updated typos
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@@ -110,20 +110,19 @@ Automatically generated HTML file from DocOnce source
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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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('Adjust hyperparameters', 2, None, '___sec47'),
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('Visualization', 2, None, '___sec48'),
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('scikit-learn implementation', 2, None, '___sec49'),
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('Visualization', 2, None, '___sec50'),
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('Building neural networks in Tensorflow and Keras',
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2,
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None,
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'___sec49'),
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('Tensorflow', 2, None, '___sec50'),
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('Collect and pre-process data', 2, None, '___sec51'),
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('Using TensorFlow backend', 2, None, '___sec52'),
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('Optimizing and using gradient descent', 2, None, '___sec53'),
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('Using Keras', 2, None, '___sec54')]}
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'___sec51'),
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('Tensorflow', 2, None, '___sec52'),
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('Collect and pre-process data', 2, None, '___sec53'),
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('Using TensorFlow backend', 2, None, '___sec54'),
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('Optimizing and using gradient descent', 2, None, '___sec55'),
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('Using Keras', 2, None, '___sec56')]}
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end of tocinfo -->
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<body>
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@@ -208,14 +207,16 @@ MathJax.Hub.Config({
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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>Building neural networks in Tensorflow and Keras</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs051.html#___sec50" style="font-size: 80%;"><b>Tensorflow</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs052.html#___sec51" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs053.html#___sec52" style="font-size: 80%;"><b>Using TensorFlow backend</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs054.html#___sec53" style="font-size: 80%;"><b>Optimizing and using gradient descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs055.html#___sec54" style="font-size: 80%;"><b>Using Keras</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs048.html#___sec47" style="font-size: 80%;"><b>Adjust hyperparameters</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs049.html#___sec48" style="font-size: 80%;"><b>Visualization</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs050.html#___sec49" style="font-size: 80%;"><b>scikit-learn implementation</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs051.html#___sec50" style="font-size: 80%;"><b>Visualization</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs052.html#___sec51" style="font-size: 80%;"><b>Building neural networks in Tensorflow and Keras</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs053.html#___sec52" style="font-size: 80%;"><b>Tensorflow</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs054.html#___sec53" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs055.html#___sec54" style="font-size: 80%;"><b>Using TensorFlow backend</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs056.html#___sec55" style="font-size: 80%;"><b>Optimizing and using gradient descent</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs057.html#___sec56" style="font-size: 80%;"><b>Using Keras</b></a></li>
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</ul>
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</li>
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@@ -267,7 +268,7 @@ i.e. each neuron \( j \) outputs the probability of being in class \( j \) given
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The denominator is a normalization factor to ensure the outputs (probabilities) sum up to 1.
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The exponent is just the weighted sum of inputs as before:
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$$ z_j = \sum_{i=1}^n w_ {ij} a_i = \boldsymbol{a}^T \boldsymbol{w}_j .$$
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$$ z_j = \sum_{i=1}^n w_ {ij} a_i+b_j.$$
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<p>
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Since each neuron in the output layer is connected to the 50 inputs from the hidden layer we have 50x10 = 500
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@@ -299,7 +300,7 @@ weights to the output layer.
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<li><a href="._NeuralNet-bs045.html">46</a></li>
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<li><a href="._NeuralNet-bs046.html">47</a></li>
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<li><a href="">...</a></li>
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<li><a href="._NeuralNet-bs055.html">56</a></li>
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<li><a href="._NeuralNet-bs057.html">58</a></li>
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<li><a href="._NeuralNet-bs038.html">»</a></li>
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</ul>
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<!-- ------------------- end of main content --------------- -->
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