Summary

We have reached the end of this chapter, where we have learned the basic theoretical concepts behind neural networks and deep learning networks. All of this will be helpful to understand the steps for the practical implementation of deep learning networks described in the coming chapters.

We started with the artificial neuron and moved on to describe how to assemble and train a network of neurons, a fully connected feedforward neural network, via a variant of the gradient descent algorithm, using the backpropagation algorithm to calculate the gradient.

We concluded the chapter with a few hints on how to design and train a neural network. First, we described some commonly used network topologies, neural layers, and activation functions to design the appropriate neural architecture.

We then moved to analyze the effects of some parameters involved in the training algorithm. We introduced a few more parameters and techniques to optimize the training algorithm against...