12, "gradients_of_discrimin": 6, "gradients_of_gener": 6, "gradienttap": 6, "gradual": [3, 16], "grai": [6, 8], "graph": [1, 3, 11, 13, 14, 15], "graph_from_dot_data": 11, "graphic": [2, 3, 11, 23], "grasp": 2, "gray_r": [3, 5], "grayscal": 5, "great": [0, 7, 15], "greater": [3, 9, 20], "greatli": 15, "greedi": 11, "green": [2, 5, 11, 20], "grei": 6, "grid": [3, 5, 8, 9, 10, 14, 20], "grossli": 15, "ground": [2, 23], "group": [0, 2, 8, 9, 11, 16, 17, 19, 21, 23], "groupbi": [2, 23], "grow": [3, 5, 11, 12], "growth": [2, 23], "gru": 6, "guarante": [2, 6, 15, 20, 23], "guess": [3, 6, 12, 15, 16], "guestrin": 12, "guid": 3, "h": [0, 2, 3, 7, 8, 10, 15, 20, 21, 22, 23], "h1": 4, "h_": [2, 15, 23], "h_1": [4, 15], "h_2": [4, 15], "h_m": 12, "ha": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "haanen": [21, 23], "habit": 2, "had": [2, 3, 8, 9, 15, 23], "hadamard": [3, 14, 15], "half": [3, 10, 11], "halv": 12, "hand": [2, 3, 4, 5, 7, 13, 14, 15, 17, 18, 20, 21, 22, 23], "handi": 5, "handl": [0, 2, 3, 4, 7, 11, 13, 17], "handle_unknown": 11, "handsid": 14, "handwrit": 14, "handwritten": [3, 7], "happen": [3, 4, 5, 6, 7, 8, 12, 15, 20], "hard": [3, 9, 10, 12, 15], "hardcopi": [17, 23], "harder": [2, 3], "harmon": 5, "hasn": 23, "hassl": [2, 17, 23], "hast": [17, 23], "hasti": [1, 2, 8, 22, 23], "hat": [1, 2, 3, 7, 8, 9, 11, 12, 13, 14, 15, 18], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "haven": 3, "he": [9, 23], "head": [2, 6, 12, 20], "header": [2, 23], "heads_proba": 12, "health": 2, "hear": [2, 15, 23], "heart": [2, 9, 23], "heatmap": [2, 3, 5, 9, 23], "heavili": 2, "heavisid": 3, "height": [3, 5, 8], "held": 15, "help": [0, 1, 2, 3, 6, 14, 15, 23], "helper": [6, 16], "henc": [2, 7, 8, 10, 11, 12, 14, 15, 23], "henrik": [21, 23], "her": 9, "here": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hereaft": [2, 10, 14, 23], "hermitian": 18, "hessenberg": 18, "hessian": [2, 4, 7, 15], "heterogen": [11, 12], "hi": 9, "hidden": [3, 5, 6, 14], "hidden_bia": 3, "hidden_bias_gradi": 3, "hidden_layer_s": [2, 3, 23], "hidden_neuron": 6, "hidden_weight": 3, "hidden_weights_gradi": 3, "hierarch": 7, "high": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 23], "higher": [2, 3, 5, 7, 8, 10, 15, 23], "highest": [3, 4], "highli": [2, 5, 6, 12, 17, 18, 22, 23], "highwai": 2, "hing": 10, "hint": [0, 1, 15], "hip": 17, "hire": 2, "hist": [6, 8, 9, 20], "histogram": [2, 8, 9, 20], "histor": [9, 13], "histori": [0, 5, 6, 14], "hitherto": 7, "hjorth": [21, 23], "hobbi": 20, "hoc": 7, "hoff": 22, "hold": [3, 5, 8, 15, 16], "holder": [2, 23], "home": 2, "homepag": 23, "homework": [8, 15], "homogen": [3, 5, 11, 12, 15], "honchar": 4, "hopefulli": [0, 2, 13, 20, 23], "horizont": 13, "horlyk": [21, 23], "hors": [5, 9, 23], "hot": [3, 11], "hour": [3, 17, 19, 20, 21, 23], "how": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "howev": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hspace": [2, 6, 10, 12, 20, 23], "hstack": 3, "htf": 23, "html": [1, 2, 17, 19, 21, 22, 23], "http": [0, 1, 2, 5, 6, 8, 15, 17, 18, 19, 21, 22, 23], "huang": [2, 23], "huber": [2, 23], "huge": [3, 5, 6, 17], "human": [2, 3, 5, 8, 11, 14], "humid": 11, "hundr": 3, "hungri": 3, "hybrid": 19, "hydrogen": [2, 23], "hyperbol": [3, 6, 14], "hyperparam": 10, "hyperparamet": [5, 6, 7, 8, 11, 15], "hyperplan": 13, "h\u00f8rlyk": [21, 23], "i": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "i0": [2, 23], "i1": [2, 8, 10, 14, 23], "i2": [2, 10, 14, 23], "i3": [2, 14, 23], "i5": [2, 23], "i_": 15, "i_1": [7, 8], "i_2": [7, 8], "ian": 22, "ic": 3, "id": [9, 15], "ida": [21, 23], "idea": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 18], "ideal": [2, 4, 8, 10, 15, 20, 23], "idem": 8, "ident": [7, 8, 14, 15, 18], "identifi": [2, 3, 9, 11, 13, 14, 15, 16, 23], "ieor": 20, "ifi": 22, "ifs": [17, 23], "ignor": [0, 2, 3, 5, 11], "ii": [18, 20], "iii": [18, 23], "ij": [1, 2, 3, 5, 8, 10, 14, 16, 18, 20, 23], "ik": [2, 18, 23], "illustr": [7, 9, 12, 14, 15, 16, 17, 23], "im": 8, "imag": [3, 5, 6, 8, 11, 13, 14, 16, 22, 23], "image_at_epoch_": 6, "image_batch": 6, "image_height": 5, "image_path": [2, 8, 9, 11, 23], "image_width": 5, "imageio": 8, "images_from_seed_imag": 6, "imagin": 3, "immedi": [2, 5, 6, 8, 17, 23], "implement": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 20, 23], "impli": [5, 7, 8, 9, 15, 18], "implicit": 5, "implicitli": [13, 20], "import": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "importantli": 5, "impos": [2, 8, 13, 14, 23], "imposs": [2, 7, 23], "impress": [2, 14, 23], "improv": [0, 2, 6, 7, 11, 12, 13, 15], "impur": 11, "imread": 8, "imshow": [3, 5, 6, 8], "in3050": [22, 23], "in3310": 23, "in4080": [22, 23], "in4300": [22, 23], "in4310": 22, "in5400": 5, "in5550": 22, "in_out_neuron": 6, "inaccur": 15, "inact": 14, "inadequ": [2, 23], "inch": 8, "includ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 17, 20, 21, 22, 23], "include_bia": [8, 11], "incom": [1, 14], "incorrect": 3, "incoveni": 10, "increas": [2, 3, 5, 6, 7, 8, 11, 14, 15, 20, 23], "increasingli": 20, "ind": 8, "inde": [2, 4, 6, 7, 8, 15, 23], "indefinit": 6, "independ": [2, 7, 8, 9, 10, 14, 15, 20, 23], "index": [2, 3, 5, 6, 12, 16, 17, 18, 20, 22, 23], "index_col": [2, 23], "indic": [1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 23], "indispens": 8, "individu": [3, 8, 9, 12, 14, 20, 23], "indu": 2, "indx": 18, "indx1": 4, "indx2": 4, "indx3": 4, "ineffici": [5, 15], "inequ": [10, 15], "inertia": 15, "inf1000": [17, 23], "inf1100": [17, 23], "inf1100l": [17, 23], "inf1110": [17, 23], "inf3000": 23, "infeas": 11, "infer": [2, 3, 6, 8, 22, 23], "inferenc": 3, "infil": [2, 8, 9, 11, 23], "infin": [7, 8, 9, 13], "infinit": 5, "infinitesim": 20, "influenc": [8, 12], "influenti": 3, "info": 23, "inform": [2, 3, 5, 6, 8, 11, 13, 14, 15, 16, 18, 22, 23], "inforom": 0, "infti": [5, 8, 15, 20], "ingeni": 15, "ingredi": [2, 11, 23], "inher": 8, "inherit": [18, 23], "initi": [2, 3, 4, 8, 12, 15, 16, 18, 20, 23], "inject": 16, "inlin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "inner": [2, 15], "inp": 6, "inplac": 15, "input": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 20, 23], "input_dim": 3, "input_shap": [5, 6], "inputs": 3, "inputs_shuffl": [2, 3], "insert": [5, 7, 8, 10, 12, 20], "insid": [2, 6, 9], "insight": [2, 3, 7, 17, 23], "insist": [8, 15], "inspir": [2, 3, 14, 23], "instabl": 4, "instal": [0, 2, 3, 7, 8, 11], "instanc": [1, 2, 3, 4, 6, 8, 11, 13, 15, 23], "instanti": 12, "instead": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 20, 23], "institut": 3, "instruct": [2, 3], "int": [2, 3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 20], "int32": 12, "int_": [5, 8, 20], "int_0": 20, "int_a": 20, "intak": 2, "integ": [3, 4, 15, 16, 18, 20, 23], "integer_vector": 3, "integr": [5, 8, 20, 23], "intellig": [2, 16, 22, 23], "intend": 12, "intens": 3, "intention": 16, "interact": [2, 8, 11, 14, 17, 23], "intercept": [1, 2, 8, 10, 13, 15, 23], "intercept_": [2, 8, 10, 11, 15, 23], "interchang": [7, 14, 18], "interconnect": 3, "interest": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 20, 23], "interfac": [2, 3, 18], "interior": [2, 11, 23], "intermedi": 18, "intern": [3, 12, 14], "interpol": [3, 5, 6, 8, 14], "interpr": 7, "interpret": [0, 1, 2, 3, 8, 11, 12, 14, 15, 18, 20], "interv": [2, 5, 7, 8, 9, 15, 20, 23], "intial": 15, "intract": [2, 6], "intrins": [5, 13, 18, 20, 23], "intro": [17, 22, 23], "introduc": [2, 3, 7, 8, 10, 12, 14, 18, 20, 23], "introduct": [3, 4, 6, 15, 22], "introductori": [2, 6, 18, 22, 23], "intuit": [2, 7, 8, 10, 14, 15, 23], "inv": [2, 7, 15, 23], "invalu": [2, 15, 17, 23], "invari": 3, "invd": 7, "inver": 10, "invers": [2, 5, 8, 15, 23], "inverse_transform": 10, "invert": [1, 2, 7, 9, 12, 15, 23], "invh": 15, "invok": [2, 10], "involv": [2, 4, 8, 9, 13, 14, 23], "io": [2, 17, 19, 21, 22, 23], "ip": [2, 10, 20, 23], "ipca": 13, "ipynb": [17, 23], "ipython": [2, 7, 9, 11, 13, 16, 17, 23], "iq": 8, "iri": [10, 11], "irreduc": 8, "irrelev": 7, "irrespect": [2, 23], "isn": 7, "isnul": 2, "isomap": 13, "issu": [0, 3, 11, 18], "it_arrai": 15, "item": [2, 15, 23], "items": [18, 23], "iter": [3, 4, 6, 8, 10, 15, 16, 20, 23], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "itself": [7, 8, 14, 20, 23], "j": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 22, 23], "j1": 18, "j_": 8, "j_lasso_sk": 8, "j_ridge_sk": 8, "j_sk": 8, "jackknif": [8, 17, 23], "jacobian": [4, 15], "jason": 6, "jax": [17, 23], "jensen": [21, 23], "jerom": 22, "ji": [14, 18], "jit": 15, "jj": [2, 7, 8, 23], "jk": [2, 3, 8, 14, 18, 23], "jl": [2, 23], "jm": 18, "jnp": 15, "job": [0, 4, 10, 12], "join": [2, 6, 8, 9, 11, 23], "joint": [6, 7], "judg": 15, "judgement": 8, "julia": [17, 18], "jump": 20, "junk": 6, "jupit": 23, "jupyt": [0, 1, 2, 17, 22, 23], "just": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "justif": 2, "justifi": [5, 12], "k": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "k0": 9, "k1": 9, "kaggl": 8, "kappa_d": 20, "karl": [21, 23], "karush": 10, "katrin": [21, 23], "keep": [0, 2, 3, 6, 7, 8, 13, 15, 16, 18, 23], "keepdim": [3, 8, 12, 18], "kei": [2, 3, 5, 8, 14], "kept": [6, 8, 16], "kera": [2, 6, 17, 23], "kernel": [2, 3, 5, 17, 23], "kernel_regular": [3, 5], "kernel_s": 6, "kernelpca": 13, "kev": [2, 23], "kevin": [22, 23], "keyword": [18, 23], "kfold": 8, "kg": 3, "ki": 18, "kick": [3, 15], "kiener": 4, "kilomet": 8, "kind": [2, 4, 5, 6, 10, 14, 15, 16, 23], "kj": [8, 14, 18], "kjm": [17, 23], "kkt": 10, "kl": 20, "km": [14, 23], "kmean": 16, "kmeanspoint": 16, "kn_k": 16, "know": [0, 1, 2, 3, 4, 7, 8, 10, 15, 17, 23], "knowledg": [2, 17, 23], "known": [3, 5, 6, 7, 8, 9, 10, 11, 14, 18, 20, 22], "kondev": [2, 23], "kp": 20, "kpca": 13, "kroneck": 16, "kuhn": 10, "kvalsund": [21, 23], "kwown": [2, 23], "l": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "l0": 9, "l1": [2, 3, 5, 9, 23], "l1_l2": [3, 5], "l1regl": 7, "l2": [3, 5], "l_": 18, "l_1": 9, "l_2": [9, 15], "l_j": 14, "la": 15, "la_i": 14, "la_k": 14, "lab": [17, 23], "label": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 23], "labelencod": [9, 12], "labels": [8, 10, 