In this chapter, I will discuss what a neuron is and what its components are. I will clarify the mathematical notation we will require and cover the many activation functions that are used today in neural networks. Gradient descent optimization will be discussed in detail, and the concept of learning rate and its quirks will be introduced. To make things a bit more fun, we will then use a single neuron to perform linear and logistic regression on real datasets. I will then discuss and explain how to implement the two algorithms with tensorflow.
To keep the chapter focused ...
