#012A Building a Deep Neural Network Master Data Science

In this post we will see what are the building blocks of a Deep Neural Network. We will pick one layer, for example layer \(l \) of a deep neural network and we will focus on computatons for that layer. Calculation of the forward pass for layer \( l \) we get as we input activations from the previous layer and as the output we get activations of the current layer, layer \(l \). It is good to cache the value of \( z {[l]} \) for calculations in backwardpass. Backward pass is done as we input \(da {[l]} \) and we get the output \(da {[l-1]} \), as presented in the following graph.