11], "labels_shuffl": [2, 3], "laboratori": 19, "lack": [2, 23], "lagari": 4, "lagrang": [10, 13], "lambda": [2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 20, 23], "lambda_": 13, "lambda_0": 13, "lambda_1": [7, 10, 13], "lambda_2": [10, 13], "lambda_i": [10, 13], "lambda_iy_i": 10, "lambda_jy_iy_j": 10, "lambda_k": 10, "lambda_n": [7, 10], "lamda": 3, "land": [2, 10], "landmark": 10, "landscap": 15, "langl": [2, 8, 13, 20, 23], "languag": [2, 3, 6, 10, 17, 18, 22, 23], "lapack": [18, 23], "laplac": 7, "laptop": [0, 17], "larg": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 22, 23], "larger": [2, 5, 7, 8, 10, 12, 13, 15, 20, 23], "largest": [6, 10, 13], "lasso": [2, 9, 17, 23], "lasso_sk": 8, "last": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 18, 20, 21, 23], "latent": 6, "latent_dim": 6, "latent_point": 6, "latent_space_value_rang": 6, "later": [0, 2, 3, 6, 9, 10, 14, 15, 16, 17, 23], "latest": [0, 6, 17], "latest_checkpoint": 6, "latex": 23, "latter": [2, 5, 8, 9, 10, 13, 15, 18, 20, 23], "lattic": 14, "law": 2, "layer": [2, 6, 15, 23], "lbfg": [9, 11, 12], "lcc": [7, 8], "lda": 13, "ldot": [2, 8, 13, 23], "le": [7, 9, 12, 15, 20], "lead": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "leaf": 11, "leaki": 3, "leakyrelu": 6, "lear": 15, "learn": [5, 6, 7, 8, 9, 10, 11, 12, 14, 18, 21, 22], "learnabl": 5, "learner": 12, "learnig": 23, "learning_r": [10, 12], "learning_rate_init": [2, 3, 23], "learning_schedul": 15, "least": [2, 9, 10, 12, 13, 17, 18, 20], "leat": 15, "leav": [2, 3, 5, 7, 8, 11, 13, 23], "lectur": [2, 3, 7, 12, 13, 14, 15, 17, 18, 19, 21, 22], "lecturenot": [2, 17, 22, 23], "left": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "leftarrow": [10, 14], "legend": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 23], "leinonen": 23, "len": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 18, 23], "length": [1, 2, 3, 5, 6, 10, 11, 15, 17, 23], "length_of_sequ": 6, "leq": [2, 7, 9, 10, 15, 16, 20, 23], "less": [2, 3, 5, 6, 7, 8, 10, 11, 15, 17, 20, 23], "lessen": 3, "let": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "letter": [1, 2, 18, 20, 23], "level": [2, 3, 7, 8, 11, 17, 18, 19, 21, 23], "li": [10, 13, 23], "lib": 23, "liblinear": 12, "librari": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 18, 20, 22], "licens": [2, 3, 17, 23], "lie": [2, 8, 13, 20, 23], "life": [2, 3, 10, 14, 23], "lifetim": 15, "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23], "likelihood": [2, 3, 7, 11, 23], "lim_": 20, "limit": [2, 7, 8, 10, 14, 18, 23], "lin_clf": 10, "lin_model": 2, "lin_reg": 11, "linalg": [2, 4, 7, 8, 10, 13, 15, 18, 20, 23], "line": [0, 1, 2, 5, 8, 10, 13, 15, 23], "line1": 10, "line2": 10, "line3": 10, "line_model": 0, "line_ms": 0, "line_predict": 0, "linear": [1, 3, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20], "linear_model": [0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 15, 23], "linear_regress": 8, "linearli": 7, "linearloc": [8, 15], "linearregress": [0, 1, 2, 8, 9, 11, 23], "linearsvc": 10, "liner": [3, 5], "linerar": 12, "linewidth": [2, 4, 6, 8, 10, 11, 12], "link": [0, 2, 6, 11, 14, 17, 19, 21, 23], "linlag": 7, "linpack": [18, 23], "linreg": [2, 23], "linspac": [1, 2, 4, 5, 6, 8, 10, 11, 12, 15, 18, 20, 23], "linu": 6, "linux": [2, 3, 17, 23], "liquid": [2, 23], "list": [0, 2, 3, 4, 5, 6, 11, 17, 23], "listedcolormap": [11, 12], "literatur": [3, 9, 16, 22], "littl": [3, 5, 11, 14], "live": [1, 10], "ll": [2, 20, 23], "lle": 2, "lloyd": [6, 16], "lmb": [2, 4, 7, 8], "lmbd": [2, 3, 5, 23], "lmbd_val": [2, 3, 5, 23], "lmbda": 15, "ln": [3, 15], "load": [2, 3, 6, 8, 9, 11, 12], "load_boston": 2, "load_breast_canc": [3, 9, 11, 12, 13], "load_data": [5, 6], "load_digit": [3, 5], "load_iri": [10, 11], "loc": [2, 5, 8, 9, 10, 11, 12, 23], "local": [0, 2, 3, 5, 9, 14, 15], "locat": [0, 4, 5, 10], "log": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 18, 23], "log10": [2, 7, 8], "log_": [2, 23], "log_clf": 12, "logarithm": [2, 7, 9, 18, 23], "logic": [2, 3, 11, 23], "login": 0, "logist": [2, 3, 4, 10, 11, 12, 13, 14, 15, 17], "logisticregress": [9, 11, 12, 13], "logit": 9, "logreg": [9, 11, 12, 13], "logspac": [2, 3, 5, 7, 8, 23], "long": [2, 3, 5, 6, 14, 15, 23], "longer": [4, 5, 10, 12, 16, 18, 20, 23], "loocv": 8, "look": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "loop": [1, 3, 6, 8, 12, 14, 16, 17, 18, 23], "lose": 3, "loss": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 18, 23], "loss_fil": 6, "lossfil": 6, "lost": 6, "lot": [1, 2, 3, 6, 8], "low": [2, 8, 11, 12, 13, 23], "lower": [1, 2, 3, 5, 8, 11, 12, 18], "lowercas": [18, 23], "lowest": [11, 15, 20], "lr": [3, 5, 6, 12], "lstat": 2, "lstm": 6, "lstm_2layer": 6, "lstsq": [2, 23], "lt": 8, "lu": [2, 7, 23], "lubksb": 18, "luckili": 4, "ludcmp": 18, "lux": 18, "lvert": 3, "lw": [2, 23], "m": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23], "m_": [11, 14], "m_1": 16, "m_h": [2, 23], "m_k": 16, "m_l": 14, "m_n": [2, 23], "m_p": [2, 23], "m_t": 15, "ma": 13, "machin": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 22], "machinelearn": [1, 2, 8, 17, 19, 21, 22, 23], "mackai": 22, "made": [2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 23], "mae": [2, 23], "magic": 6, "magnitud": [3, 8, 9, 15], "mai": [2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "mail": [19, 21], "main": [2, 3, 5, 6, 7, 8, 9, 11, 18, 22], "mainli": [2, 7, 8, 9, 11, 23], "maintain": 8, "major": [3, 8, 11, 12, 15, 18, 23], "make": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 22, 23], "make_axes_locat": 8, "make_moon": [10, 11, 12], "make_pipelin": [2, 8, 12], "makedir": [2, 8, 9, 11, 23], "makeplot": 2, "malcondit": 18, "malign": [3, 9, 11], "mammographi": 7, "manag": [0, 2, 4, 5, 17, 23], "mandatori": [21, 23], "mani": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 22, 23], "manifold": 13, "manner": 5, "manual": 8, "map": [2, 3, 4, 8, 9, 10, 13, 14, 16, 20, 23], "margin": [2, 7, 10], "marit": [2, 23], "mark": 23, "marker": [2, 9, 18, 23], "markov": [17, 23], "marsaglia": 20, "mass": [2, 3, 7, 15], "massag": [2, 23], "masses2016": [2, 23], "masses2016ol": [2, 23], "masses2016tre": 2, "masseval2016": [2, 23], "master": [19, 21], "mat": [17, 23], "mat1100": [17, 23], "mat1110": [17, 23], "mat1120": [17, 23], "match": [0, 3, 6, 7, 15, 16], "materi": [0, 6, 7, 9, 15, 18, 19, 21], "math": [5, 9, 14, 15, 18, 20, 22, 23], "mathbb": [2, 6, 7, 8, 9, 10, 13, 14, 15, 16, 18, 20, 23], "mathbf": [2, 7, 8, 9, 10, 15, 18, 23], "mathcal": [3, 7, 8, 9, 15], "matheemat": 5, "mathemat": [2, 8, 13, 14, 15, 17, 18, 20, 22, 23], "mathemati": 23, "mathrm": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "matmul": [3, 4, 7], "matnat": 22, "matplotlib": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "matric": [1, 2, 3, 5, 6, 8, 9, 10, 13, 15, 17], "matrix": [2, 4, 5, 6, 8, 9, 10, 12, 15, 20], "matshow": 3, "matter": [4, 5, 15], "max": [2, 3, 4, 5, 6, 11, 12, 14, 15, 21, 23], "max_depth": [2, 11, 12], "max_diff": 4, "max_diff1": 4, "max_diff2": 4, "max_it": [2, 3, 10, 15, 23], "max_iter": 16, "max_leaf_nod": 12, "max_sampl": 12, "maxdegre": [2, 8, 12], "maxdepth": 12, "maxim": [3, 6, 7, 9, 10, 13], "maximum": [2, 4, 5, 7, 9, 10, 11, 12, 15, 16, 23], "maxpolydegre": [7, 8], "maxpooling2d": 5, "mbox": [7, 8], "mcculloch": 14, "md": 13, "mdoel": 6, "mean": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "mean_absolute_error": [2, 23], "mean_divisor": 16, "mean_i": 20, "mean_matrix": 16, "mean_squared_error": [0, 2, 6, 8, 9, 12, 23], "mean_squared_log_error": [2, 23], "mean_vector": 16, "mean_x": 20, "meaning": [2, 6, 9, 23], "meansquarederror": [2, 23], "meant": [5, 9, 12, 15], "measur": [1, 2, 3, 4, 7, 8, 11, 13, 14, 16, 20, 23], "mechan": [2, 6, 20, 23], "median": [2, 23], "medicin": 14, "medium": [6, 10, 15], "medv": 2, "meet": [2, 21], "mehta": [2, 23], "memori": [5, 6, 13, 14, 15, 18], "mention": [2, 14, 15, 20, 23], "mere": 2, "meshgrid": [4, 7, 8, 10, 11, 12, 13], "mess": 0, "messag": [7, 15], "messi": 4, "met": [2, 5, 10], "meteorolog": 11, "meter": 8, "method": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 16, 17, 18, 20, 22], "metion": 8, "metric": [0, 2, 3, 5, 8, 9, 11, 12, 16, 23], "metropoli": [17, 23], "mev": [2, 20, 23], "mgd": 15, "mglearn": [17, 23], "mgrid": 15, "mhjensen": 23, "mi": 12, "mia": [21, 23], "microsoft": 22, "mid": 3, "midel": 6, "midnight": 0, "midpoint": 11, "might": [0, 2, 3, 4, 6, 8, 11, 15], "mild": 11, "millimet": 8, "million": [2, 23], "mimic": 14, "min": [2, 4, 7, 10, 11], "min_": [2, 4, 7, 16, 23], "min_samples_leaf": 11, "mind": [0, 2, 8, 15, 23], "mindboard": 6, "mine": [17, 23], "mini": [3, 13, 14, 15], "minibatch": [3, 13, 15], "minibathc": 15, "miniforge3": 23, "minim": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "minima": [2, 3, 9, 15, 23], "minimum": [2, 3, 4, 8, 10, 11, 13, 15], "minmaxscal": 2, "minor": 20, "minst": 3, "minu": 9, "mirjalili": 23, "mirror": 11, "misc": 8, "misclassif": [10, 11, 12], "misclassifi": [10, 12], "miser": 2, "mismatch": 3, "miss": [2, 9, 12], "mistak": 6, "mit": 22, "mix": [3, 4, 23], "mixtur": 15, "mk": [11, 18], "mkdir": [2, 8, 9, 11, 23], "ml": [2, 3, 12, 15, 18], "mlab": 20, "mle": [7, 9], "mlp": 3, "mlpclassifi": 3, "mlpregressor": [2, 23], "mm": 18, "mn": [14, 20], "mnist": [3, 13], "mod": 20, "mode": [19, 21, 23], "model": [1, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 20, 22], "model_select": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "moder": 12, "modern": [2, 8, 9, 17, 23], "modif": [4, 14, 15], "modifi": [2, 3, 5, 7, 9, 10, 12, 14, 15, 23], "modul": [1, 2, 18, 23], "modular": 20, "modulo": 20, "moe": 13, "moment": [7, 8, 15, 20], "mondai": [21, 23], "monitor": 15, "monoton": [7, 14, 20], "mont": [2, 8, 17, 20, 22, 23], "montli": 1, "moor": [7, 8], "more": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "moreov": [2, 5], "morten": [21, 23], "mortenhj": 23, "most": [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "mostli": [3, 13], "motion": [2, 15], "motiv": [3, 6], "move": [0, 1, 2, 6, 7, 8, 9, 11, 14, 15, 16, 20], "mpl": [2, 9, 23], "mpl_toolkit": [4, 8, 15], "mplot3d": [4, 8, 15], "mplregressor": 