Updated neural network
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mhjensen
@@ -6,9 +6,9 @@ Automatically generated HTML file from DocOnce source
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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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: Elements of machine learning">
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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: Elements of machine learning</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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<!-- Bootstrap style: bootstrap -->
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<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
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@@ -44,28 +44,78 @@ Automatically generated HTML file from DocOnce source
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('Artificial neurons', 2, None, '___sec1'),
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('Neural network types', 2, None, '___sec2'),
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('Feed-forward neural networks', 2, None, '___sec3'),
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('Recurrent neural networks', 2, None, '___sec4'),
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('Other types of networks', 2, None, '___sec5'),
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('Multilayer perceptrons', 2, None, '___sec6'),
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('Why multilayer perceptrons?', 2, None, '___sec7'),
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('Convolutional Neural Network', 2, None, '___sec4'),
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('Recurrent neural networks', 2, None, '___sec5'),
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('Other types of networks', 2, None, '___sec6'),
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('Multilayer perceptrons', 2, None, '___sec7'),
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('Why multilayer perceptrons?', 2, None, '___sec8'),
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('Relevance', 3, None, '___sec17'),
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('Setting up a Multi-layer perceptron model',
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('Relevance', 3, None, '___sec18'),
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('The multilayer perceptron (MLP)', 2, None, '___sec19'),
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('From one to many layers, the universal approximation theorem',
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None,
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'___sec18'),
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('Two-layer Neural Network', 2, None, '___sec19')]}
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'___sec20'),
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('Deriving the back propagation code for a multilayer perceptron '
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'model',
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2,
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'___sec21'),
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('Definitions', 2, None, '___sec22'),
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('Derivatives and the chain rule', 2, None, '___sec23'),
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('Derivative of the cost function', 2, None, '___sec24'),
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('Bringing it together, first back propagation equation',
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2,
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None,
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'___sec25'),
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('Derivatives in terms of $z_j^L$', 2, None, '___sec26'),
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('Bringing it together', 2, None, '___sec27'),
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('Final back propagating equation', 2, None, '___sec28'),
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('Setting up the Back propagation algorithm',
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2,
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None,
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'___sec29'),
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('Setting up a Multi-layer perceptron model for classification',
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2,
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None,
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'___sec30'),
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('Defining the cost function', 2, None, '___sec31'),
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('Developing a code for doing neural networks with back '
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'propagation',
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2,
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None,
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'___sec32'),
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('Collect and pre-process data', 2, None, '___sec33'),
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('Train and test datasets', 2, None, '___sec34'),
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('Define model and architecture', 2, None, '___sec35'),
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('Layers', 2, None, '___sec36'),
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('Weights and biases', 2, None, '___sec37'),
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('Feed-forward pass', 2, None, '___sec38'),
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('Matrix multiplication', 2, None, '___sec39'),
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('Choose cost function and optimizer', 2, None, '___sec40'),
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('Optimizing the cost function', 2, None, '___sec41'),
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('Regularization', 2, None, '___sec42'),
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('Matrix multiplication', 2, None, '___sec43'),
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('Improving performance', 2, None, '___sec44'),
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('Full object-oriented implementation', 2, None, '___sec45'),
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('Evaluate model performance on test data', 2, None, '___sec46'),
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('Adjust hyperparameters (if necessary, network architecture',
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2,
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None,
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'___sec47'),
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('scikit-learn implementation', 2, None, '___sec48'),
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('And then with Tensorflow', 2, None, '___sec49')]}
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end of tocinfo -->
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<body>
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@@ -95,7 +145,7 @@ 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: Elements of machine learning</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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@@ -107,22 +157,52 @@ MathJax.Hub.Config({
