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diff --git a/doc/LectureNotes/_build/html/exercisesweek43.html b/doc/LectureNotes/_build/html/exercisesweek43.html
index 75ff3d0ad..25a596f47 100644
--- a/doc/LectureNotes/_build/html/exercisesweek43.html
+++ b/doc/LectureNotes/_build/html/exercisesweek43.html
@@ -1066,6 +1066,7 @@ to use it. Before we get into the details of the neural network, we
 will first present some implementations of various schedulers, cost
 functions and activation functions that can be used together with the
 neural network.</p>
+<p>The codes here were developed by Eric Reber and Gregor Kajda during spring 2023.</p>
 <div class="section" id="learning-rate-methods">
 <h3>Learning rate methods<a class="headerlink" href="#learning-rate-methods" title="Permalink to this headline">¶</a></h3>
 <p>The code below shows object oriented implementations of the Constant,
@@ -5358,12 +5359,8 @@ case.</p>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.001, Lambda=0
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
+
+  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41],"5":0,"7":34,"9":26,"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41],"import":[5,21,25,32,33,34],"new":[4,34,35],A:[0,1,4,8,9,21,32,34,35,36,40,41],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41],To:[32,33],With:[4,34],about:[32,33],abov:[23,34],activ:[1,12,23,27,34,38,39,40,41],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:8,an:[0,4,10,32],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37],bring:[12,39],build:[1,3,9,23,40,41],c:[26,27,32],calcul:33,can:[21,32,35,37,38],cancer:[1,7,9,11,36,40,41],cart:9,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:3,classic:11,classif:[1,9,10,23,27,36,39,40],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41],collect:[1,3,39,40,41],come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],construct:[39,40,41],continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39],correctli:[34,35],correl:[11,33,36],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41],dataset:[1,3,39,40,41],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,dens:[0,32],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,26,33,41],directli:[37,38],disadvantag:9,discret:29,discuss:36,distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:29,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:32,euler:[2,41],evalu:[1,27,39,40,41],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:4,extrem:[10,32],ey:10,f:[26,27],fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],find:[35,37,38],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flow:40,fold:[35,36],forc:3,forest:10,format:[26,27,32],forward:[1,2,12,38,39,40,41],fourier:3,frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39],full:[2,39,40,41],funtion:[40,41],further:[3,5,33],g:26,gan:4,gate:[23,38,39,40,41],gaussian:25,gd:[13,21,37,38],gener:[4,9,32],geometr:[11,36,37],get:21,gini:9,good:[0,32],goodfellow:21,grade:[30,32],gradient:[1,2,10,13,21,27,36,37,38,40,41],grid:36,group:36,growth:[2,41],ha:24,handl:[25,32,33],happen:[34,35],have:32,heard:32,hessian:[33,36,37],hidden:[2,40,41],histogram:35,homework:[36,37],hous:[0,33],how:36,hyperbol:[38,39],hyperparamet:[1,39,40,41],hyperplan:8,i:[1,40,41],id3:9,idea:11,ide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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41],"5":0,"7":34,"9":26,"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41],"import":[5,21,25,32,33