3, "mse": [0, 1, 2, 6, 7, 8, 11, 12, 23], "mse_simpletre": 12, "mselassopredict": 7, "mselassotrain": 7, "mseownridgepredict": 8, "msepredict": 7, "mseridgepredict": [2, 7, 8], "msetrain": 7, "msle": [2, 23], "mt": [9, 14], "mu": [2, 8, 13, 15, 20, 23], "mu0": 20, "mu1": 20, "mu2": 20, "mu_": [8, 20], "mu_i": 8, "mu_n": 13, "mu_x": 20, "much": [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "multi": [2, 3, 5, 9, 17, 23], "multiclass": [3, 9], "multidimension": [13, 14, 23], "multilay": 3, "multinomi": 9, "multipl": [0, 4, 6, 7, 8, 9, 14, 15, 20], "multipli": [5, 7, 8, 13, 15, 18, 20], "multiplum": 10, "multivari": [2, 4, 12, 13, 17, 20, 23], "multivariate_norm": [13, 16], "multpli": 1, "murphi": [13, 22, 23], "must": [0, 3, 4, 7, 8, 10, 12, 14, 15, 16, 20], "mutat": 9, "mutual": [3, 5, 8, 15], "mx_": 20, "my": 23, "myenv": 23, "myriad": [2, 17, 23], "mz1": 20, "mz2": 20, "m\u00f8svatn": 8, "n": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "n1": 18, "n2": 18, "n_": [3, 4, 5, 10, 14, 20], "n_0": [14, 20], "n_boostrap": [8, 12], "n_bootstrap": 8, "n_categori": [3, 5], "n_cluster": 16, "n_compon": 13, "n_epoch": 15, "n_estim": 12, "n_examples_to_gener": 6, "n_featur": 3, "n_filter": 5, "n_hidden": 4, "n_hidden_neuron": [2, 3, 23], "n_i": 20, "n_input": [2, 3, 5], "n_instanc": 11, "n_job": 12, "n_k": 16, "n_l": [14, 20], "n_layer": 3, "n_m": 11, "n_neuron": 3, "n_neurons_connect": 5, "n_neurons_layer1": 3, "n_neurons_layer2": 3, "n_point": 16, "n_sampl": [8, 10, 11, 12, 16], "n_split": 8, "n_step": 6, "n_t": 4, "n_x": 4, "nabla": [3, 15], "nabla_": [4, 15], "nabla_w": 15, "nag": 15, "naimi": [2, 23], "naiv": 9, "naive_kmean": 16, "name": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23], "narrow": 15, "nation": [3, 7], "nativ": [17, 23], "natur": [2, 3, 6, 10, 11, 14, 15, 20, 22, 23], "navier": 14, "navig": 0, "nb": 20, "nb_": 18, "nbconvert": 23, "nd": 16, "ndarrai": 8, "ne": [11, 12, 18, 20], "nearest": [3, 5, 8, 13], "nearli": 15, "neat": 23, "neccesari": 8, "necess": 4, "necessari": [2, 3, 5, 6, 10, 16, 23], "necessarili": [2, 6, 13, 20, 23], "necesserali": 7, "neck": 9, "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "neg": [2, 3, 5, 7, 8, 9, 12, 15, 18, 20, 23], "neg_mean_squared_error": 8, "neglect": 20, "neglig": 20, "neighbor": [5, 8, 13], "neither": [6, 15], "neq": [15, 16, 20], "nervou": 14, "nest": [11, 14], "nesterov": 15, "net": [4, 6, 14], "netlib": [18, 23], "network": [2, 11, 15, 17, 22], "neural": [2, 15, 17, 22], "neural_network": [2, 3, 4, 23], "neuralnetwork": 3, "neuron": [3, 4, 5, 6, 14], "neutral": [2, 23], "neutron": [2, 23], "never": [3, 6, 8, 11, 20], "new": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "new_chang": 15, "new_hobbit": 23, "newaxi": [2, 5, 8, 11], "newli": [2, 23], "newton": [3, 9, 10, 15, 20], "next": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 23], "next_guess": 15, "next_input": 6, "ng": 3, "ni": 16, "nice": [2, 3, 7, 13, 23], "niter": 15, "nitric": 2, "nlambda": [2, 7, 8], "nlp": 22, "nm": 20, "nm_n": [2, 23], "nmse": 8, "nn": [4, 7, 8, 14, 18, 23], "nn_model": 3, "nnmin": 4, "node": [3, 5, 11, 12, 14], "nois": [2, 6, 7, 8, 10, 11, 12, 15, 23], "noise_dimens": 6, "noisi": [3, 8], "non": [2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "none": [2, 3, 4, 6, 7, 11, 12, 15, 20, 23], "nonlinear": [5, 8, 10, 11, 13, 14], "nonneg": [8, 11, 15], "nonparametr": 8, "nonsens": 20, "nonsingular": 18, "nonumb": [5, 9, 10, 15, 18], "nor": [3, 6, 15], "norm": [2, 3, 7, 8, 10, 13, 15, 23], "normal": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "normali": [18, 23], "norwai": [8, 23], "notat": [2, 4, 7, 8, 15, 16, 20, 23], "note": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 22, 23], "notebook": [0, 1, 2, 3, 5, 11, 17, 23], "noth": [3, 4, 7, 10, 14, 16, 20], "notic": [6, 7, 14, 15, 18, 20, 23], "notion": 5, "novel": [5, 8, 12, 23], "novemb": [3, 21, 23], "now": [0, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23], "nowadai": [2, 3, 5, 11, 17, 23], "nox": 2, "np": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "npr": 4, "nsampl": 8, "nt": 4, "nu": 20, "nuclear": 7, "nuclei": [2, 20, 23], "nucleon": [2, 23], "nucleu": [2, 23], "num": 6, "num_coordin": 4, "num_hidden_neuron": 4, "num_it": 4, "num_neuron": 4, "num_neurons_hidden": 4, "num_point": 4, "num_tre": 12, "num_valu": 4, "number": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 23], "numberid": 9, "numberparamet": 5, "numer": [2, 7, 8, 11, 12, 13, 14, 15, 17, 18, 22, 23], "numpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "nunmpi": 7, "nx": 4, "ny": 20, "o": [2, 3, 6, 7, 8, 9, 10, 11, 13, 18, 21, 22, 23], "obei": [8, 13, 15], "object": [0, 2, 3, 6, 10, 12, 18, 23], "obliqu": 7, "observ": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "obtain": [2, 3, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "obviou": [7, 8, 13, 20], "obviouli": 23, "obvious": [2, 6, 7, 8, 18, 23], "occupi": 2, "occur": [2, 8, 10, 11, 18, 20, 23], "octob": [21, 23], "od": 2, "odd": [2, 5, 9, 23], "odenum": 4, "odesi": 4, "oen": 2, "off": [3, 5, 6, 7, 11, 15, 20], "offer": [8, 13, 17, 18, 19, 21, 23], "offic": [21, 23], "offici": [19, 23], "often": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "ofter": [18, 23], "ol": [2, 15], "old": [0, 3, 7, 12, 15], "ols_paramet": 1, "ols_sk": 8, "ols_svd": 8, "olsbeta": [2, 7], "omega": [4, 5, 8], "omega_0": 5, "omit": [2, 7, 23], "onc": [3, 8, 11, 13, 15], "one": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 23], "onehot": 3, "onehot_vector": 3, "onehotencod": 11, "ones": [1, 2, 4, 7, 8, 10, 11, 12, 13, 15, 18, 23], "ones_lik": 6, "onl": 5, "onli": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "onlin": [0, 13, 19], "onto": [7, 13], "open": [0, 2, 3, 6, 8, 9, 11, 17, 19, 21, 23], "oper": [0, 1, 2, 3, 5, 7, 8, 12, 13, 14, 15, 17, 20, 23], "operation": 20, "oplu": 20, "opmiz": 15, "opportun": 2, "oppos": [8, 15], "opposit": [3, 7, 10], "opt": [3, 7, 23], "optim": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16], "optimis": [3, 5], "option": [0, 2, 3, 5, 7, 8, 10, 13, 18], "optmiz": [3, 10, 15], "oral": 23, "orang": 2, "order": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "ordinari": [2, 4, 5, 9, 13, 15, 17], "oreilli": [22, 23], "org": [1, 2, 5, 6, 17, 18, 22, 23], "organ": [8, 9, 12, 18], "orient": [3, 7, 20], "origin": [0, 2, 5, 7, 8, 10, 13, 14, 15, 18, 23], "orthogn": 7, "orthogon": [2, 7, 8, 10, 13, 15, 18, 23], "orthonorm": 7, "os": [21, 23], "oscar": 3, "oscil": [5, 15], "oskar": 23, "oskarlei": 23, "oslo": [2, 17, 19, 21, 23], "osx": [2, 17, 23], "other": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 15, 16, 17, 19, 20, 21, 22], "otherwis": [2, 3, 6, 9, 15, 18, 23], "ouput": [7, 9, 14], "our": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20], "ourmodel": 2, "ourselv": [2, 7, 8, 10, 13, 15, 23], "out": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "out_fil": 11, "outcom": [2, 9, 11, 12, 14, 20], "outdoor": 11, "outer": [8, 14, 15], "outfil": 6, "outlier": [2, 10, 23], "outlin": [8, 12, 13], "outlook": 11, "outperform": 12, "output": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "output_bia": 3, "output_bias_gradi": 3, "output_shap": 6, "output_weight": 3, "output_weights_gradi": 3, "outputlayer1": 14, "outputlayer2": 14, "outsid": 6, "over": [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 14, 15, 18, 23], "over1": 15, "overal": [3, 12], "overcast": 11, "overcom": [14, 15], "overdetermin": [2, 23], "overfit": [2, 3, 5, 8, 11, 12, 15], "overflow": 7, "overhead": 14, "overlap": [5, 9, 10, 11], "overlin": [2, 7, 8, 11, 12, 13, 16, 18, 23], "overst": 2, "overtrain": 6, "overview": 5, "own": [1, 6, 7, 8, 10, 14, 15, 17, 18], "owner": 2, "ownmsepredict": 2, "ownmsetrain": 2, "ownridgebeta": [2, 8], "ownypredictridg": 2, "ownytilderidg": 2, "oxid": 2, "p": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "p0": 4, "p1": 4, "p_": [4, 6, 10, 11], "p_hidden": 4, "p_i": [7, 20], "p_j": 20, "p_n": 20, "p_output": 4, "p_x": 20, "pack": [2, 23], "packag": [0, 2, 3, 5, 6, 7, 10, 13, 15, 17, 20], "packtpub": 23, "packtpublish": 23, "pad": [5, 6], "page": [2, 17, 23], "pai": [0, 2, 3, 11, 15], "pair": [2, 4, 5, 11, 17, 20, 23], "paltform": 0, "panda": [2, 6, 7, 8, 9, 11, 13, 17], "panel": 23, "paper": 3, "paradigm": [2, 23], "parallel": [12, 15, 17, 18, 23], "param": 4, "paramat": 4, "paramet": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20], "parameter": [2, 8, 12, 23], "parametr": [2, 8, 23], "paramt": [5, 7], "part": [2, 3, 5, 7, 8, 12, 18, 19, 20, 21, 23], "partial": [1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "particip": [0, 17, 19, 21, 23], "particl": [2, 6, 15, 20, 23], "particular": [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 20, 22, 23], "particularli": [7, 8, 10, 13, 15, 20], "partit": [3, 6, 11], "partli": [8, 23], "partner": 0, "pass": [4, 5, 14, 16], "past": [12, 20], "patch": [8, 20], "path": [2, 6, 8, 9, 11, 17, 23], "patient": 9, "patter": 6, "pattern": [2, 5, 6, 14, 22, 23], "pauli": [2, 23], "pc": [13, 17], "pca": [2, 9, 17, 23], "pd": [2, 6, 7, 8, 9, 11, 13, 23], "pde": 4, "pdf": [0, 1, 2, 5, 6, 7, 8, 11, 22, 23], "pedagog": [2, 23], "penal": 8, "penalti": [8, 15], "penros": [7, 8], "pentagon": 15, "peopl": [2, 3, 11, 15, 17], "per": [2, 3, 8, 19, 21, 23], "percentag": [2, 12, 13, 21], "perceptron": [2, 3, 9, 23], "peregrin": 23, "perfect": [2, 3, 15, 23], "perfectli": [6, 8], "perform": [1, 2, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 20, 23], "performac": 6, "perhap": [2, 7, 15, 23], "perimet": 3, "period": [3, 6, 20], "permiss": 0, "permut": 13, "persist": 15, "person": [1, 7, 8, 9, 19, 21, 23], "perspect": 22, "pertin": [14, 23], "petal": [10, 11], "peter": 22, "phantom": 20, "phase": [8, 14], "phenomena": 20, "phi": 10, "phi_k": 10, "philosophi": 15, "phone": [21, 23], "photo": [6, 23], "phrase": [2, 23], "physic": [2, 3, 6, 9, 14, 15, 20, 21, 22, 23], "pi": [4, 5, 7, 8, 9, 11, 14, 15, 20], "pick": [3, 11, 12, 13, 15, 16], "pickl": 3, "pictur": [2, 23], "pie": [17, 23], "piec": [13, 16], "pillow": [2, 17, 23], "pinv": [7, 8, 15], "pip": [0, 2, 3, 17, 23], "pip3": [2, 3, 23], "pipelin": [2, 8, 10, 