206 lines
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HTML
206 lines
9.0 KiB
HTML
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs001.html#___sec0" style="font-size: 80%;"><b>Neural networks</b></a></li>
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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="#___sec2" style="font-size: 80%;"><b>Neural network types</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-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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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0003"></a>
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<!-- !split -->
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<h2 id="___sec2" class="anchor">Neural network types </h2>
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<p>
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An artificial neural network (NN), is a computational model that
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consists of layers of connected neurons, or <em>nodes</em>. It is supposed
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to mimic a biological nervous system by letting each neuron interact
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with other neurons by sending signals in the form of mathematical
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functions between layers. A wide variety of different NNs have been
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developed, but most of them consist of an input layer, an output layer
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and eventual layers in-between, called <em>hidden layers</em>. All layers can
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contain an arbitrary number of nodes, and each connection between two
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nodes is associated with a weight variable.
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<p>
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Neural networks (also called neural nets) are neural-inspired
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nonlinear models for supervised learning. As we will see, neural nets
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can be viewed as natural, more powerful extensions of supervised
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learning methods such as linear and logistic regression and soft-max
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methods.
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<p>
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<li><a href="._NeuralNet-bs002.html">«</a></li>
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<li><a href="._NeuralNet-bs000.html">1</a></li>
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<li><a href="._NeuralNet-bs001.html">2</a></li>
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<li><a href="._NeuralNet-bs002.html">3</a></li>
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<li class="active"><a href="._NeuralNet-bs003.html">4</a></li>
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<li><a href="._NeuralNet-bs004.html">5</a></li>
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<li><a href="._NeuralNet-bs006.html">7</a></li>
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<li><a href="._NeuralNet-bs007.html">8</a></li>
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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="._NeuralNet-bs010.html">11</a></li>
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<li><a href="._NeuralNet-bs011.html">12</a></li>
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<li><a href="._NeuralNet-bs012.html">13</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-bs004.html">»</a></li>
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