week41 exercises small changes
This commit is contained in:
KarlHenrik
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tags: 645f666f9bcd5a90fca523b33c5a78b7
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<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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<ul class="nav bd-sidenav">
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+12
@@ -232,6 +232,18 @@
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<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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<ul class="nav bd-sidenav">
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+12
@@ -232,6 +232,18 @@
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<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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<ul class="nav bd-sidenav">
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+12
@@ -232,6 +232,18 @@
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<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
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<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
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|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
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|
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|
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
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|
||||
|
||||
|
||||
|
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
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|
||||
|
||||
|
||||
|
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
||||
|
||||
|
||||
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
||||
|
||||
|
||||
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
||||
|
||||
|
||||
|
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
||||
|
||||
|
||||
|
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
||||
|
||||
|
||||
|
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
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|
||||
|
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|
||||
|
||||
|
||||
|
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</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
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|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -234,6 +234,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
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|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
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|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
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|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
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|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
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|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
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|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
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||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
+12
@@ -232,6 +232,18 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../exercisesweek38.html">Exercises week 38</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../exercisesweek39.html">Exercises week 39</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week39.html">Week 39: Resampling methods and logistic regression</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../../../../../exercisesweek41.html">Exercises week 41</a></li>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
|
||||
@@ -327,7 +327,7 @@
|
||||
"id": "0da7fd52",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**d)** Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer?\n"
|
||||
"**d)** Why is a neural network with no activation functions mathematically equivelent to(can be reduced to) a neural network with only one layer?\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -454,7 +454,7 @@
|
||||
"id": "a6349db6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**b)** Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)\n"
|
||||
"**b)** Make a matrix of inputs with the shape (number of inputs, number of features), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -480,7 +480,7 @@
|
||||
"id": "efd07b4e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**c)** Create and evaluate a neural network with 4 inputs and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax.\n"
|
||||
"**c)** Create and evaluate a neural network with 4 input features, and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax.\n"
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||||
]
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|
||||
<p><strong>b)</strong> Compute the mean square error for the line model and for the second degree polynomial model.</p>
|
||||
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|
||||
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|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">PolynomialFeatures</span> <span class="c1"># use the fit_transform method of the created object!</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">PolynomialFeatures</span> <span class="c1"># use the fit_transform method of the created object!</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span>
|
||||
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|
||||
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|
||||
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|
||||
@@ -532,7 +532,7 @@ document.write(`
|
||||
<p>Hopefully your model fit the data quite well, but to know how well the model actually generalizes to unseen data, which is most often what we care about, we need to split our data into training and testing data.</p>
|
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||||
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||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
|
||||
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|
||||
<p>We calculate the optimal intercept by including a feature with the constant value of 1 in our model, which is then multplied by some parameter <span class="math notranslate nohighlight">\(\theta_0\)</span> from the OLS method into the optimal intercept value (which will be <span class="math notranslate nohighlight">\(\theta_0\)</span>). In practice, we include the intercept in our model by adding a column of ones to the start of our feature matrix.</p>
|
||||
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|
||||
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|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
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|
||||
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|
||||
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|
||||
@@ -556,7 +556,7 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
|
||||
<p><strong>b)</strong> Use the expression from <strong>3d)</strong> to find the optimal parameters <span class="math notranslate nohighlight">\(\boldsymbol{\hat{\beta}_{OLS}}\)</span> for predicting spending based on these features. Create a function for this operation, as you are going to need to use it a lot.</p>
|
||||
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|
||||
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|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">OLS_parameters</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">OLS_parameters</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="o">...</span>
|
||||
|
||||
<span class="c1">#beta = OLS_parameters(X, y)</span>
|
||||
@@ -581,7 +581,7 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
|
||||
<p><strong>a)</strong> Create a feature matrix <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span> for the features <span class="math notranslate nohighlight">\(x, x^2, x^3, x^4, x^5\)</span>, including an intercept column of ones at the start. Make this into a function, as you will do this a lot over the next weeks.</p>
|
||||
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|
||||
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|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">polynomial_features</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">polynomial_features</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="c1">#X[:, 0] = ...</span>
|
||||
@@ -605,7 +605,7 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
|
||||
<p><strong>c)</strong> Like in exercise 4 last week, split your feature matrix and target data into a training split and test split.</p>
|
||||
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|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
|
||||
|
||||
<span class="c1">#X_train, X_test, y_train, y_test = ...</span>
|
||||
</pre></div>
|
||||
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||||
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@@ -479,10 +479,10 @@ defining a new cost function to be optimized, that is</p>
|
||||
<h2>Exercise 3 - Scaling data<a class="headerlink" href="#exercise-3-scaling-data" title="Link to this heading">#</a></h2>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -499,7 +499,7 @@ defining a new cost function to be optimized, that is</p>
|
||||
<p><strong>a)</strong> Adapt your function from last week to only include the intercept column if the boolean argument <code class="docutils literal notranslate"><span class="pre">intercept</span></code> is set to true.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">polynomial_features</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">polynomial_features</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="c1">#X[:, 0] = ...</span>
|
||||
@@ -512,7 +512,7 @@ defining a new cost function to be optimized, that is</p>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">polynomial_features</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">polynomial_features</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">))</span>
|
||||
<span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:]</span>
|
||||
@@ -558,7 +558,7 @@ defining a new cost function to be optimized, that is</p>
|
||||
<p><strong>a)</strong> Implement a function for computing the optimal Ridge parameters using the expression from <strong>2a)</strong>.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">Ridge_parameters</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">Ridge_parameters</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="c1"># Assumes X is scaled and has no intercept column</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X</span><span class="p">)</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y</span>
|
||||
|
||||
|
||||
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Block a user