12], "pippin": 23, "pit": 6, "pitfal": 8, "pitt": 14, "pixel": [3, 5, 6, 23], "pixel_height": [3, 5], "pixel_width": [3, 5], "place": [0, 2, 6, 8, 10, 15, 18, 23], "plai": [2, 5, 6, 7, 8, 10, 13, 17, 23], "plain": [10, 12, 14, 15, 16], "plan": [8, 11, 21, 22, 23], "plane": [10, 11], "plateau": 7, "platform": [17, 23], "plausibl": 14, "pleas": [15, 21, 23], "plenti": 3, "plethora": [5, 14], "plot": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "plot_confusion_matrix": [9, 12], "plot_count": 8, "plot_cumulative_gain": [9, 12], "plot_data": 3, "plot_dataset": 10, "plot_decision_boundari": [11, 12], "plot_import": 12, "plot_max": 6, "plot_min": 6, "plot_model": 6, "plot_numb": 6, "plot_predict": 10, "plot_regression_predict": 11, "plot_result": 6, "plot_roc": [9, 12], "plot_surfac": [4, 8, 15], "plot_train": 11, "plot_tre": [11, 12], "plt": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "plu": [2, 5, 7, 9, 23], "pm": 10, "pmatrix": 4, "pml": 22, "pn": 5, "png": [2, 6, 8, 9, 11, 23], "point": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "point_1": 6, "point_2": 6, "poisson": [17, 20, 23], "poli": [8, 10], "poly100_kernel_svm_clf": 10, "poly3": 2, "poly3_plot": 2, "poly_featur": [0, 10, 11], "poly_features10": 11, "poly_fit": 11, "poly_fit10": 11, "poly_kernel_svm_clf": 10, "poly_model": 0, "poly_ms": 0, "poly_predict": 0, "polydegre": [2, 7, 8, 12], "polygon": 15, "polym": 14, "polynomi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 23], "polynomial_featur": [0, 1, 8], "polynomial_svm_clf": 10, "polynomialfeatur": [0, 1, 2, 8, 10, 11], "polytrop": [2, 8], "pool": 5, "pool_siz": 5, "poor": [3, 15], "poorli": 2, "popul": [2, 7, 23], "popular": [0, 2, 3, 5, 8, 9, 10, 11, 13, 14, 17, 18, 20], "popularli": [2, 23], "portabl": 12, "portion": [13, 15], "pose": [2, 6, 7, 8, 13, 20, 23], "posit": [2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 18, 20, 23], "possibl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 23], "possibli": [8, 10, 15], "posterior": 7, "postpon": 2, "postul": 7, "potenti": [2, 5, 7, 8, 14, 15], "pott": 14, "power": [2, 3, 7, 8, 10, 11, 14, 15, 23], "pp": [7, 8], "practic": [1, 2, 7, 8, 9, 10, 20], "practition": [2, 3, 5, 23], "pre": 23, "preced": [3, 13, 14, 20], "preceed": 6, "preceq": 10, "precis": [2, 4, 7, 13, 15, 18, 20, 23], "pred": 8, "predicit": 2, "predict": [0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 17, 22, 23], "predict_prob": 3, "predict_proba": [9, 12], "predictor": [2, 7, 8, 9, 11, 12, 13, 23], "prefer": [2, 3, 8, 10, 11, 13, 15, 17, 23], "prepar": [2, 8, 18, 23], "preprocess": [0, 1, 2, 6, 8, 9, 10, 11, 12, 13], "prerequisit": 2, "presenc": 15, "present": [2, 7, 8, 9, 11, 14, 15, 18, 20, 23], "preserv": [5, 13, 18], "press": [0, 15, 22], "pretrain": [3, 6], "pretti": [2, 6, 10, 11, 17, 23], "prev_centroid": 16, "prevent": [15, 20], "previou": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20], "previous": [4, 5, 11, 12, 20], "price": [2, 6, 11, 15], "primal": 10, "primari": [2, 9, 23], "prime": 20, "princip": [2, 7, 9, 17, 23], "principl": [2, 8, 9, 10, 16, 23], "print": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "print_funct": [10, 11], "printout": [2, 23], "prior": [2, 7, 8, 23], "privat": 2, "prob": [3, 20], "probabilist": [2, 22, 23], "probabl": [2, 3, 5, 6, 8, 9, 12, 15, 17, 23], "problem": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20], "probml": 22, "proce": [2, 7, 8, 9, 10, 11, 12, 13, 15, 18, 23], "procedur": [4, 6, 7, 8, 10, 12, 13, 15], "proceed": 18, "process": [2, 4, 6, 8, 11, 12, 14, 15, 17, 18, 20, 22, 23], "prod": 22, "prod_": [3, 7, 9], "produc": [2, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 23], "product": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 18, 23], "profess": [2, 23], "program": [0, 2, 3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 20, 21, 23], "programm": 18, "progress": [3, 6, 16], "prohibit": 8, "project": [0, 2, 3, 4, 5, 7, 13, 15, 17, 19], "project_root_dir": [2, 8, 9, 11, 23], "promin": 14, "promis": 10, "promot": [21, 23], "prone": [0, 11], "pronounc": [15, 17, 23], "proof": [2, 13, 14, 15, 23], "propag": [4, 5, 15], "proper": [2, 4, 8, 9], "properli": [3, 8, 10, 12, 15], "properti": [1, 2, 3, 5, 14, 15, 18, 23], "proport": [2, 3, 7, 11, 13, 15, 20, 23], "propos": [3, 6, 8, 12, 23], "propto": [7, 15], "proton": [2, 23], "prove": [5, 15], "provid": [2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 17, 18, 20, 23], "proxi": [3, 15], "prune": 11, "pseudo": [18, 20], "pseudoinv": 7, "pseudoinvers": [7, 8], "pseudorandom": [8, 20], "psychologi": [2, 23], "pt": 15, "public": [0, 2, 17, 23], "pull": 0, "punish": [2, 3, 23], "pure": [5, 11, 20], "purest": 11, "puriti": 11, "purpos": [2, 5, 12, 14, 16, 23], "push": 0, "put": 3, "py": [7, 23], "pycod": 23, "pydata": 17, "pydot": 11, "pyhton2": 23, "pylab": [2, 9, 23], "pypi": 17, "pyplot": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "pythagora": 7, "python": [3, 4, 5, 7, 8, 10, 13, 14, 15, 16, 20], "python2": 2, "python3": [2, 17, 23], "pytorch": [2, 17, 23], "q": [7, 8, 10, 13, 20], "qp": 10, "qquad": [4, 13, 15, 18], "qr": [7, 8, 18], "quad": [3, 15, 18], "quadrat": [2, 10, 11, 15, 23], "qualit": [6, 11, 20], "qualiti": [2, 11, 17, 23], "quantifi": 3, "quantil": 12, "quantit": [2, 8, 11, 23], "quantiti": [1, 2, 4, 7, 8, 9, 11, 12, 13, 14, 16, 18, 20, 23], "quantum": [6, 14, 22, 23], "quartil": 2, "quench": 7, "queri": 11, "question": [2, 7, 8, 11, 13, 14, 15, 21, 23], "qugan": 6, "quick": [6, 20], "quickli": [3, 5, 11, 13, 15], "quit": [0, 3, 7, 8, 11, 12, 14], "quot": 6, "r": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "r2": [2, 7, 8, 23], "r2_score": [2, 23], "r2score": [2, 23], "r_1": 11, "r_2": 11, "r_j": 11, "r_m": 11, "rad": 2, "radial": [2, 10, 14], "radioact": 20, "radiu": [2, 3], "rain": 11, "ramp": 3, "ran0": 20, "ran1": 20, "ran2": 20, "ran3": 20, "rand": [0, 2, 6, 7, 8, 11, 12, 15, 18, 23], "randint": [8, 11, 15], "randn": [0, 2, 3, 4, 7, 8, 11, 13, 15, 23], "random": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 17, 18, 23], "random_forest_model": 12, "random_index": 15, "random_indic": [3, 5], "random_st": [2, 9, 10, 11, 12, 13], "randomforestclassifi": 12, "randomli": [3, 8, 11, 15, 16], "rang": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "rangl": [2, 8, 13, 20, 23], "rangle_x": 20, "rank": 7, "rankdir": 6, "raphson": [3, 10, 15], "rapidli": 2, "rare": [3, 15], "raschka": 23, "rasckha": 23, "rate": [2, 3, 4, 5, 6, 10, 11, 12, 14, 15], "rather": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "ratio": [6, 9, 11, 12, 13], "rational": [2, 23], "ravel": [7, 8, 9, 10, 11, 12, 13, 15, 18], "raw": 5, "rbf": [10, 13, 14], "rbf_kernel_svm_clf": 10, "rbf_pca": 13, "rc": [2, 20], "rcond": [2, 23], "rcparam": [2, 3, 5, 9, 10, 11, 12, 20, 23], "re": [0, 4, 6, 15], "reach": [3, 6, 7, 8, 11, 12, 14, 15, 16, 23], "read": [1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 18, 20, 22], "read_csv": [2, 8, 9, 11], "read_fwf": [2, 23], "reader": [2, 8, 18, 20, 23], "readi": [2, 3, 7, 8, 10, 12, 13, 14, 18, 23], "readili": 3, "readm": 0, "readthedoc": 17, "real": [1, 2, 3, 6, 9, 12, 13, 14, 18], "real_loss": 6, "real_output": 6, "realist": [10, 23], "realiti": 20, "realiz": [3, 14], "realli": [2, 3, 23], "rearrang": 15, "reason": [2, 3, 5, 6, 12, 15, 22, 23], "reassign": 3, "recal": [7, 8, 11, 12, 13, 14, 18, 20, 23], "recast": 5, "receiv": [3, 5, 12, 14, 20], "recent": [2, 8, 15, 22], "recept": [5, 14], "receptive_field": 5, "recip": [2, 8, 9, 18, 23], "reciproc": 7, "recogn": [2, 6, 7, 12, 23], "recognit": [2, 3, 5, 14, 22, 23], "recommen": 23, "recommend": [0, 2, 4, 5, 6, 7, 8, 10, 15, 17, 18, 22], "reconsid": 11, "reconstruct": 13, "record": [12, 19, 21, 23], "recreat": 0, "rectangl": [11, 15], "rectangular": 7, "rectifi": [3, 5, 14], "recur": [2, 17, 23], "recurr": [2, 3, 17, 23], "recurs": [11, 17, 18, 23], "red": [2, 5, 6, 8, 10, 11], "redefin": [2, 12, 23], "reduc": [3, 5, 7, 8, 11, 12, 13, 15, 23], "reduct": [2, 12, 13, 17, 20, 23], "refer": [2, 3, 4, 5, 7, 8, 13, 14, 15, 16, 18, 22, 23], "referenc": 4, "refin": 14, "refit": 8, "reflect": [2, 3, 6, 7, 20, 23], "refresh": [17, 23], "refreshprogrammingskil": 23, "reg": [12, 13], "regard": [3, 11, 15], "regardless": [1, 14], "region": [5, 6, 8, 11, 14], "regist": [8, 20], "reglasso": 7, "regr_1": [2, 11], "regr_2": [2, 11], "regr_3": [2, 11], "regress": [1, 3, 10, 13, 14, 17, 18], "regressor": [2, 9, 12], "regridg": [2, 7, 8], "regular": [2, 5, 6, 7, 8, 9, 11, 15, 21, 23], "regularli": 0, "reilli": [2, 22, 23], "reinforc": [2, 10, 17, 23], "reiter": 3, "reject": 9, "rel": [2, 6, 8, 9, 11, 14, 15, 20, 23], "relat": [2, 3, 5, 6, 7, 13, 15, 16, 18, 20, 23], "relationship": [2, 6, 11, 23], "relativeerror": [2, 23], "releas": [3, 17, 23], "relev": [2, 3, 7, 9, 13, 17, 20, 23], "reli": [2, 8, 10], "reliabl": [9, 20], "relu": [5, 6, 23], "remain": [3, 4, 6, 8, 14, 18, 20], "remaind": 20, "reman": 4, "remark": 3, "rememb": [2, 10, 15, 18, 23], "remind": [2, 7, 13, 15, 18, 20], "remot": 0, "remov": [2, 6, 7, 8], "renam": 0, "render": [2, 23], "reorder": [7, 9], "reorgan": [2, 23], "repeat": [2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 16, 18, 20, 23], "repeated": 23, "repeatedli": [2, 8, 12, 15], "repet": 5, "repetit": [8, 23], "rephras": 15, "replac": [2, 3, 5, 6, 7, 8, 12, 14, 16, 17, 23], "replica": 8, "repo": 0, "report": 23, "repositori": [2, 6, 23], "repres": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23], "represent": [2, 3, 5, 8, 20, 23], "representd": 5, "reproduc": [0, 1, 2, 7, 8, 11, 14, 17, 20, 23], "repuls": [2, 23], "request": [2, 15], "requir": [0, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 18, 23], "res1": 4, "res2": 4, "res3": 4, "res_analyt": 4, "res_analytical1": 4, "res_analytical2": 4, "res_analytical3": 4, "resaml": 8, "resampl": [2, 9, 12, 17, 23], "rescal": [2, 13, 14], "rescu": 7, "reseach": 8, "research": [2, 6, 15, 17, 22, 23], "resembl": [8, 20], "reserv": [3, 7, 8, 20], "reshap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 18, 23], "residenti": 2, "residu": [2, 7, 15, 23], "resiz": 7, "resourc": 23, "respect": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 