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Morten Hjorth-Jensen
2024-11-23 22:44:33 +01:00
parent c3fac15bef
commit af7737872c
6 changed files with 271 additions and 303 deletions
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doc/pub/week48/html/week48-reveal.html
+24 -37
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@@ -243,7 +243,7 @@ MathJax.Hub.Config({
<p>The algorithm described here can be applied to both classification and regression problems.</p>
<p>We will grow of forest of say \( B \) trees.</p>
<ol>
<ul>
<p><li> For \( b=1:B \)
<ol type="a"></li>
<p><li> Draw a bootstrap sample from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
@@ -259,8 +259,9 @@ MathJax.Hub.Config({
<p>
</ol>
<p>
<p><li> Output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem.</li>
</ol>
</ul>
<p>
<p>Finally we output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem. </p>
</section>
<section>
@@ -1361,8 +1362,7 @@ plt.show()
<section>
<h2 id="machine-learning">Machine learning </h2>
<p>The following topics will be covered</p>
<ol>
<ul>
<p><li> Linear methods for regression and classification:
<ol type="a"></li>
<p><li> Ordinary Least Squares</li>
@@ -1393,7 +1393,7 @@ plt.show()
<p><li> Regression</li>
</ol>
<p>
</ol>
</ul>
</section>
<section>
@@ -1468,17 +1468,17 @@ Neural Networks, etc.</a>
<section>
<h2 id="choose-a-model-and-algorithm">Choose a Model and Algorithm </h2>
<ol>
<ul>
<p><li> Supervised?</li>
<p><li> Start with the simplest model that fits your problem</li>
<p><li> Start with minimal processing of data</li>
</ol>
</ul>
</section>
<section>
<h2 id="preparing-your-data">Preparing Your Data </h2>
<ol>
<ul>
<p><li> Shuffle your data</li>
<p><li> Mean center your data</li>
<ul>
@@ -1500,51 +1500,38 @@ Neural Networks, etc.</a>
<p><li> Can be hit or miss</li>
</ul>
<p>
<p><li> When to do train/test split?</li>
</ol>
<p><li> When to do train/test split?</li>
</ul>
</section>
<section>
<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
<ol>
<ul>
<p><li> RELU? ELU? GELU? etc</li>
<p><li> Sigmoid or Tanh?</li>
<p><li> Set all weights to 0?</li>
<ul>
<p><li> Terrible idea</li>
<p><li> Set all weights to 0? Terrible idea</li>
<p><li> Set all weights to random values? Small random values</li>
</ul>
<p>
<p><li> Set all weights to random values?</li>
<ul>
<p><li> Small random values</li>
</ul>
<p>
</ol>
</section>
<section>
<h2 id="optimization-methods-and-hyperparameters">Optimization Methods and Hyperparameters </h2>
<ol>
<p><li> Stochastic gradient descent
<ol type="a"></li>
<p><li> Stochastic gradient descent + momentum</li>
</ol>
<p>
<p><li> State-of-the-art approaches:</li>
<ul>
<p><li> RMSProp</li>
<p><li> Adam</li>
<p><li> and more</li>
<p><li> Stochastic gradient descent</li>
<ul>
<p><li> Stochastic gradient descent + momentum</li>
</ul>
<p>
<p><li> State-of-the-art approaches:
<ol type="a"></li>
<p><li> RMSProp</li>
<p><li> Adam</li>
<p><li> and more</li>
</ol>
<p>
</ul>
<p>
<p>Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft
classifiers, depths of trees and many other. Need to explore a large
set of hyperparameters and regularization methods.
doc/pub/week48/html/week48-solarized.html
+23 -29
View File
@@ -353,7 +353,7 @@ MathJax.Hub.Config({
<p>The algorithm described here can be applied to both classification and regression problems.</p>
<p>We will grow of forest of say \( B \) trees.</p>
<ol>
<ul>
<li> For \( b=1:B \)
<ol type="a"></li>
<li> Draw a bootstrap sample from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
@@ -364,8 +364,9 @@ MathJax.Hub.Config({
<li> split the node into daughter nodes</li>
</ol>
</ol>
<li> Output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem.</li>
</ol>
</ul>
<p>Finally we output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem. </p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="random-forests-compared-with-other-methods-on-the-cancer-data">Random Forests Compared with other Methods on the Cancer Data </h2>
@@ -1377,8 +1378,7 @@ plt.show()
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="machine-learning">Machine learning </h2>
<p>The following topics will be covered</p>
<ol>
<ul>
<li> Linear methods for regression and classification:
<ol type="a"></li>
<li> Ordinary Least Squares</li>
@@ -1405,7 +1405,7 @@ plt.show()
<li> Kernel methods</li>
<li> Regression</li>
</ol>
</ol>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="learning-outcomes-and-overarching-aims-of-this-course">Learning outcomes and overarching aims of this course </h2>
@@ -1470,15 +1470,15 @@ Neural Networks, etc.</a>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="choose-a-model-and-algorithm">Choose a Model and Algorithm </h2>
<ol>
<ul>
<li> Supervised?</li>
<li> Start with the simplest model that fits your problem</li>
<li> Start with minimal processing of data</li>
</ol>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="preparing-your-data">Preparing Your Data </h2>