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs002.html#___sec1" style="font-size: 80%;"><b>Artificial neurons</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs003.html#___sec2" style="font-size: 80%;"><b>Neural network types</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs004.html#___sec3" style="font-size: 80%;"><b>Feed-forward neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs005.html#___sec4" style="font-size: 80%;"><b>Recurrent neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs006.html#___sec5" style="font-size: 80%;"><b>Other types of networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs007.html#___sec6" style="font-size: 80%;"><b>Multilayer perceptrons</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs008.html#___sec7" style="font-size: 80%;"><b>Why multilayer perceptrons?</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs009.html#___sec8" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs005.html#___sec4" style="font-size: 80%;"><b>Convolutional Neural Network</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs006.html#___sec5" style="font-size: 80%;"><b>Recurrent neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs007.html#___sec6" style="font-size: 80%;"><b>Other types of networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs008.html#___sec7" style="font-size: 80%;"><b>Multilayer perceptrons</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs009.html#___sec8" style="font-size: 80%;"><b>Why multilayer perceptrons?</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs010.html#___sec9" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs011.html#___sec10" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs012.html#___sec11" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs013.html#___sec12" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs014.html#___sec13" style="font-size: 80%;"> Matrix-vector notation</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs015.html#___sec14" style="font-size: 80%;"> Matrix-vector notation and activation</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs016.html#___sec15" style="font-size: 80%;"> Activation functions</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs017.html#___sec16" style="font-size: 80%;"> Activation functions, Logistic and Hyperbolic ones</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs018.html#___sec17" style="font-size: 80%;"> Relevance</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs019.html#___sec18" style="font-size: 80%;"><b>Setting up a Multi-layer perceptron model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs020.html#___sec19" style="font-size: 80%;"><b>Two-layer Neural Network</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs014.html#___sec13" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs015.html#___sec14" style="font-size: 80%;"> Matrix-vector notation</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs016.html#___sec15" style="font-size: 80%;"> Matrix-vector notation and activation</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs017.html#___sec16" style="font-size: 80%;"> Activation functions</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs018.html#___sec17" style="font-size: 80%;"> Activation functions, Logistic and Hyperbolic ones</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs019.html#___sec18" style="font-size: 80%;"> Relevance</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs020.html#___sec19" style="font-size: 80%;"><b>The multilayer perceptron (MLP)</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs021.html#___sec20" style="font-size: 80%;"><b>From one to many layers, the universal approximation theorem</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs022.html#___sec21" style="font-size: 80%;"><b>Deriving the back propagation code for a multilayer perceptron model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs023.html#___sec22" style="font-size: 80%;"><b>Definitions</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs024.html#___sec23" style="font-size: 80%;"><b>Derivatives and the chain rule</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs025.html#___sec24" style="font-size: 80%;"><b>Derivative of the cost function</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs026.html#___sec25" style="font-size: 80%;"><b>Bringing it together, first back propagation equation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs027.html#___sec26" style="font-size: 80%;"><b>Derivatives in terms of \( z_j^L \)</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs028.html#___sec27" style="font-size: 80%;"><b>Bringing it together</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs029.html#___sec28" style="font-size: 80%;"><b>Final back propagating equation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs030.html#___sec29" style="font-size: 80%;"><b>Setting up the Back propagation algorithm</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs031.html#___sec30" style="font-size: 80%;"><b>Setting up a Multi-layer perceptron model for classification</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs032.html#___sec31" style="font-size: 80%;"><b>Defining the cost function</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs033.html#___sec32" style="font-size: 80%;"><b>Developing a code for doing neural networks with back propagation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs034.html#___sec33" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs035.html#___sec34" style="font-size: 80%;"><b>Train and test datasets</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs036.html#___sec35" style="font-size: 80%;"><b>Define model and architecture</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs037.html#___sec36" style="font-size: 80%;"><b>Layers</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs038.html#___sec37" style="font-size: 80%;"><b>Weights and biases</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs039.html#___sec38" style="font-size: 80%;"><b>Feed-forward pass</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs040.html#___sec39" style="font-size: 80%;"><b>Matrix multiplication</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs041.html#___sec40" style="font-size: 80%;"><b>Choose cost function and optimizer</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs042.html#___sec41" style="font-size: 80%;"><b>Optimizing the cost function</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs043.html#___sec42" style="font-size: 80%;"><b>Regularization</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs044.html#___sec43" style="font-size: 80%;"><b>Matrix multiplication</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs045.html#___sec44" style="font-size: 80%;"><b>Improving performance</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs046.html#___sec45" style="font-size: 80%;"><b>Full object-oriented implementation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs047.html#___sec46" style="font-size: 80%;"><b>Evaluate model performance on test data</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs048.html#___sec47" style="font-size: 80%;"><b>Adjust hyperparameters (if necessary, network architecture</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs049.html#___sec48" style="font-size: 80%;"><b>scikit-learn implementation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs050.html#___sec49" style="font-size: 80%;"><b>And then with Tensorflow</b></a></li>
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</ul>
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</li>
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@@ -141,7 +221,7 @@ MathJax.Hub.Config({
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<div class="jumbotron">
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<center><h1>Data Analysis and Machine Learning: Elements of machine learning</h1></center> <!-- document title -->
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<center><h1>Data Analysis and Machine Learning: Neural networks, from the simple perceptron to deep learning</h1></center> <!-- document title -->
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<p>
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<!-- author(s): Morten Hjorth-Jensen -->
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@@ -157,12 +237,9 @@ MathJax.Hub.Config({
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Sep 28, 2018</h4></center> <!-- date -->
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<center><h4>Oct 2, 2018</h4></center> <!-- date -->
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<br>
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<p>
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<!-- add own code for DNN -->
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<p>
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<p><a href="._NeuralNet-bs001.html" class="btn btn-primary btn-lg">Read »</a></p>
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@@ -184,7 +261,7 @@ MathJax.Hub.Config({
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<li><a href="._NeuralNet-bs008.html">9</a></li>
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<li><a href="._NeuralNet-bs009.html">10</a></li>
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<li><a href="">...</a></li>
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<li><a href="._NeuralNet-bs020.html">21</a></li>
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<li><a href="._NeuralNet-bs050.html">51</a></li>
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<li><a href="._NeuralNet-bs001.html">»</a></li>
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