20, 23], "respond": 14, "respons": [2, 9, 11, 14, 23], "rest": [2, 7], "restat": [2, 14, 23], "restor": 6, "restored_discrimin": 6, "restored_gener": 6, "restrict": [2, 5, 11, 14, 23], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "retail": 2, "retain": [7, 8], "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 20, 23], "return_data": 16, "return_sequ": 6, "return_x_i": 11, "reus": [3, 5, 8], "reveal": [2, 14, 23], "revers": [3, 18], "review": [17, 18], "revisit": 16, "revolut": 23, "reward": [2, 6, 23], "rewrit": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "rewritten": [4, 8, 10, 12, 20], "rewrot": 15, "rf": 12, "rgb": 5, "rgoj5yh7evk": 17, "rh": 8, "rho": [2, 12, 15], "rho_1": 12, "rho_2": 12, "rho_m": 12, "rich": [2, 23], "ride": 11, "rideclass": 11, "ridedata": 11, "ridg": [9, 13, 15, 17, 23], "ridge_sk": 8, "ridgebeta": 7, "right": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "right_sid": 4, "rightarrow": [2, 3, 7, 8, 10, 13, 14, 15, 20, 23], "rigor": [2, 23], "ring": 8, "rise": [2, 23], "risk": [2, 15, 23], "rival": 6, "river": 2, "rlm": 23, "rm": [2, 20], "rmse": 2, "rmsporp": 15, "rmsprop": [3, 5, 6, 15], "rnd_clf": 12, "rng": 20, "rnn": [6, 14], "rnn1": 6, "rnn2": 6, "rnn_2layer": 6, "rnn_input": 6, "rnn_output": 6, "rnn_train": 6, "rntrick1": 20, "rntrick2": 20, "rntrick3": 20, "rntrick4": 20, "ro": [2, 15, 23], "robert": 22, "robust": [2, 23], "robustscal": 2, "roc": [9, 12], "role": [2, 4, 7, 8, 10, 17, 23], "roll": 8, "room": [2, 21, 23], "root": [0, 2, 7, 11, 15, 20], "rot": 23, "rotat": [3, 10, 11, 12], "rotation_matrix": 11, "roughli": [3, 5], "round": [2, 9, 11, 15], "routin": [15, 18, 23], "row": [1, 2, 3, 4, 7, 8, 11, 13, 18, 23], "rr": 7, "rrr": 7, "rug": 15, "rule": [2, 3, 7, 8, 15, 23], "run": [0, 2, 3, 4, 6, 7, 8, 10, 11, 13, 15, 17, 23], "runtim": [0, 3, 8, 16], "rust": [2, 17, 18, 23], "rvert": 3, "rvert_2": 3, "s_": [5, 8], "s_1": 8, "s_i": [8, 9], "s_j": 8, "s_k": 8, "saddl": 15, "sai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "said": [8, 11, 15], "sake": [2, 7, 9, 13, 23], "sale": [2, 23], "sam": 23, "same": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 23], "samm": 12, "sampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 23], "sample_vari": 16, "sampleexptvari": 20, "samwis": 23, "sastri": 13, "satisfactori": [2, 23], "satisfi": [3, 4, 5, 8, 10, 15, 18, 20], "satur": [3, 8], "save": [2, 6, 8, 9, 11, 15, 23], "save_fig": [2, 8, 9, 11, 12, 23], "savefig": [2, 6, 8, 9, 11, 20, 23], "savetxt": 6, "saw": 7, "scalabl": 12, "scalar": [4, 7, 8, 12], "scale": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 23], "scale_mean": 6, "scale_std": 6, "scaler": [2, 9, 10, 11, 12, 13], "scan": [7, 9], "scari": 7, "scatter": [0, 2, 3, 8, 9, 10, 11, 16, 23], "scenario": [8, 15], "schedul": 15, "scheme": [3, 15], "schrage": 20, "sch\u00f8yen": 8, "scienc": [2, 3, 12, 14, 15, 17, 19, 20, 21, 22], "scientif": [2, 17, 23], "scientist": [2, 23], "scikit": [0, 1, 5, 7, 8, 10, 11, 12, 15, 17, 18, 22], "scikit_learn": 2, "scikitlearn": 23, "scikitplot": [9, 12], "scipi": [2, 5, 7, 8, 15, 17, 18, 23], "scl": 8, "scm": 0, "score": [0, 1, 2, 3, 5, 8, 9, 11, 12, 13, 21, 23], "scores_kfold": 8, "scratch": [1, 3, 15], "sdg": 15, "seaborn": [2, 3, 5, 8, 9, 23], "seamless": [2, 17, 23], "search": [0, 2, 3, 5, 7, 11, 15, 23], "sebastian": 23, "sebastianraschka": 23, "sec": 8, "second": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 23], "secondeigvector": 13, "secondli": 14, "section": [1, 6, 13, 18, 20], "sector": 2, "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 23], "seed": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 20, 23], "seed_imag": 6, "seek": [3, 4, 10], "seem": [3, 5, 6], "seemingli": [2, 23], "seen": [2, 3, 5, 7, 12, 14, 20], "segment": 15, "seismic": 8, "seldomli": [2, 23], "select": [0, 3, 7, 8, 10, 11, 12, 13, 19, 20, 21, 22, 23], "selevet": 0, "self": [3, 7], "sell": 6, "semest": [9, 19], "semi": [10, 15], "semilogx": 8, "send": [7, 14, 15, 21, 23], "senior": [19, 21], "sens": [2, 6, 8, 10, 23], "sensibl": 5, "sensit": [2, 7, 8, 11, 15, 23], "sent": 4, "sentenc": [6, 14], "separ": [2, 3, 4, 6, 8, 10, 11, 14, 16, 17, 20, 23], "septemb": 23, "sequenc": [5, 6, 9, 11, 12, 14, 15, 17, 18, 20, 23], "sequenti": [3, 5, 6, 12, 14, 20], "seri": [2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 18, 23], "serif": [2, 9, 20, 23], "serv": [2, 3, 4, 5, 7, 9, 15, 22, 23], "session": [0, 3, 19, 21, 23], "set": [1, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 20, 21], "set_major_formatt": 8, "set_major_loc": 8, "set_tick": [3, 10], "set_ticklabel": 3, "set_titl": [2, 3, 4, 5, 9, 14, 16, 23], "set_xlabel": [2, 3, 4, 5, 9, 14, 23], "set_xlim": [9, 14], "set_xticklabel": 3, "set_ylabel": [2, 3, 4, 5, 9, 23], "set_ylim": [9, 14], "set_ytick": 9, "set_yticklabel": [3, 8], "set_zlim": 8, "seth": 6, "setminu": 8, "setosa": [10, 11], "setosa_or_versicolor": 10, "setp": 8, "setup": [3, 6, 8, 10, 17, 23], "sever": [1, 2, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "sgd": [3, 5], "sgd_clf": 10, "sgdclassifi": 10, "sgdreg": 15, "sgdregressor": 15, "sgn": 7, "shallow": 15, "shape": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "share": [0, 3, 5, 23], "shareabl": 0, "she": 9, "shift": [0, 3, 8, 14, 20], "ship": 5, "shire": 23, "short": [6, 7], "shortcom": 15, "shorten": 6, "shorter": 20, "shorthand": 23, "shortli": [18, 23], "should": [0, 2, 4, 5, 7, 8, 10, 11, 13, 14, 18, 20, 23], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "show_shap": 6, "shown": [2, 6, 7, 10, 14, 15, 18], "shrink": [5, 7, 8, 10, 13], "shrinkag": [7, 8], "shrunk": 13, "shuffl": [2, 3, 6, 8, 15], "side": [2, 4, 7, 10, 14, 15, 18, 23], "sigh": [17, 23], "sigma": [2, 3, 7, 8, 9, 12, 13, 14, 15, 18, 20, 23], "sigma0": 20, "sigma1": 20, "sigma2": 20, "sigma_": [7, 18, 23], "sigma_0": 7, "sigma_1": 7, "sigma_2": 7, "sigma_fn": [9, 14], "sigma_i": [2, 7, 23], "sigma_j": 7, "sigma_m": [8, 20], "sigma_n": [13, 20], "sigma_t": 15, "sigma_x": 20, "sigmoid": [3, 4, 6, 9, 10, 12, 14], "sigmundson": 8, "sign": [3, 4, 9, 10, 12, 20, 21], "signal": [3, 5, 12, 14], "signifi": 6, "signific": 3, "significantli": [3, 15, 20], "sim": [6, 7, 8, 15, 20], "similar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 23], "similarli": [2, 3, 5, 7, 10, 12, 15, 20, 23], "simpl": [1, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20], "simplepredict": 12, "simpler": [1, 2, 3, 7, 8, 9, 15, 17, 23], "simplernn": 6, "simplest": [2, 3, 5, 6, 11, 12, 14, 16, 23], "simpletre": 12, "simpli": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 23], "simplic": [4, 7, 8, 9, 10, 11, 12, 13, 14, 16], "simplicti": 7, "simplifi": [2, 8, 11, 17, 23], "simplist": [5, 8, 20], "simul": 8, "simultan": 8, "sin": [2, 3, 4, 5, 6, 11, 14, 15, 18, 23], "sinc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 22, 23], "sine": [5, 14], "singl": [2, 3, 4, 5, 7, 8, 9, 10, 11, 14, 15, 18, 20, 23], "singular": [2, 8, 15, 18, 23], "sinusoid": 5, "site": [2, 19, 23], "situat": [2, 6, 7, 9, 15, 20, 23], "six": [5, 20], "size": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 18, 20, 23], "sketch": 12, "ski": 11, "skill": 2, "skip": 13, "skl": [2, 8, 23], "sklearn": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 23], "skplt": [9, 12], "sl": 8, "slack": 10, "slice": [4, 18, 23], "slide": [1, 2, 5, 20, 23], "slight": [8, 15], "slightli": [3, 4, 5, 7, 8, 9, 12, 20], "slope": [10, 13, 14], "slow": [2, 4, 10, 15], "slower": [7, 18, 23], "slowest": 18, "slowli": 14, "slp": 3, "small": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "smaller": [2, 3, 4, 7, 8, 10, 11, 13, 15, 20, 23], "smallest": [2, 6, 16, 23], "smallest_row_index": 16, "smooth": [2, 5, 8, 15, 23], "sn": [2, 3, 5, 8, 9, 23], "sne": 13, "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "soar": 8, "social": 2, "soft": [3, 9, 12, 14], "soften": 10, "softmax": [5, 9], "softwar": [2, 10, 17, 18], "sol": 10, "sole": [2, 8, 23], "solid": [2, 9], "solut": [2, 3, 4, 5, 7, 8, 10, 12, 13, 15, 18, 20, 23], "soluton": 4, "solv": [1, 2, 3, 5, 7, 8, 10, 12, 13, 14, 15, 18, 23], "solve_expdec": 4, "solve_ode_deep_neural_network": 4, "solve_ode_neural_network": 4, "solve_pde_deep_neural_network": 4, "solveod": 4, "solveode_popul": 4, "solver": [4, 9, 10, 11, 12, 18, 23], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "some_model": 8, "somehow": 6, "someon": 1, "someth": [0, 2, 3, 5, 6, 9, 11, 13, 20, 23], "sometim": [2, 3, 13, 14, 15, 16], "soon": [18, 21], "sophist": [2, 23], "sopt": 15, "sort": [7, 8, 11, 13, 20], "sound": [5, 7], "sourc": [2, 3, 5, 8, 17, 18, 20, 23], "space": [2, 3, 6, 7, 10, 11, 13, 14, 15, 16, 20], "span": [2, 5, 7, 11, 13, 18, 23], "spare": 3, "spars": [5, 8, 18, 23], "sparse_mtx": [18, 23], "sparsecategoricalcrossentropi": 5, "sparsiti": 12, "spatial": [3, 4, 5, 14], "speak": 20, "special": [8, 9, 12, 14, 15, 18, 20, 23], "specif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20, 22, 23], "specifi": [2, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "specifici": [2, 12, 23], "spectacular": 5, "spectral": 3, "speech": [2, 3, 5, 6, 14], "speed": [3, 4, 6, 15], "spend": [1, 20], "sphere": 2, "spin": 8, "spite": 2, "spline": 10, "split": [1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 20, 23], "splite": 2, "splitter": [3, 12], "spontan": 20, "spot": 5, "spread": [2, 13, 20, 23], "springer": [22, 23], "spuriou": 15, "sqrsignal": 5, "sqrt": [2, 5, 6, 7, 8, 10, 12, 13, 15, 20], "squar": [0, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 17, 18, 20], "squarederror": 12, "squaredeuclidean": 16, "squash": 14, "srtm": 8, "srtm_data_norway_1": 8, "stabil": 7, "stabl": [1, 2, 6, 7, 8, 11, 17, 23], "stack": [5, 6], "stage": [0, 7, 15], "stai": [2, 4, 6, 7, 13, 23], "stand": [2, 7, 11, 14, 23], "standard": [2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 20, 23], "standardscal": [2, 8, 9, 10, 11, 12, 13], "stanford": 15, "start": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "start_tim": 16, "stat": 8, "state": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "statement": [2, 9, 18, 23], "statist": [2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 