We started this chapter with a tutorial on the mathematical apparatus that forms the foundation of NNs. Then, we recapped on NNs and their architecture. Along the way, we tried to explicitly connect the mathematical concepts with the various components of the NNs. We paid special attention to the various types of activation functions. Finally, we took a comprehensive look at the NN training process. We discussed gradient descent, cost functions, backpropagation, weights initialization, and SGD optimization techniques.
In the next chapter, we'll discuss the intricacies of convolutional networks and their applications in the computer vision domain.