<ol>
<ul>
<li> Shuffle your data</li>
<li> Mean center your data</li>
<ul>
@@ -1493,37 +1493,31 @@ Neural Networks, etc.</a>
<li> Decorrelates data</li>
<li> Can be hit or miss</li>
</ul>
<li> When to do train/test split?</li>
</ol>
<li> When to do train/test split?</li>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
<ol>
<ul>
<li> RELU? ELU? GELU? etc</li>
<li> Sigmoid or Tanh?</li>
<li> Set all weights to 0?</li>
<ul>
<li> Terrible idea</li>
<li> Set all weights to 0? Terrible idea</li>
<li> Set all weights to random values? Small random values</li>
</ul>
<li> Set all weights to random values?</li>
<ul>
<li> Small random values</li>
</ul>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="optimization-methods-and-hyperparameters">Optimization Methods and Hyperparameters </h2>
<ol>
<li> Stochastic gradient descent
<ol type="a"></li>
<li> Stochastic gradient descent + momentum</li>
</ol>
<li> State-of-the-art approaches:</li>
<ul>
<li> RMSProp</li>
<li> Adam</li>
<li> and more</li>
<li> Stochastic gradient descent</li>
<ul>
<li> Stochastic gradient descent + momentum</li>
</ul>
<li> State-of-the-art approaches:
<ol type="a"></li>
<li> RMSProp</li>
<li> Adam</li>
<li> and more</li>
</ol>
</ul>
<p>Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft
classifiers, depths of trees and many other. Need to explore a large
set of hyperparameters and regularization methods.
doc/pub/week48/html/week48.html
+23 -29
View File
@@ -430,7 +430,7 @@ MathJax.Hub.Config({
<p>The algorithm described here can be applied to both classification and regression problems.</p>
<p>We will grow of forest of say \( B \) trees.</p>
<ol>
<ul>
<li> For \( b=1:B \)
<ol type="a"></li>
<li> Draw a bootstrap sample from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
@@ -441,8 +441,9 @@ MathJax.Hub.Config({
<li> split the node into daughter nodes</li>
</ol>
</ol>
<li> Output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem.</li>
</ol>
</ul>
<p>Finally we output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem. </p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="random-forests-compared-with-other-methods-on-the-cancer-data">Random Forests Compared with other Methods on the Cancer Data </h2>
@@ -1454,8 +1455,7 @@ plt<span style="color: #666666">.</span>show()
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="machine-learning">Machine learning </h2>
<p>The following topics will be covered</p>
<ol>
<ul>
<li> Linear methods for regression and classification:
<ol type="a"></li>
<li> Ordinary Least Squares</li>
@@ -1482,7 +1482,7 @@ plt<span style="color: #666666">.</span>show()
<li> Kernel methods</li>
<li> Regression</li>
</ol>
</ol>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="learning-outcomes-and-overarching-aims-of-this-course">Learning outcomes and overarching aims of this course </h2>
@@ -1547,15 +1547,15 @@ Neural Networks, etc.</a>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="choose-a-model-and-algorithm">Choose a Model and Algorithm </h2>
<ol>
<ul>
<li> Supervised?</li>
<li> Start with the simplest model that fits your problem</li>
<li> Start with minimal processing of data</li>
</ol>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="preparing-your-data">Preparing Your Data </h2>
<ol>
<ul>
<li> Shuffle your data</li>
<li> Mean center your data</li>
<ul>
@@ -1570,37 +1570,31 @@ Neural Networks, etc.</a>
<li> Decorrelates data</li>
<li> Can be hit or miss</li>
</ul>
<li> When to do train/test split?</li>
</ol>
<li> When to do train/test split?</li>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
<ol>
<ul>
<li> RELU? ELU? GELU? etc</li>
<li> Sigmoid or Tanh?</li>
<li> Set all weights to 0?</li>
<ul>
<li> Terrible idea</li>
<li> Set all weights to 0? Terrible idea</li>
<li> Set all weights to random values? Small random values</li>
</ul>
<li> Set all weights to random values?</li>
<ul>
<li> Small random values</li>
</ul>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="optimization-methods-and-hyperparameters">Optimization Methods and Hyperparameters </h2>
<ol>
<li> Stochastic gradient descent
<ol type="a"></li>
<li> Stochastic gradient descent + momentum</li>
</ol>
<li> State-of-the-art approaches:</li>
<ul>
<li> RMSProp</li>
<li> Adam</li>
<li> and more</li>
<li> Stochastic gradient descent</li>
<ul>
<li> Stochastic gradient descent + momentum</li>
</ul>
<li> State-of-the-art approaches:
<ol type="a"></li>
<li> RMSProp</li>
<li> Adam</li>
<li> and more</li>
</ol>
</ul>
<p>Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft
classifiers, depths of trees and many other. Need to explore a large
set of hyperparameters and regularization methods.
doc/pub/week48/ipynb/ipynb-week48-src.tar.gz
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doc/pub/week48/ipynb/week48.ipynb
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