16, 18, 22], "statu": [0, 2, 9, 23], "stavang": 8, "std": [2, 6, 8, 23], "steep": 15, "step": [0, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 16, 18, 23], "step_fn": [9, 14], "step_length": 15, "steps_list": 11, "stereo": 5, "still": [2, 4, 5, 7, 8, 13, 15, 20], "stimuli": 14, "stk": [22, 23], "stk2100": [22, 23], "stk3155": [0, 19, 21], "stk4021": [22, 23], "stk4051": [22, 23], "stk4155": [19, 21], "stk5000": 22, "stochast": [2, 3, 7, 8, 10, 13, 14, 23], "stock": 6, "stoke": 14, "stone": [2, 9], "stop": [3, 6, 11, 15, 16], "storag": 7, "store": [2, 3, 4, 5, 8, 13, 15, 20, 23], "storehaug": [21, 23], "str": [3, 5, 6], "straight": [2, 8, 10, 15, 23], "straightforward": [2, 4, 5, 7, 8, 10, 11, 12, 15, 18, 23], "strategi": [2, 3, 11, 23], "stratifi": 8, "strength": [2, 7, 16], "stretch": 13, "strict": [10, 15], "strictli": [10, 15], "stride": [6, 18], "strike": 8, "string": 3, "stroke": 9, "strong": [5, 8, 11, 12, 14, 18, 20], "strongli": [0, 2, 10, 17, 18], "stronli": 2, "structur": [2, 3, 4, 5, 8, 11, 12, 14, 17, 23], "stuck": [3, 15], "student": [0, 2, 19, 21, 22, 23], "studi": [2, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 22, 23], "studier": 22, "style": [2, 9, 11, 18, 23], "st\u00f8land": 21, "sub": [11, 14], "subdivid": [2, 18, 23], "subfield": 2, "subject": [8, 10, 20], "submit": 23, "subplot": [2, 3, 5, 6, 8, 9, 10, 11, 12, 16, 23], "subplots_adjust": [10, 20], "subprogram": [18, 23], "subract": 2, "subroutin": [2, 23], "subscript": 3, "subsequ": [3, 6, 7, 8, 14, 18, 20], "subset": [3, 8, 11, 14, 15, 17, 23], "subspac": [2, 10, 13], "substanti": [11, 12], "substep": 13, "substitut": [1, 5, 8, 14, 18], "subsubset": 11, "subtask": 8, "subtl": 3, "subtract": [2, 6, 7, 8, 13, 15, 18, 20], "subtre": 11, "succeed": [2, 6, 23], "success": [5, 9, 11, 15, 20], "successfulli": [6, 11], "sudo": [2, 17, 23], "suffer": [2, 3, 4, 7, 12, 23], "suffici": [3, 8, 10, 13, 15], "suggest": [3, 15, 22], "suit": [10, 14], "suitabl": [0, 2, 20], "sum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_i": [2, 4, 7, 8, 10, 15], "sum_j": 8, "sum_ja_": 2, "sum_k": [8, 10, 14, 18], "sum_logist": 15, "sum_m": 5, "sum_n": 5, "sum_nx_": 5, "summar": [7, 8, 11], "summari": [3, 5, 6, 12, 19], "summat": [1, 2, 5], "sunni": 11, "super": 7, "superfici": 5, "superscript": [3, 14], "supervis": [2, 7, 8, 9, 11, 14, 17, 23], "supplement": 9, "support": [2, 3, 11, 12, 13, 15, 17, 23], "suppos": [2, 7, 8, 9, 10, 12, 13, 14, 15, 18, 23], "suppress": [7, 15], "sure": [1, 2, 3, 6, 8], "surf": 8, "surfac": [2, 8, 23], "surpass": 8, "surpris": [2, 23], "surround": [5, 17], "survei": [2, 7, 8, 23], "svc": [10, 11, 12], "svd": [2, 8, 13, 23], "svdinv": 7, "svm": [10, 11, 12, 13], "svm_clf": [10, 12], "swath": 7, "switch": 2, "sy": 15, "symbol": [3, 7, 13, 15, 17, 20, 23], "symmeteri": 3, "symmetr": [2, 7, 10, 13, 14, 15, 18, 23], "symmetri": 8, "sympi": [2, 17, 23], "synonim": 20, "syntax": 15, "system": [0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 23], "systemat": [6, 8], "t": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "t0": [5, 8, 15], "t1": [4, 15], "t2": 4, "t3": 4, "t_": 4, "t_0": [4, 11, 15], "t_1": 15, "t_b": 12, "t_i": [3, 4, 7, 14], "t_j": 14, "t_k": 11, "tabl": [11, 20, 21, 23], "tabul": [2, 23], "tabular": 23, "tackl": 6, "tag": [4, 5, 6, 7, 8, 9, 14, 15, 16, 18, 20], "taht": [2, 23], "tail": 20, "tailor": [4, 10, 13, 23], "taiwan": [2, 23], "take": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "taken": [2, 3, 5, 8, 12, 15, 18], "tan": 5, "tangent": [3, 6, 14, 15], "tanh": [3, 6, 9, 10, 14], "target": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 23], "target_nam": 11, "task": [2, 3, 5, 8, 11, 13, 14, 16, 23], "tau": [5, 7, 20], "taught": 23, "tax": 2, "taylor": [4, 15], "taylornr": 15, "tc": 10, "teach": [0, 19, 23], "team": 3, "teaser": 2, "technic": [2, 7, 8, 15], "techniqu": [2, 3, 10, 12, 15, 17, 20, 22, 23], "technologi": [2, 3], "tell": [1, 2, 6, 8, 12, 13, 15, 20], "temp": 3, "temp1": 3, "temp2": 3, "temperatur": [2, 11, 23], "temporarili": 3, "ten": [5, 23], "tend": [5, 7, 8, 10, 11, 12, 14, 15, 16], "tendenc": [2, 23], "tension": 8, "tensor": 5, "tensorflow": [2, 4, 6, 10, 16, 17, 18, 22, 23], "term": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "term1": [7, 8, 13], "term2": [7, 8, 13], "term3": [7, 8, 13], "term4": [7, 8, 13], "termin": [0, 2, 6, 7, 11, 12, 15], "terrain": 8, "terrain1": 8, "test": [1, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 20, 23], "test_acc": 5, "test_accuraci": [3, 5], "test_error": 8, "test_imag": [5, 6], "test_ind": 8, "test_input": 6, "test_label": [5, 6], "test_loss": 5, "test_pr": 3, "test_predict": 3, "test_rnn": 6, "test_scor": [9, 12], "test_siz": [0, 2, 3, 5, 7, 8, 12], "test_split": 11, "testerror": [2, 8], "testi": 6, "testpredict": 6, "testx": 6, "text": [2, 3, 4, 6, 7, 10, 11, 13, 15, 18, 20, 22], "textbook": 1, "textual": 11, "textur": 3, "tf": [3, 5, 6, 15, 16], "th": [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 20, 23], "than": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20, 23], "thank": [6, 8], "theano": [3, 17, 23], "thei": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "them": [2, 3, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18, 23], "theme": [0, 2, 23], "themselv": [2, 20, 23], "thenc": 8, "theorem": [4, 8, 9], "theoret": [2, 6, 12], "theori": [2, 3, 5, 10, 11, 14, 15, 17, 22, 23], "thereaft": [2, 7, 8, 13, 14, 18, 23], "therebi": [2, 7, 9, 13, 23], "therefor": [2, 3, 4, 5, 6, 8, 9, 10, 13, 15, 20, 23], "therein": 13, "thereof": [2, 8, 15, 23], "theta": [3, 6, 15, 20, 23], "theta_": [3, 15, 23], "theta_0": 23, "theta_0x_": 23, "theta_1": 23, "theta_1x_": 23, "theta_1x_0": 23, "theta_1x_1": 23, "theta_1x_2": 23, "theta_2": 23, "theta_2x_": 23, "theta_2x_0": 23, "theta_2x_1": 23, "theta_2x_2": 23, "theta_i": [3, 23], "theta_j": 23, "theta_linreg": 15, "theta_t": 15, "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22], "thing": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 23], "think": [2, 3, 5, 6, 8, 11, 14, 15, 16, 20, 23], "third": [2, 5, 8, 15, 21, 23], "thirti": 9, "thorughout": 23, "those": [2, 5, 7, 8, 10, 11, 12, 13, 18, 23], "though": [1, 3, 4, 5, 6, 15, 18, 20], "thought": [8, 16, 20], "thousand": [2, 3], "three": [2, 3, 5, 7, 8, 10, 11, 14, 18, 19, 20, 21, 23], "threshold": [3, 5, 11, 12, 13, 14, 15], "through": [0, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 20, 23], "throughout": [0, 2, 6, 7, 16, 17, 18, 20, 23], "throw": [5, 8, 20], "thu": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 21, 23], "thumb": [2, 8], "tibshirani": [8, 22, 23], "tick_param": 8, "ticker": [8, 15, 20], "tif": 8, "tight_layout": [3, 9], "tightli": 13, "tild": [2, 7, 8, 9, 13, 20, 23], "till": [2, 6, 9, 10, 11, 12, 14, 18, 23], "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "timeit": 6, "timer": 6, "tini": 3, "tip": 5, "titl": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 20, 23], "tmp": 15, "tn": [4, 5, 9], "to_categor": [3, 5, 6], "to_categorical_numpi": 3, "to_numer": [2, 8, 23], "todai": 5, "togeth": [2, 5, 8, 10, 13, 15, 17, 23], "toi": 16, "told": 15, "toler": [4, 16], "tolist": 6, "tomographi": 14, "too": [2, 4, 6, 7, 8, 11, 13, 15, 20, 22], "took": [10, 23], "tool": [0, 2, 3, 5, 8, 15, 17], "toolbox": 10, "top": [2, 5, 7, 8, 11, 12, 17, 23], "topic": [2, 7, 8, 9, 10, 17], "topolog": [5, 14], "topologi": [3, 14], "torkjellsdatt": [21, 23], "toss": [12, 20], "total": [2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 21, 23], "total_loss": 6, "totalclustervari": 16, "totalscatt": 16, "toward": [0, 3, 4, 9, 14, 15], "town": 2, "tp": [6, 9], "tpng": 11, "tpu": [15, 17, 23], "tqdm": 8, "track": [0, 5, 15, 16, 18], "tract": 2, "tractabl": [2, 23], "trade": [7, 11], "tradeoff": [2, 7, 23], "tradit": [2, 3, 6, 8, 23], "train": [1, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15], "train_accuraci": [2, 3, 5, 23], "train_dataset": 6, "train_end": [2, 3], "train_error": 8, "train_imag": [5, 6], "train_ind": 8, "train_label": [5, 6], "train_pr": 3, "train_siz": [2, 3, 5], "train_step": 6, "train_test_split": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "train_test_split_numpi": [2, 3], "trainable_vari": 6, "trained_model": 8, "trainerror": 2, "traini": 6, "training_checkpoint": 6, "training_dataset": 6, "training_gradi": 15, "trainingerror": 8, "trainpredict": 6, "trainscor": 6, "trainx": 6, "trait": [2, 23], "trajectori": 6, "transfer": [11, 23], "transform": [2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "transit": [8, 14], "translat": [3, 6, 8, 12, 23], "transpos": [3, 7, 13, 18], "travers": [2, 7], "treat": [2, 3, 5, 8, 14, 15, 20, 23], "tree": [2, 3, 17, 23], "tree_clf": [11, 12], "tree_clf_": 11, "tree_clf_sr": 11, "tree_reg": 11, "tree_reg1": 11, "tree_reg2": 11, "trend": 20, "treue": 9, "trevor": 22, "tri": [1, 4, 5, 6, 11, 15], "triain": 2, "trial": [2, 4, 6, 8, 15, 20, 23], "triangl": 15, "triangular": 18, "trick": [5, 6, 10, 13, 15, 20], "trickier": 20, "tridiagon": 18, "trillion": 17, "trivial": [2, 3, 7, 13, 20, 23], "troubl": [0, 2, 10, 14], "truck": 5, "true": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "true_beta": 8, "true_fun": 8, "truli": 23, "try": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23], "tucker": 10, "tuesdai": [21, 23], "tumor": [9, 11], "tumour": 9, "tunabl": 3, "tune": [6, 11, 15, 18, 23], "turn": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "tutori": [3, 6], "tv": 4, "tveito": 4, "tweak": [3, 6, 12, 20], "twice": 15, "twist": 13, "two": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23], "tx": 15, "tx_1": 15, "txt": [0, 6], "ty": 15, "type": [2, 3, 5, 8, 10, 12, 15, 18, 20], "typic": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 20, 23], "u": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "u_": 18, "u_i": 14, "u_m": 12, "ua": [2, 23], "ubuntu": [2, 17, 23], "uci": 2, "uio": [0, 21, 22], "un": 16, "unabl": 0, "unari": [18, 23], "unbalanc": [8, 11], "unbias": [2, 7, 8, 23], "uncent": 8, "uncertainti": [2, 7, 23], "uncertitud": 20, "unchang": [3, 5], "uncorrel": [12, 20], "undefin": 7, "under": [2, 3, 7, 8, 12, 15, 17, 23], "underdetermin": [2, 