updated can notes
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mhjensen
@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
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<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
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<meta name="description" content="Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning and convolutional networks">
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<meta name="description" content="Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning">
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<title>Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning and convolutional networks</title>
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<title>Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning</title>
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@@ -141,65 +141,7 @@ Automatically generated HTML file from DocOnce source
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('Limitations of supervised learning with deep networks',
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('Convolutional Neural Networks (recognizing images)',
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('3D volumes of neurons', 2, None, '___sec68'),
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('Layers used to build CNNs', 2, None, '___sec69'),
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('Systematic reduction', 2, None, '___sec77'),
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('More on GD and cost function', 2, None, '___sec96'),
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('An implementation of a Deep Neural Network',
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end of tocinfo -->
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<body>
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@@ -229,7 +171,7 @@ MathJax.Hub.Config({
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<span class="icon-bar"></span>
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<span class="icon-bar"></span>
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</button>
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<a class="navbar-brand" href="NeuralNet-bs.html">Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning and convolutional networks</a>
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<a class="navbar-brand" href="NeuralNet-bs.html">Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning</a>
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</div>
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<div class="navbar-collapse collapse navbar-responsive-collapse">
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@@ -303,44 +245,6 @@ MathJax.Hub.Config({
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs064.html#___sec63" style="font-size: 80%;"><b>Which activation function should we use?</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs065.html#___sec64" style="font-size: 80%;"><b>A top-down perspective on Neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="#___sec65" style="font-size: 80%;"><b>Limitations of supervised learning with deep networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs067.html#___sec66" style="font-size: 80%;"><b>Convolutional Neural Networks (recognizing images)</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs068.html#___sec67" style="font-size: 80%;"><b>Regular NNs don’t scale well to full images</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs069.html#___sec68" style="font-size: 80%;"><b>3D volumes of neurons</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs070.html#___sec69" style="font-size: 80%;"><b>Layers used to build CNNs</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs071.html#___sec70" style="font-size: 80%;"><b>Transforming images</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs072.html#___sec71" style="font-size: 80%;"><b>CNNs in brief</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs073.html#___sec72" style="font-size: 80%;"><b>CNNs in more detail, building convolutional neural networks in Tensorflow and Keras</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs074.html#___sec73" style="font-size: 80%;"><b>Setting it up</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs075.html#___sec74" style="font-size: 80%;"><b>The MNIST dataset again</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs076.html#___sec75" style="font-size: 80%;"><b>Strong correlations</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs077.html#___sec76" style="font-size: 80%;"><b>Layers of a CNN</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs078.html#___sec77" style="font-size: 80%;"><b>Systematic reduction</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs079.html#___sec78" style="font-size: 80%;"><b>Prerequisites: Collect and pre-process data</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs080.html#___sec79" style="font-size: 80%;"><b>Importing Keras and Tensorflow</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs081.html#___sec80" style="font-size: 80%;"><b>Using TensorFlow backend</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs082.html#___sec81" style="font-size: 80%;"><b>Train the model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs083.html#___sec82" style="font-size: 80%;"><b>Visualizing the results</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs084.html#___sec83" style="font-size: 80%;"><b>Running with Keras</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs085.html#___sec84" style="font-size: 80%;"><b>Final part</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs086.html#___sec85" style="font-size: 80%;"><b>Final visualization</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs087.html#___sec86" style="font-size: 80%;"><b>Fun links</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs088.html#___sec87" style="font-size: 80%;"><b>Applications: solving ordinary differential equations with Neural Networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs089.html#___sec88" style="font-size: 80%;"><b>Trial solution</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs090.html#___sec89" style="font-size: 80%;"><b>More details</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs091.html#___sec90" style="font-size: 80%;"><b>Reformulating the problem</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs092.html#___sec91" style="font-size: 80%;"><b>Estimating errors</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs093.html#___sec92" style="font-size: 80%;"><b>Creating a simple Deep Neural Net</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs094.html#___sec93" style="font-size: 80%;"><b>Setting up the code, feed forward part</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs095.html#___sec94" style="font-size: 80%;"><b>Backpropagation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs096.html#___sec95" style="font-size: 80%;"><b>Gradient Descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs097.html#___sec96" style="font-size: 80%;"><b>More on GD and cost function</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs098.html#___sec97" style="font-size: 80%;"><b>An implementation of a Deep Neural Network</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs099.html#___sec98" style="font-size: 80%;"><b>The final parts of the code</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs100.html#___sec99" style="font-size: 80%;"><b>And adding Back propagation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs101.html#___sec100" style="font-size: 80%;"><b>Solving the ODE</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs102.html#___sec101" style="font-size: 80%;"><b>Using neural network</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs103.html#___sec102" style="font-size: 80%;"><b>Using a deep neural network</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs104.html#___sec103" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
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</ul>
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</li>
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@@ -380,6 +284,7 @@ Here we list some of the important limitations of supervised neural network base
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Some of these remarks are particular to DNNs, others are shared by all supervised learning methods. This motivates the use of unsupervised methods which in part circumvent these problems.
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<p>
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<p>
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<!-- navigation buttons at the bottom of the page -->
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<ul class="pagination">
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@@ -395,18 +300,6 @@ Some of these remarks are particular to DNNs, others are shared by all supervise
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<li><a href="._NeuralNet-bs064.html">65</a></li>
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<li><a href="._NeuralNet-bs065.html">66</a></li>
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<li class="active"><a href="._NeuralNet-bs066.html">67</a></li>
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<li><a href="._NeuralNet-bs067.html">68</a></li>
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<li><a href="._NeuralNet-bs068.html">69</a></li>
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<li><a href="._NeuralNet-bs069.html">70</a></li>
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<li><a href="._NeuralNet-bs070.html">71</a></li>
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<li><a href="._NeuralNet-bs071.html">72</a></li>
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<li><a href="._NeuralNet-bs072.html">73</a></li>
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<li><a href="._NeuralNet-bs073.html">74</a></li>
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<li><a href="._NeuralNet-bs074.html">75</a></li>
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<li><a href="._NeuralNet-bs075.html">76</a></li>
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<li><a href="">...</a></li>
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<li><a href="._NeuralNet-bs104.html">105</a></li>
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<li><a href="._NeuralNet-bs067.html">»</a></li>
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</ul>
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<!-- ------------------- end of main content --------------- -->
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