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Logistic Function Logistic regression is a generalized linear model, sharing many similarities with multiple linear regression. We define the logistic function (sigmoid) as: $$ g(z) = \frac{1}{1 + e^{-z}} $$ With $ z = \theta^T x $, the hypothesis becomes: $$ h_\theta(x) = \frac{1}{1 + e^{-\theta^T x}} $$ The graph of the logistic function is: ...

Training Deep Networks Why do we need batch normalization layers? Let us review some practical challenges that arise when training neural networks. First, the way data are preprocessed often dramatically influences the final result. Recall the example of using a multilayer perceptron to predict house prices. When working with real data, our fir ...