23], "underfit": [3, 8], "underflowproblem": 7, "undergo": 7, "undergradu": [19, 21], "underli": [2, 3, 11, 15, 20, 23], "underset": [6, 16], "understand": [0, 2, 3, 5, 7, 8, 12, 15, 16, 17, 23], "understood": [10, 15], "undesir": 10, "undetermin": [7, 10], "undo": 6, "unexpect": 8, "unexpected": 20, "unfair": 8, "unfortun": [3, 10, 11, 12], "unicode_liter": [10, 11], "uniform": [2, 3, 7, 8, 13, 15, 20, 23], "uniformli": [15, 20], "unifrompdf": 20, "unimport": 15, "union": [7, 8], "uniqu": [2, 4, 8, 15, 16, 18, 23], "unique_cluster_label": 16, "unit": [2, 3, 5, 6, 7, 12, 14, 20, 23], "unitari": [7, 8, 18], "unitarili": [18, 23], "uniti": 20, "univari": 20, "univers": [2, 3, 4, 15, 17, 19, 21, 23], "unix": 3, "unknow": [2, 18, 23], "unknown": [2, 3, 5, 6, 7, 8, 10, 12, 15, 18, 23], "unknowwn": 14, "unlabel": 3, "unless": [2, 5, 8, 13, 15, 23], "unlik": [3, 5, 10, 15], "unnecessarili": 11, "unord": 5, "unravel": 3, "unrol": [5, 13], "unseen": [0, 2, 9, 11], "unstabl": 3, "unsupervis": [2, 3, 6, 14, 17, 23], "unsymmetr": [18, 23], "until": [3, 4, 6, 11, 14, 15, 16], "untouch": 2, "unusu": 14, "up": [1, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21], "updat": [0, 3, 4, 12, 14, 15, 16], "uploa": 23, "upload": [0, 17, 22], "upon": [2, 3, 8, 9, 13, 18], "upper": [1, 2, 10, 11, 18], "uppercas": [18, 23], "upsampl": 6, "upscal": 6, "url": 23, "us": [0, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 22], "usag": [2, 10, 17, 23], "usd": 2, "usd10000": 2, "use_bia": 6, "usecol": [2, 23], "useless": 3, "user": [2, 3, 4, 6, 8, 9, 17, 18, 23], "usernam": 0, "usetex": 20, "usg": 8, "usr": 20, "usual": [2, 5, 6, 9, 14, 15, 16, 23], "ut": 7, "util": [3, 5, 6, 8, 9, 12, 16, 23], "ux": 18, "v": [0, 2, 4, 6, 7, 8, 13, 15, 17], "v0": 20, "v1": 20, "v2": 20, "v_0": 13, "va": 3, "vahid": 23, "val": 15, "val_accuraci": 5, "val_loss": 6, "vale": 4, "valid": [2, 3, 6, 9, 11, 12, 15, 17, 20, 23], "validation_data": 5, "validation_split": 6, "valu": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23], "valuat": 11, "valy": 6, "van": [2, 23], "vandenbergh": [10, 15], "vandermond": [2, 23], "vanilla": [2, 8, 13, 16], "vanish": [3, 6, 15, 20], "var": [7, 8, 12, 13, 20], "var_x": 20, "varabl": 10, "varepsilon": [7, 8], "varepsilon_": [7, 8], "varepsilon_i": [7, 8], "vari": [2, 3, 5, 7, 8, 12, 23], "variabl": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 23], "varianc": [2, 3, 7, 9, 11, 12, 13, 15, 16, 17, 18, 20, 23], "variance_i": [7, 13], "variance_x": [7, 13], "variant": [2, 3, 8, 10, 14, 15, 23], "variat": [5, 6, 13, 23], "varieti": [2, 5, 14, 17, 23], "variou": [1, 3, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "varydimens": 6, "vastli": 5, "vaue": 3, "vault": 2, "vdot": [4, 15], "vec": 8, "vector": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17], "vector_mean": 16, "ventur": [2, 10, 17, 23], "venv": 0, "verbos": [3, 5, 6], "veri": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 22, 23], "verifi": [5, 13, 18, 23], "versatil": [10, 23], "versicolor": [10, 11], "version": [0, 2, 5, 12, 15, 16, 17, 18, 20, 23], "versu": 3, "vert": [1, 2, 3, 7, 8, 9, 10, 11, 13, 15, 23], "vert_1": [7, 8], "vert_2": [7, 8, 13], "via": [2, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 23], "vidal": 13, "video": [2, 3, 14, 17, 19, 21, 23], "view": [3, 5, 7, 8, 14, 15, 20, 22, 23], "violat": 10, "virginica": 11, "viridi": [2, 3, 4, 5, 23], "virtual": 3, "viscos": 15, "viscou": 15, "visibl": 0, "vision": [2, 5], "visual": [2, 5, 13, 14, 17, 23], "visualis": 3, "visualstudio": [0, 1], "viz": [8, 10, 20], "vmap": 15, "vmax": [3, 8], "vmin": [3, 8], "voic": 5, "volum": [2, 5, 23], "vote": [12, 23], "voting_clf": 12, "votingclassifi": 12, "votingsimpl": 12, "vstack": [7, 13, 18, 20, 23], "vt": 7, "w": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "w1": 10, "w2": [10, 13], "w3": 10, "w_": [3, 14], "w_1": [10, 18], "w_1x_": 10, "w_1x_1": 10, "w_2": [10, 18], "w_2x_": 10, "w_2x_2": 10, "w_3": 18, "w_4": 18, "w_hidden": 4, "w_i": [3, 4, 12], "w_ix_i": 14, "w_j": 18, "w_m": 18, "w_output": 4, "w_px_": 10, "w_px_p": 10, "wa": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 16, 18, 23], "wai": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "walk": 11, "walker": 20, "wang": [2, 23], "want": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "warn": [6, 23], "warrant": 8, "wast": 5, "watch": 17, "wave": 5, "wavelet": 10, "we": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "weak": [11, 12, 16], "weather": [3, 14], "web": [17, 19, 21, 23], "webpag": 23, "websit": [8, 18, 19, 23], "wedg": [10, 20], "wednesdai": [21, 23], "wee": 13, "week": [2, 7, 8, 9, 19, 21], "weekli": [0, 1, 17, 19, 21, 22, 23], "weekss": [], "weight": [2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 20], "weigth": 4, "welcom": [0, 10, 17], "well": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 22, 23], "went": 10, "were": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 20, 23], "wessel": [2, 23], "what": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "whatev": 5, "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "whenev": [0, 15, 20], "where": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "wherea": [8, 20], "wherein": [3, 14], "whether": [2, 5, 7, 9, 11, 20, 23], "which": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "whichev": [3, 5], "while": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20, 23], "white": 11, "who": [0, 2], "whole": [3, 5, 6, 7, 11, 13, 15], "whose": [2, 8, 12, 20], "whow": 13, "why": [0, 1, 2, 3, 5, 8, 15], "wide": [2, 3, 5, 8, 9, 14, 17, 18, 23], "widehat": 8, "width": [2, 5, 10, 11, 23], "wieringen": [2, 23], "win": 12, "wind": 11, "wing": [21, 23], "winther": 4, "wiothout": 8, "wiscons": 9, "wisconsin": 12, "wisdom": 8, "wise": [2, 3, 7, 14, 15], "wish": [2, 4, 7, 9, 10, 13, 15, 16, 18, 23], "with_std": 2, "wither": 8, "within": [2, 4, 5, 6, 9, 11, 14, 15, 16, 20, 22, 23], "withinclust": 16, "without": [0, 2, 3, 7, 8, 10, 11, 13, 14, 15, 23], "won": [0, 2, 23], "wonder": 10, "word": [2, 3, 5, 6, 7, 8, 9, 16, 20, 23], "work": [0, 1, 2, 3, 6, 8, 9, 10, 11, 15, 17, 19, 20, 21, 23], "workshop": 23, "world": [1, 2, 10], "worldwid": [2, 23], "worri": 0, "wors": [2, 3, 5, 6, 8, 23], "worth": 11, "would": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "wrap": [8, 18, 23], "write": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 18, 23], "written": [1, 2, 4, 5, 7, 13, 14, 15, 17, 18, 20, 23], "wrong": [0, 3, 10], "wrongli": 12, "wrote": [7, 13], "wrt": [12, 15], "wth": [12, 15], "www": [17, 18, 22, 23], "wx_1": 10, "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x0": 10, "x1": [6, 10, 11, 12, 15], "x1_exampl": 10, "x1d": 10, "x2": [10, 11, 12, 15], "x2d": [10, 13], "x2d_train": 13, "x2dsl": 13, "x3": 10, "x_": [2, 4, 5, 7, 8, 10, 12, 13, 15, 16, 18, 20, 23], "x_0": [2, 7, 13, 18, 23], "x_1": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_2": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_3": [10, 18, 20], "x_4": 18, "x_center": 13, "x_data": 3, "x_data_ful": 3, "x_hidden": 4, "x_i": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x_input": 4, "x_ix_": [2, 23], "x_iy_i": 10, "x_j": [1, 2, 4, 10, 11, 14, 20], "x_jy_j": 10, "x_k": [14, 16, 18, 20], "x_l": 20, "x_m": [8, 14, 18, 20], "x_n": [2, 4, 5, 8, 10, 13, 14, 15, 18, 20, 23], "x_new": [11, 12], "x_offset": 8, "x_output": 4, "x_p": [5, 9, 11], "x_poli": 11, "x_poly10": 11, "x_pred": 6, "x_prev": 4, "x_reduc": 13, "x_scale": 10, "x_small": 15, "x_test": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13], "x_test_own": 8, "x_test_scal": [2, 8, 9, 11, 12, 13], "x_tot": 6, "x_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "x_train_mean": 8, "x_train_own": 8, "x_train_scal": [2, 8, 9, 11, 12, 13], "x_val": 3, "xarrai": [17, 23], "xavier": 3, "xbnew": 15, "xcode": [2, 17, 23], "xdclassiffierconfus": 12, "xdclassiffierroc": 12, "xg_clf": 12, "xgb": 12, "xgbclassifi": 12, "xgboost": 11, "xgboot": 12, "xgbregressor": 12, "xgparam": 12, "xgtree": 12, "xi": [10, 15], "xi_": 10, "xi_1": 10, "xi_i": 10, "xk": 10, "xla": [15, 17, 23], "xlabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "xlim": [8, 12], "xm": 11, "xmesh": 15, "xnew": [2, 15, 23], "xp": 20, "xpanda": 2, "xpd": [7, 13], "xplot": 2, "xscale": 2, "xsr": 11, "xt_x": 15, "xtest": 8, "xtick": [5, 8, 10, 11], "xtrain": 8, "xu": [2, 23], "xx": [2, 18, 23], "xy": [2, 8, 10, 18, 23], "xytext": 10, "xz": [18, 23], "y": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "y1": 6, "y2": 6, "y3": 6, "y_": [2, 3, 7, 8, 12, 13, 18, 23], "y_0": [2, 7, 13, 18, 23], "y_1": [2, 7, 10, 11, 13, 15, 18, 23], "y_1y_1": 10, "y_1y_1k": 10, "y_1y_2": 10, "y_1y_2k": 10, "y_1y_n": 10, "y_1y_nk": 10, "y_2": [2, 7, 10, 11, 13, 18, 23], "y_2y_1": 10, "y_2y_1k": 10, "y_2y_2": 10, "y_2y_2k": 10, "y_3": [2, 11, 18], "y_4": 18, "y_data": [2, 3, 7, 8, 23], "y_data_ful": 3, "y_decis": 10, "y_fit": 2, "y_i": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "y_if_": 12, "y_ix_": [2, 23], "y_ix_i": [9, 10, 15], "y_iy_jk": 10, "y_j": [8, 10, 14], "y_k": 14, "y_m": 18, "y_model": [2, 6, 7, 8, 23], "y_n": [10, 15], "y_ny_1": 10, "y_ny_1k": 10, "y_ny_2": 10, "y_ny_2k": 10, "y_ny_n": 10, "y_ny_nk": 10, "y_offset": 8, "y_plot": 11, "y_pred": [2, 3, 6, 8, 9, 10, 11, 12], "y_pred1": 11, "y_pred2": 11, "y_pred_rf": 12, "y_pred_tre": 12, "y_proba": [9, 12], "y_scaler": 8, "y_test": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13], "y_test_onehot": 3, "y_test_predict": 2, "y_tot": 6, "y_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_train_mean": 8, "y_train_onehot": 3, "y_train_predict": 2, "y_train_scal": 8, "y_val": 3, "ye": [5, 8, 9], "year": [2, 17, 23], "yet": [2, 3, 8, 10, 13, 15, 23], "yi": 15, "yield": [2, 4, 7, 8, 10, 12, 14, 15, 16, 18, 20, 23], "yk": 10, "ylabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "ylim": [5, 8], "ym": 11, "ymesh": 15, "yn": 2, "yo": [10, 11, 12], "yoshua": [3, 22], "you": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 21, 22, 23], "young": 2, "your": [0, 3, 4, 6, 7, 8, 10, 13, 15, 17, 18, 23], "your_model_object": 1, "yourself": [13, 15, 23], "youtub": 17, "ypred": 8, "ypredict": [2, 15, 