,34],"new":[4,34,35],A:[0,1,4,8,9,21,32,34,35,36,40,41],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41],To:[32,33],With:[4,34],about:[32,33],abov:[23,34],activ:[1,12,23,27,34,38,39,40,41],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:8,an:[0,4,10,32],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37],bring:[12,39],build:[1,3,9,23,40,41],c:[26,27,32],calcul:33,can:[21,32,35,37,38],cancer:[1,7,9,11,36,40,41],cart:9,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:3,classic:11,classif:[1,9,10,23,27,36,39,40],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41],collect:[1,3,39,40,41],come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],construct:[39,40,41],continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39],correctli:[34,35],correl:[11,33,36],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41],dataset:[1,3,39,40,41],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,dens:[0,32],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,26,33,41],directli:[37,38],disadvantag:9,discret:29,discuss:36,distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:29,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:32,euler:[2,41],evalu:[1,27,39,40,41],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:4,extrem:[10,32],ey:10,f:[26,27],fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],find:[35,37,38],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flow:40,fold:[35,36],forc:3,forest:10,format:[26,27,32],forward:[1,2,12,38,39,40,41],fourier:3,frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39],full:[2,39,40,41],funtion:[40,41],further:[3,5,33],g:26,gan:4,gate:[23,38,39,40,41],gaussian:25,gd:[13,21,37,38],gener:[4,9,32],geometr:[11,36,37],get:21,gini:9,good:[0,32],goodfellow:21,grade:[30,32],gradient:[1,2,10,13,21,27,36,37,38,40,41],grid:36,group:36,growth:[2,41],ha:24,handl:[25,32,33],happen:[34,35],have:32,heard:32,hessian:[33,36,37],hidden:[2,40,41],histogram:35,homework:[36,37],hous:[0,33],how:36,hyperbol:[38,39],hyperparamet:[1,39,40,41],hyperplan:8,i:[1,40,41],id3:9,idea:11,ideal:[36,37],ident:[34,35],identifi:35,ii:[32,41],iid:[34,35],iii:41,illustr:[34,38,39],implement:[1,39,40,41],implic:[5,33],improv:[1,37,39,40,41],includ:[13,21,36,37,38],increment:11,independ:[34,35],index:9,inform:30,input:[2,41],instal:[24,26,32],instructor:30,intercept:33,interpret:[5,11,32,33,34,35,36,37],interv:35,introduc:[11,13,21,33,37,38],introduct:[0,6,24,25,26,27,32,38,39,40],invers:[5,25,34],invert:33,iter:[10,37],its:33,iv:41,jacobian:[33,41],jax:[13,21,37,38],job:[38,39],julia:32,jungl:10,k:[35,36],kera:[1,3,40,41],kernel:[8,11],l:39,lagrangian:8,lambda:36,lasso:[5,6,26,33,34,35,36],last:[33,35,36,38],later:[5,33],layer:[1,2,3,12,39,40,41],learn:[0,1,2,11,13,14,15,16,21,23,24,26,27,32,33,34,35,36,37,38,39,40,41],least:[5,6,18,26,32,33,34],lectur:[32,33,34,35,36,39,40,41],level:10,librari:[24,32],likelihood:[7,34,35,36],limit:[1,13,29,35,36,37,38,40,41],linear:[0,8,13,25,27,32,33,34,36],link:[5,11,28,31,33,34,35],literatur:[26,27],logist:[7,27,32,36,37,38,39,40,41],loop:[37,38],loss:[33,36,37],lu:25,machin:[0,8,13,24,26,27,32,36,37],made:[34,35],mai:32,main:29,make:[0,9,10,15,16,32,33],mani:[10,12,39],manipul:33,margin:[34,35],mass:32,materi:[28,32,33,34,35,36,41],math:[5,33],mathemat:[3,5,8,33,37,38,39],matric:[5,25,32,34],matrix:[1,5,11,12,25,32,33,34,36,37,38,39,40,41],matter:[0,32],max:33,maximum:[34,35,36],mean:[0,32,33,34,36],measur:36,meet:[5,10,29,32,33],mercer:8,method:[6,9,10,13,21,23