added video

doc/LectureNotes/_build/html/week35.html

@@ -1118,6 +1118,7 @@ doconce format html week35.do.txt --no_mako -->
 <ol class="simple">
 <li><p>These lecture notes</p></li>
 <li><p><a class="reference external" href="https://youtu.be/VKakN-e4aUA">Video of lecture</a></p></li>
+<li><p><a class="reference external" href="https://youtu.be/yiY0OltU1s8">Video for exercises week 35</a></p></li>
 <li><p>Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</p></li>
 <li><p>Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</p></li>
 <li><p>For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL”<a class="reference external" href="https://mml-book.github.io/">https://mml-book.github.io/</a>” (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</p></li>
@@ -1597,7 +1598,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9960987015666213
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9955853547770925
 </pre></div>
 </div>
 </div>
@@ -1614,7 +1615,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.008479177639949449
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.009975924157146357
 </pre></div>
 </div>
 </div>
@@ -1629,31 +1630,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[3.33897624e-02 2.82933065e-02 2.08502578e-02 5.70368882e-03
- 1.36392181e-02 3.54890204e-02 4.52322923e-02 7.20183951e-02
- 6.08424143e-03 1.51876722e-02 2.72107224e-02 4.38536455e-02
- 1.01890271e-02 7.59583924e-03 1.54362677e-02 1.39857639e-02
- 3.04600349e-02 5.35392690e-02 6.14211764e-03 2.44308477e-03
- 1.74218636e-03 1.36163844e-02 3.15915417e-02 6.07182377e-04
- 5.90558936e-03 5.35227195e-05 9.78647442e-03 1.26624586e-02
- 1.45101677e-02 1.92012445e-02 5.05837221e-02 6.17112259e-03
- 1.11723198e-02 6.93356993e-03 1.93438490e-02 1.33978089e-02
- 7.08793873e-03 2.86963555e-02 3.74845516e-02 9.09026386e-03
- 9.83959386e-03 1.47179272e-02 2.28687236e-03 1.79501684e-02
- 4.29334886e-02 5.70888990e-03 1.77605945e-03 8.68241292e-03
- 9.19704684e-02 1.94558442e-02 6.13073296e-03 4.87955713e-03
- 2.61004589e-02 2.48914361e-02 1.68762810e-02 5.66037582e-02
- 3.32605607e-02 2.20330004e-02 8.80858500e-02 5.40907614e-02
- 6.99537250e-03 2.13619518e-02 2.92595981e-02 2.13400535e-02
- 4.74734301e-03 7.13396439e-03 2.94193224e-03 1.28749016e-02
- 1.04702436e-01 2.25969618e-02 4.31600436e-03 4.04787888e-02
- 7.73538998e-03 2.21569425e-02 1.89182406e-02 1.89215419e-03
- 1.06170091e-02 1.90932454e-02 1.33876558e-02 9.18004907e-03
- 3.59120619e-02 4.23663774e-03 4.75744838e-02 5.05958988e-03
- 7.91845464e-04 3.83995681e-02 1.29557246e-02 4.35758612e-02
- 3.74194699e-03 2.05752451e-02 1.29311961e-02 7.07147609e-03
- 5.43495641e-03 9.07930457e-03 9.59668773e-04 2.80566926e-03
- 3.14738036e-02 1.34254673e-02 4.80114766e-02 5.31124303e-02]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.03310948 0.03502605 0.01238528 0.01145325 0.01120753 0.04024121
+ 0.02365946 0.00802057 0.01686313 0.06417556 0.02459689 0.03750064
+ 0.00027126 0.04470158 0.02703163 0.07640212 0.0372278  0.12977735
+ 0.00607227 0.05519101 0.00506541 0.01080061 0.01466418 0.0235557
+ 0.0039114  0.01903413 0.00530754 0.00284682 0.00234831 0.00575843
+ 0.00288898 0.00864249 0.03421216 0.0238318  0.02846355 0.05133809
+ 0.02410477 0.00618364 0.00616064 0.05455225 0.01761095 0.00756235
+ 0.02465031 0.01140481 0.00569631 0.04057615 0.0102482  0.06849452
+ 0.02568311 0.0038013  0.02460166 0.00968573 0.00510049 0.03368503
+ 0.0275728  0.01629911 0.03063595 0.00953303 0.04675468 0.04944357
+ 0.00768229 0.00974873 0.02744664 0.0039037  0.02755583 0.00960513
+ 0.00340126 0.02270334 0.03343177 0.01023188 0.01071707 0.0053717
+ 0.00989298 0.02147188 0.01733341 0.02393489 0.09413086 0.00693229
+ 0.01547395 0.00893513 0.01198614 0.00880861 0.05151828 0.01074123
+ 0.02611675 0.04174576 0.02879514 0.04182729 0.05830426 0.02412449
+ 0.03064238 0.00449104 0.02332052 0.04607275 0.06390074 0.02795048
+ 0.0056416  0.00463706 0.0240445  0.02081809]
 </pre></div>
 </div>
 </div>
@@ -1722,15 +1715,15 @@ but now splitting the data into a training set and a test set.</p>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.9493303   0.64347371  2.56535839  3.65129524 -1.82028236]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.90133797  0.41151358  5.10853105 -1.67354637  1.25212002]
 Training R2
-0.9968948714928851
+0.9943899533894374
 Training MSE
-0.007373820933373729
+0.012961500458019132
 Test R2
-0.9971066204281798
+0.9940852985232707
 Test MSE
-0.009252005542369688
+0.012176386103148444
 </pre></div>
 </div>
 </div>
@@ -2411,7 +2404,9 @@ Feature min values after scaling:
  -0.88613493 -0.884669   -0.88323026 -0.88182591 -0.75269037 -0.75050135
  -0.74829661 -0.74607851 -0.74384949 -0.6652177  -0.66294408 -0.66064822
  -0.65833132 -0.65599456 -0.6536392 ]
-Feature max values after scaling:
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Feature max values after scaling:
  [0.         1.71737253 1.75576555 2.20916295 2.23971032 2.26995402
  2.60543038 2.63162342 2.65743689 2.68286725 2.94273542 2.96631321
  2.98947894 3.01222822 3.034557   3.24159785 3.26297455 3.28391978

doc/LectureNotes/_build/jupyter_execute/week35.py

@@ -30,11 +30,13 @@
 # 
 # 2. [Video of lecture](https://youtu.be/VKakN-e4aUA)
 # 
-# 3. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
+# 3. [Video for exercises week 35](https://youtu.be/yiY0OltU1s8)
 # 
-# 4. Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
+# 4. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
 # 
-# 5. For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
+# 5. Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
+# 
+# 6. For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
 
 # ## For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week
 #