Chapter 11: Training Deep Neural Networks

This chapter focuses on Deep Learning and techniques that can be used to keep neural networks from getting out of hand as their complexities get deeper. Traditionally Deep Learning is defined as a neural network that contains 3 or more layers. But, with this addition of layers comes additional complexity and with complexity comes more ways for a project to break. Most of this chapter deals with introducing us to the techniques that we can use to minimize these breakages when training deep models. Neural Networks are trained through backpropagation using gradient descent to adjust their weighting so that we get the intended result.