,26,35,36,37,38],midnight:[26,27],min:33,mini:[37,38],minibatch:[21,37,38],minim:[32,36,41],ml:32,mle:[34,35],mlp:[12,39],mnist:[3,4],model:[0,1,4,6,12,32,38,39,40,41],moment:[37,38],momentum:[13,21,37,38],moon:[8,9],more:[3,6,21,25,26,32,33,34,35,36,37,38,40,41],multi:[38,39,40],multiclass:23,multilay:[12,38,39],multipl:[1,3,39,40,41],multipli:8,need:[26,32],network:[1,2,3,4,7,12,23,27,32,36,38,39,40,41],neural:[1,2,3,4,7,12,23,27,32,38,39,40,41],neuron:[38,39],newton:[21,36,37,38],nice:[40,41],noen:[21,37,38],non:8,normal:[0,1,35,40,41],notat:[12,38,39],note:[21,33,34,35],novemb:27,now:[1,9,13,21,34,35,37,38,40,41],nuclear:[0,32],nueral:36,numba:32,number:[0,2,21,29,33,37,38,41],numer:[2,26,27,29,41],numpi:[21,25,32,37,38],object:[3,39,40,41],obtain:11,octob:[26,39,40,41],od:[2,41],off:[6,26],ol:[5,6,21,26,34,35,37,38],one:[2,12,36,37,39,41],ones:[38,39],oper:25,optim:[1,8,13,24,32,33,36,37,38,39,40,41],order:[13,21,37,38],ordinari:[5,6,18,26,32,33,34,41],organ:[0,32],orient:[39,40,41],oslo:31,other:[4,9,11,12,25,32,33,36,38,39],our:[0,4,5,11,13,32,33,36,37,39,40,41],outcom:[24,32],output:[2,40,41],overarch:[0,4,8,9,17,18,19,20,21,22,23,32,33],overview:[10,32,37,38],own:[0,10,11,15,16,27,32,33],packag:[25,32],panda:[32,33],paper:26,paramet:[32,33,36,37,38],part:[13,24,26,27,33,36,37],partial:[2,41],pass:[1,39,40,41],pca:11,pdf:29,pencil:26,perceptron:[12,38,39,40],perform:[1,9,39,40,41],period:3,perspect:[1,40,41],plan:[33,34,35,36,37,38,39,40,41],plot:[35,36],point:4,poisson:[2,41],polynomi:[3,34],popul:[2,41],possibl:41,practic:[13,21,30,32,37,38],pre:[1,3,39,40,41],predict:4,predictor:36,preprocess:33,prerequisit:[3,24,32],princip:11,principl:3,pro:9,probabl:[5,29,34,35],problem:[1,2,13,21,32,33,34,36,37,38,39,40,41],procedur:[9,32],process:[1,3,39,40,41],product:[37,38],program:[2,13,26,27,36,37,41],project:[6,26,27,32,36],prop:[13,37,38],propag:[1,12,39,40,41],properti:[5,29,33,36],python:[0,9,15,24,25,32],quick:8,r:32,random:[10,11,29,36],raphson:[36,37],rate:[21,23,37,38],read:[9,32,33,35],real:[6,26,32],recommend:[32,33,37,38],rectangular:34,recurr:[4,12,38,39],recurs:[37,38],reduc:[0,33],reduct:3,reformul:[2,41],regress:[0,5,6,7,9,10,13,17,18,26,27,32,33,34,35,36,37,38,39],regular:[1,36,39,40,41],relat:33,relev:[31,33,35,36,38,39],relu:[1,40,41],remark:3,remind:[6,8,32,36,37],repeat:33,replac:[13,37,38],report:[26,27],repositori:35,repres:[23,39,40,41],requir:[2,24,41],resampl:[6,26,34,35],rescal:[6,34],residu:33,resourc:[2,41],result:[33,34],review:40,revisit:[13,36,37],rewrit:[32,33,35],rewritten:36,ridg:[0,5,6,17,18,26,33,34,35,36,37],rm:[13,37,38],rmsprop:[21,37,38],rule:[12,39],s:[8,10,21,35,36,37,38],same:[13,21,35,37,38],sampl:11,scale:[33,35],schedul:[23,28,32],schemat:9,scheme:[2,41],scienc:32,scikit:[0,1,11,15,16,23,32,33,34,35,36,37,39,40,41],search:36,second:[13,21,37,38],select:36,semest:30,sensit:[36,37],septemb:[34,35,36],session:34,set:[0,2,3,9,12,15,23,32,33,36,39,40,41],setup:41,sgd:[13,37,38],should:[1,40,41],sigmoid:[40,41],similar:[13,21,37,38],simpl:[0,4,9,13,32,33,34,36,37,38],singl:[10,38,39],singular:[5,11,17,33],size:33,slightli:[37,38],soft:8,softmax:[1,39,40],softwar:[26,32],solut:41,solv:[2,34,36,37,41],solver:13,some:[13,25,32,33,36,37],specif:41,specifi:[2,41],split:[0,15,16,32,33],squar:[0,5,6,10,18,26,32,33,34],standard:[13,33,35,37],start:[21,38],state:[0,32],statement:32,statist:[5,6,24,29,32,34,35],steepest:[10,13,36,37],step:[27,35,36,37,38],still:33,stochast:[13,21,27,29,37,38],stop:[37,38],strongli:32,studi:36,subtract:33,suggest:32,sum:35,summari:[30,32,38],superposit:3,supervis:[1,40,41],support:8,svd:[5,33,34],syntax:[37,38],systemat:3,t:[33,34],taken:21,teach:[28,30],teacher:[30,32],technic:[34,41],techniqu:[6,11,26,34],technolog:24,tensor:40,tensorflow:[1,3,40,41],tent:32,term:[35,39],test:[0,1,15,16,27,32,33,34,39,40,41],textbook:[31,32],than:[36,37],thei:32,theorem:[5,8,11,12,29,34,35,39],theori:29,thi:[17,18,19,20,21,22,32],think:33,thursdai:[33,34,35,36,39,40,41],time:[37,38],tip:[13,21,37,38],togeth:[12,39],tool:32,top:[1,40,41],topic:32,toward:11,trade:[6,26],tradeoff:[6,35],train:[0,1,4,15,16,32,33,39,40,41],transform:3,tree:[9,10],trial:41,tuesdai:34,tune:[1,40,41],two:[3,8,24,26,33,36],type:[2,4,12,32,38,39,41],uio:32,understand:35,univers:[12,31,39],unsupervis:14,unsupport:[37,38],up:[0,2,9,12,15,21,23,32,33,35,36,39,40,41],us:[0,1,2,3,7,13,21,23,24,27,32,33,36,37,38,39,40,41],usag:[23,34,35],valid:[6,26,35,36],valu:[5,11,17,18,29,33,34,35,36],vari:[21,37,38],variabl:[29,36,37],varianc:[6,26,34,35],variou:[0,15,27,32,35,36],vector:[8,12,25,32,33,38,39],veri:[40,41],video:[37,38],view:[0,4,10,33],visual:[1,9,39,40,41],vs:3,wai:[9,21,35],warm:21,wave:[2,41],we:[21,32,37,38,40,41],websit:[40,41],wednesdai:34,week:[15,16,17,18,19,20,21,22,23,32,33,34,35,36,37,38,39,40,41],weekend:36,weekli:[28,35],weight:[39,40,41],what:[0,32,33,34,35],when:[37,38],which:[1,21,37,38,40,41],why:[32,33,35,38,39,41],wisconsin:[7,36],wrap:[33,35],write:[4,11,27,34,41],x:[33,34],xgboost:10,xor:[23,38,39,40,41],yet:34,you:32,your:[0,10,15,16,27,32,33],yourself:36,z_j:39}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
index 3c953b05b..ed2e18e86 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "60ef4ca3",
+   "id": "b2937d10",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6584f2f2",
+   "id": "3dd00d19",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5f930b9",
+   "id": "82a19a1d",
    "metadata": {
     "editable": true
    },
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9d8df6c",
+   "id": "f74f69af",
    "metadata": {
     "editable": true
    },
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "925a54fa",
+   "id": "1b52d47a",
    "metadata": {
     "editable": true
    },
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f3c8e67",
+   "id": "3e6910cb",
    "metadata": {
     "editable": true
    },
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66ebe4a2",
+   "id": "90a3b78a",
    "metadata": {
     "editable": true
    },
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "454750aa",
+   "id": "d6a3ab1e",
    "metadata": {
     "editable": true
    },
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "8308d3ec",
+   "id": "152123b0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bef962e6",
+   "id": "73319f0a",
    "metadata": {
     "editable": true
    },
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70caa854",
+   "id": "a7e0c47a",
    "metadata": {
     "editable": true
    },
@@ -288,7 +288,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a1711478",
+   "id": "dbbacc67",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd9d7329",
+   "id": "1cac501a",
    "metadata": {
     "editable": true
    },
@@ -611,12 +611,14 @@
     "to use it. Before we get into the details of the neural network, we\n",
     "will first present some implementations of various schedulers, cost\n",
     "functions and activation functions that can be used together with the\n",
-    "neural network."