23], "ypredict2": 15, "ypredictlasso": 7, "ypredictol": [2, 7], "ypredictown": 8, "ypredictownridg": 8, "ypredictridg": [2, 7, 8], "ypredictskl": 8, "ytest": 8, "ytick": [5, 8, 10, 11], "ytild": [2, 8, 23], "ytildelasso": 7, "ytildenp": [2, 23], "ytildeol": [2, 7], "ytildeownridg": 8, "ytilderidg": [7, 8], "ytrain": 8, "yuxi": 23, "yx": [18, 23], "yy": [18, 23], "yz": [18, 23], "z": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "z_": [3, 4, 14, 18, 23], "z_0": [18, 23], "z_1": [18, 23], "z_2": [18, 23], "z_c": 3, "z_h": 3, "z_hidden": 4, "z_i": [3, 14], "z_j": [3, 14], "z_k": 14, "z_m": 3, "z_mod": 11, "z_o": 3, "z_output": 4, "zaman": 20, "zaxi": 8, "zero": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "zeros_lik": 6, "zfill": 6, "zip": [6, 8], "zm_h": [2, 23], "zn": 2, "zone": 2, "zoom": 23, "zx": [18, 23], "zy": [18, 23], "zz": [18, 23], "\u00f8yvind": 8}, "titles": ["Exercises week 34", "Exercises week 35", "3. Linear Regression", "14. Building a Feed Forward Neural Network", "15. Solving Differential Equations with Deep Learning", "16. Convolutional Neural Networks", "17. Recurrent neural networks: Overarching view", "4. Ridge and Lasso Regression", "5. Resampling Methods", "6. Logistic Regression", "8. Support Vector Machines, overarching aims", "9. Decision trees, overarching aims", "10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "11. Basic ideas of the Principal Component Analysis (PCA)", "13. Neural networks", "7. Optimization, the central part of any Machine Learning algortithm", "12. Clustering and Unsupervised Learning", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Course setting", "1. Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities"], "titleterms": {"": [10, 12], "1": [0, 1, 2], "2": [0, 1, 2, 23], "2023": 21, "3": [0, 1, 2], "34": [0, 23], "35": 1, "4": [0, 1, 2], "5": [1, 2], "A": [2, 3, 6, 10, 11, 23], "And": 23, "In": 21, "Ising": 8, "The": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 17, 23], "To": 23, "With": 6, "about": 23, "activ": [3, 14], "ad": [2, 8, 23], "adaboost": 12, "adagrad": 15, "adam": 15, "adapt": 12, "adjust": 3, "adversari": 6, "again": [5, 11], "ai": 23, "aim": [10, 11, 23], "aka": 23, "algebra": [18, 23], "algorithm": [11, 12, 13, 14, 23], "algortithm": 15, "all": 10, "an": [0, 2, 6, 12, 23], "analys": 7, "analysi": [2, 7, 8, 13, 17, 20, 23], "analyt": [1, 2], "ani": 15, "anoth": 11, "appli": 17, "approach": [2, 10, 16, 23], "approxim": 14, "architectur": 3, "arrai": [18, 23], "assist": 21, "autocorrel": 20, "autograd": [4, 15], "automat": 15, "back": [3, 13, 14], "background": 17, "bag": 12, "base": 15, "basic": [2, 7, 9, 11, 12, 13, 18], "batch": 3, "bay": 7, "befor": 13, "better": 10, "bia": 8, "binari": 3, "bind": 23, "bird": 12, "boost": 12, "bootstrap": [8, 12], "boston": 2, "breast": 3, "brief": 23, "bring": 14, "build": [3, 5, 11], "c": 23, "can": 23, "cancer": [3, 9, 11, 13], "cart": 11, "case": [10, 12, 20], "central": [15, 17, 20], "chain": 14, "chang": 12, "channel": 23, "chi": [2, 23], "choos": 3, "cifar01": 5, "classic": 13, "classif": [3, 11, 12], "classifi": 10, "clip": 3, "cluster": 16, "cnn": 5, "code": [0, 1, 2, 3, 4, 7, 11, 13, 14, 15, 16, 23], "collect": [3, 5], "commun": 23, "compar": [1, 4, 12], "complex": [2, 8], "complic": 8, "compon": 13, "comput": 11, "computerlab": 23, "con": 11, "concept": 20, "conjug": 15, "contn": 23, "convex": [10, 15], "convolut": [5, 14], "correl": 13, "cost": [3, 12], "cours": [17, 19, 22, 23], "covari": [7, 13, 20], "cover": 23, "creat": 1, "cross": 8, "cython": 23, "data": [0, 2, 3, 5, 8, 9, 11, 13, 17, 20, 23], "dataset": [3, 5], "david": 23, "deadlin": 23, "deadllin": 21, "decai": 4, "decis": [11, 12], "decomposit": [7, 13, 18], "deeep": 23, "deep": [3, 4, 23], "defin": [3, 23], "degre": 2, "deliver": [0, 1], "dens": 2, "deriv": [1, 7, 14], "descent": [4, 12, 15], "detail": [5, 23], "develop": 3, "diagon": 13, "differ": 10, "differenti": [4, 15], "diffus": 4, "dimension": [4, 5, 10], "disadvantag": 11, "discret": 20, "discrimin": 23, "distribut": [7, 20], "do": 3, "domain": 20, "down": 3, "dropout": 3, "element": [2, 20, 23], "elimin": 18, "energi": 23, "ensembl": 12, "entropi": 11, "environ": [0, 2], "equat": [2, 4, 14], "error": [2, 12, 23], "essenti": 23, "etc": 23, "euler": 4, "evalu": 3, "exampl": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "exercis": [0, 1, 2, 8], "expect": 20, "experi": 20, "explor": 2, "exponenti": 4, "express": 1, "extrapol": 6, "extrem": [12, 23], "ey": 12, "fall": 21, "famili": [3, 23], "famou": 18, "featur": [1, 11, 18], "feed": [3, 14], "final": 14, "find": 1, "fine": 3, "first": [6, 14, 23], "fit": [0, 1, 2, 12, 23], "forc": 5, "forest": 12, "format": 23, "forward": [3, 4, 14], "foster": 23, "fourier": 5, "frank": 8, "freedom": 2, "frequentist": [2, 23], "from": [7, 12, 14, 23], "full": 4, "function": [2, 3, 8, 9, 10, 12, 13, 14, 15, 20, 23], "further": [5, 7], "gan": 6, "gaussian": 18, "gd": 15, "gener": [6, 11, 23], "geometr": 13, "gini": 11, "github": 0, "goal": [0, 1], "good": [2, 23], "grade": [21, 23], "gradient": [3, 4, 12, 15], "growth": 4, "ha": 17, "handl": [18, 23], "hidden": 4, "hous": 2, "how": 1, "hyperparamet": 3, "hyperplan": 10, "i": [2, 3, 23], "id3": 11, "idea": 13, "ii": 23, "implement": [1, 3], "implic": 7, "import": [7, 18, 23], "improv": 3, "includ": 15, "increment": 13, "index": 11, "inform": 21, "input": 4, "instal": [17, 23], "instructor": 21, "interpret": [7, 13, 23], "introduc": [13, 15], "introduct": [2, 8, 17, 18, 23], "invers": [7, 18], "iter": 12, "jax": 15, "julia": 23, "jungl": 12, "kera": [3, 5], "kernel": [10, 13], "lagrangian": 10, "lasso": [7, 8], "later": 7, "layer": [3, 4, 5, 14], "learn": [0, 1, 2, 3, 4, 13, 15, 16, 17, 23], "least": [1, 7, 8, 23], "lectur": 23, "level": 12, "librari": [17, 23], "likelihood": 9, "limit": [3, 15, 20], "linear": [0, 2, 10, 15, 18, 23], "link": [7, 13, 22], "logist": [9, 23], "lu": 18, "machin": [2, 10, 15, 17, 23], "main": [20, 23], "make": [2, 11, 12], "mani": [12, 14], "mass": 23, "materi": 23, "math": 7, "mathemat": [5, 7, 10], "matric": [7, 18, 23], "matrix": [1, 3, 7, 13, 14, 18, 23], "matter": 2, "mean": 2, "meet": [7, 12, 20, 23], "mercer": 10, "method": [8, 11, 12, 15, 23], "minim": 23, "ml": 23, "mlp": 14, "mnist": [5, 6], "model": [0, 2, 3, 6, 8, 14, 23], "momentum": 15, "moon": [10, 11], "more": [5, 8, 18, 23], "multilay": 14, "multipl": [3, 5], "multipli": 10, "need": 23, "network": [3, 4, 5, 6, 9, 14, 23], "neural": [3, 4, 5, 6, 9, 14, 23], "new": 6, "non": 10, "normal": [2, 3], "notat": 14, "now": [3, 11, 15], "nuclear": [2, 23], "numba": 23, "number": [2, 4, 20], "numer": [4, 20], "numpi": [18, 23], "object": 5, "obtain": 13, "od": 4, "off": 8, "ol": [0, 1, 7, 8], "one": [4, 14], "oper": 18, "optim": [3, 10, 15, 17, 23], "order": 15, "ordinari": [1, 7, 8, 23], "organ": [2, 23], "oslo": 22, "other": [6, 11, 13, 14, 18, 23], "our": [2, 6, 7, 13, 15, 23], "outcom": [17, 23], "output": 4, "overarch": [2, 6, 10, 11, 23], "overview": [12, 23], "own": [2, 12, 13, 23], "packag": [18, 23], "panda": 23, "paramet": 23, "part": [15, 17], "partial": 4, "pass": 3, "pca": 13, "pdf": 20, "perceptron": 14, "perform": [3, 11], "period": 5, "perspect": 3, "plethora": 23, "point": 6, "poisson": 4, "polynomi": [1, 5], "popul": 4, "popular": 23, "practic": [15, 21, 23], "pre": [3, 5], "predict": 6, "prerequisit": [5, 17, 23], "princip": 13, "principl": 5, "pro": 11, "probabl": [7, 20], "problem": [3, 4, 15, 23], "procedur": [11, 23], "process": [3, 5], "program": [4, 15], "project": [8, 21, 23], "prop": 15, "propag": [3, 14], "properti": [7, 20], "python": [0, 2, 11, 17, 18, 23], "quick": 10, "r": 23, "random": [12, 13, 20], "read": [11, 23], "real": [8, 23], "recommend": 23, "recurr": [6, 14], "reduc": 2, "reduct": 5, "reformul": 4, "regress": [0, 2, 7, 8, 9, 11, 12, 15, 23], "regular": 3, "relev": 22, "relu": 3, "remark": 5, "remind": [8, 10, 23], "replac": 15, "repositori": 0, "requir": [4, 17], "resampl": 8, "rescal": 8, "resourc": 4, "revisit": 15, "rewrit": 23, "ridg": [2, 7, 8], "rm": 15, "rule": 14, "same": 15, "sampl": 13, "schedul": 23, "schemat": 11, "scheme": 4, "scienc": 23, "scikit": [2, 3, 13, 23], "second": 15, "semest": 21, "set": [0, 2, 4, 5, 11, 14, 19, 23], "setup": 0, "sgd": 15, "should": 3, "similar": 15, "simpl": [2, 6, 11, 15, 23], "singl": 12, "singular": [7, 13], "sklearn": 1, "soft": 10, "softmax": 3, "softwar": 23, "solv": 4, "solver": 15, "some": [15, 18], "specifi": 4, "split": [0, 2], "squar": [1, 2, 7, 8, 12, 23], "standard": 15, "state": 2, "statist": [7, 8, 17, 20, 23], "steepest": [12, 15], "stochast": [15, 20], "strongli": 23, "suggest": 23, "summari": [21, 23], "superposit": 5, "supervis": 3, "support": 10, "svd": 7, "systemat": 5, "take": 1, "taken": 23, "teach": 21, "teacher": [21, 23], "techniqu": [8, 13], "technologi": 17, "tensorflow": [3, 5], "tent": [21, 23], "test": [0, 2, 3], "text": 23, "textbook": [22, 23], "theorem": [7, 10, 13, 14, 20], "theori": 20, "thi": 23, "tip": 15, "togeth": 14, "tool": 23, "top": 3, "topic": 23, "toward": 13, "trade": 8, "tradeoff": 8, "train": [0, 2, 3, 6, 23], "transform": 5, "tree": [11, 12], "tune": 3, "two": [5, 10, 17], "type": [4, 6, 14, 23], "uio": 23, "univers": [14, 22], "unsupervis": 16, "up": [0, 2, 4, 11, 14, 23], "us": [1, 2, 3, 4, 5, 9, 15, 17, 23], "v": 5, "valid": 8, "valu": [7, 13, 20], "variabl": 20, "varianc": 8, "variou": 2, "vector": [1, 10, 14, 18, 23], "versu": 23, "view": [2, 6, 12], "virtual": 0, "visual": [3, 11], "wai": 11, "wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12]}}) \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/E1.ipynb b/doc/LectureNotes/_build/jupyter_execute/E1.ipynb index a53aabbdf..989dc907a 100644 --- a/doc/LectureNotes/_build/jupyter_execute/E1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/E1.ipynb @@ -19,7 +19,7 @@ "\n", "In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants. \n", "\n", - "If you are unable to complete this weekss exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." + "If you are unable to complete this week's exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight." ] }, {