+    "neural network.\n",
+    "\n",
+    "The codes here were developed by Eric Reber and Gregor Kajda during spring 2023."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8b9664dc",
+   "id": "dd153528",
    "metadata": {
     "editable": true
    },
@@ -638,7 +640,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "97ba7a9a",
+   "id": "f55eea63",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -779,7 +781,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1b3d6d1",
+   "id": "1a9bcb3e",
    "metadata": {
     "editable": true
    },
@@ -795,7 +797,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "21b4f017",
+   "id": "86013cb4",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -808,7 +810,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaf81efe",
+   "id": "535331f6",
    "metadata": {
     "editable": true
    },
@@ -820,7 +822,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3464a296",
+   "id": "7e0f6b5a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -858,7 +860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d40c1673",
+   "id": "f018ae57",
    "metadata": {
     "editable": true
    },
@@ -874,7 +876,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c230465c",
+   "id": "c13507bf",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -912,7 +914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b38deb02",
+   "id": "6dab17bc",
    "metadata": {
     "editable": true
    },
@@ -925,7 +927,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9a17387b",
+   "id": "a5dbba01",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -957,7 +959,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dcdaa478",
+   "id": "b55d31d4",
    "metadata": {
     "editable": true
    },
@@ -973,7 +975,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "902e7512",
+   "id": "b3e045a6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1031,7 +1033,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77182d43",
+   "id": "c0189342",
    "metadata": {
     "editable": true
    },
@@ -1046,7 +1048,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "2723829d",
+   "id": "640aa861",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1088,7 +1090,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc6f43b6",
+   "id": "1007ccdd",
    "metadata": {
     "editable": true
    },
@@ -1112,7 +1114,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e9a07867",
+   "id": "9584a2da",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1584,7 +1586,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f957b537",
+   "id": "9ccd1fc1",
    "metadata": {
     "editable": true
    },
@@ -1596,7 +1598,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "97018c57",
+   "id": "7f3a5b31",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1640,7 +1642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c46a864f",
+   "id": "1ac05bb6",
    "metadata": {
     "editable": true
    },
@@ -1656,7 +1658,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "da5c2906",
+   "id": "0f857604",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1671,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec8e4fce",
+   "id": "eeff4315",
    "metadata": {
     "editable": true
    },
@@ -1682,7 +1684,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "9e63ffa2",
+   "id": "46246810",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2515,7 +2517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39cf3c39",
+   "id": "8c6f9954",
    "metadata": {
     "editable": true
    },
@@ -2531,7 +2533,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "a50c2400",
+   "id": "2661939c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -10563,7 +10565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59e3767e",
+   "id": "74c5624c",
    "metadata": {
     "editable": true
    },
@@ -10578,7 +10580,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c77d9ebd",
+   "id": "4b8eb115",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -10604,7 +10606,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "7a872564",
+   "id": "2c0f92bd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -10619,7 +10621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc5ad007",
+   "id": "49201ae4",
    "metadata": {
     "editable": true
    },
@@ -10630,7 +10632,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "fac34043",
+   "id": "55b5e426",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -10640,20 +10642,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adam: Eta=0.001, Lambda=0"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Adam: Eta=0.001, Lambda=0\n",
       "\r",
       "  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
      ]
@@ -18676,7 +18665,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a581657",
+   "id": "b51762fb",
    "metadata": {
     "editable": true
    },
@@ -18687,7 +18676,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "3421ca31",
+   "id": "6b59e27d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -18707,7 +18696,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "bb6f3c50",
+   "id": "72c87921",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -26746,7 +26735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e719f18",
+   "id": "ed40d7d2",
    "metadata": {
     "editable": true
    },
@@ -26761,7 +26750,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "bba5ad80",
+   "id": "315ef3fe",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.py b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.py
index 60b03d0a7..f8b543d5e 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.py
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.py
@@ -257,6 +257,8 @@ plt.show()
 # will first present some implementations of various schedulers, cost
 # functions and activation functions that can be used together with the
 # neural network.
+# 
+# The codes here were developed by Eric Reber and Gregor Kajda during spring 2023.
 
 # ### Learning rate methods
 # 
diff --git a/doc/LectureNotes/exercisesweek43.ipynb b/doc/LectureNotes/exercisesweek43.ipynb
index e5f90421a..438de41c2 100644
--- a/doc/LectureNotes/exercisesweek43.ipynb
+++ b/doc/LectureNotes/exercisesweek43.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "60ef4ca3",
+   "id": "b2937d10",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6584f2f2",
+   "id": "3dd00d19",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5f930b9",
+   "id": "82a19a1d",
    "metadata": {
     "editable": true
    },
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9d8df6c",
+   "id": "f74f69af",
    "metadata": {
     "editable": true
    },
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "925a54fa",
+   "id": "1b52d47a",
    "metadata": {
     "editable": true
    },
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f3c8e67",
+   "id": "3e6910cb",
    "metadata": {
     "editable": true
    },
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66ebe4a2",
+   "id": "90a3b78a",
    "metadata": {
     "editable": true
    },
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "454750aa",
+   "id": "d6a3ab1e",
    "metadata": {
     "editable": true
    },
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "8308d3ec",
+   "id": "152123b0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bef962e6",
+   "id": "73319f0a",
    "metadata": {
     "editable": true
    },
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70caa854",
+   "id": "a7e0c47a",
    "metadata": {
     "editable": true
    },
@@ -276,7 +276,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a1711478",
+   "id": "dbbacc67",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -344,7 +344,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd9d7329",
+   "id": "1cac501a",
    "metadata": {
     "editable": true
    },
@@ -356,12 +356,14 @@
     "to use it. Before we get into the details of the neural network, we\n",
     "will first present some implementations of various schedulers, cost\n",
     "functions and activation functions that can be used together with the\n",
-    "neural network."
+    "neural network.\n",
+    "\n",
+    "The codes here were developed by Eric Reber and Gregor Kajda during spring 2023."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8b9664dc",
+   "id": "dd153528",
    "metadata": {
     "editable": true
    },
@@ -383,7 +385,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "97ba7a9a",
+   "id": "f55eea63",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -524,7 +526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1b3d6d1",
+   "id": "1a9bcb3e",
    "metadata": {
     "editable": true
    },
@@ -540,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "21b4f017",
+   "id": "86013cb4",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -553,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaf81efe",
+   "id": "535331f6",
    "metadata": {
     "editable": true
    },
@@ -565,7 +567,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3464a296",
+   "id": "7e0f6b5a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -587,7 +589,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d40c1673",
+   "id": "f018ae57",
    "metadata": {
     "editable": true
    },
@@ -603,7 +605,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c230465c",
+   "id": "c13507bf",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -641,7 +643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b38deb02",
+   "id": "6dab17bc",
    "metadata": {
     "editable": true
    },
@@ -654,7 +656,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9a17387b",
+   "id": "a5dbba01",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -675,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dcdaa478",
+   "id": "b55d31d4",
    "metadata": {
     "editable": true
    },
@@ -691,7 +693,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "902e7512",
+   "id": "b3e045a6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -749,7 +751,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77182d43",
+   "id": "c0189342",
    "metadata": {
     "editable": true
    },
@@ -764,7 +766,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "2723829d",
+   "id": "640aa861",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -785,7 +787,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc6f43b6",
+   "id": "1007ccdd",
    "metadata": {
     "editable": true
    },
@@ -809,7 +811,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e9a07867",
+   "id": "9584a2da",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1281,7 +1283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f957b537",
+   "id": "9ccd1fc1",
    "metadata": {
     "editable": true
    },
@@ -1293,7 +1295,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "97018c57",
+   "id": "7f3a5b31",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1337,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c46a864f",
+   "id": "1ac05bb6",
    "metadata": {
     "editable": true
    },
@@ -1353,7 +1355,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "da5c2906",
+   "id": "0f857604",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1368,7 +1370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec8e4fce",
+   "id": "eeff4315",
    "metadata": {
     "editable": true
    },
@@ -1379,7 +1381,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "9e63ffa2",
+   "id": "46246810",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1394,7 +1396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39cf3c39",
+   "id": "8c6f9954",
    "metadata": {
     "editable": true
    },
@@ -1410,7 +1412,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "a50c2400",
+   "id": "2661939c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1424,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59e3767e",
+   "id": "74c5624c",
    "metadata": {
     "editable": true
    },
@@ -1439,7 +1441,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c77d9ebd",
+   "id": "4b8eb115",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1465,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "7a872564",
+   "id": "2c0f92bd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1480,7 +1482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc5ad007",
+   "id": "49201ae4",
    "metadata": {
     "editable": true
    },
@@ -1491,7 +1493,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "fac34043",
+   "id": "55b5e426",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1506,7 +1508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a581657",
+   "id": "b51762fb",
    "metadata": {
     "editable": true
    },
@@ -1517,7 +1519,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "3421ca31",
+   "id": "6b59e27d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1537,7 +1539,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "bb6f3c50",
+   "id": "72c87921",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1552,7 +1554,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e719f18",
+   "id": "ed40d7d2",
    "metadata": {
     "editable": true
    },
@@ -1567,7 +1569,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "bba5ad80",
+   "id": "315ef3fe",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/src/week43/exercisesweek43.do.txt b/doc/src/week43/exercisesweek43.do.txt
index 4373f7eb7..6650ba3aa 100644
--- a/doc/src/week43/exercisesweek43.do.txt
+++ b/doc/src/week43/exercisesweek43.do.txt
@@ -239,6 +239,8 @@ will first present some implementations of various schedulers, cost
 functions and activation functions that can be used together with the
 neural network.
 
+The codes here were developed by Eric Reber and Gregor Kajda during spring 2023.
+
 === Learning rate methods  ===
 
 The code below shows object oriented implementations of the Constant,