AI - Neural Networks and Deep Learning - Nielsen - Chap 3 - Learning Improvement

As if Chapter 2 wasn't heavy enough, I moved onto Chapter 3, which introduced some great concepts, which I will mention here in this blog post. But, I couldn't really follow the detail very well, largely again due to the heavy mathematical expressions and notations used. 

But, I will iterate what he covers in this chapter, and I think each one of these will require its own "separate study", preferably in a simpler way and manner.

Chapter 3 discusses more efficient ways to learn.

It starts out discussing the Cost Function. 

In previous chapters, Nielsen uses the Quadratic Cost Function. But in Chapter 3, he introduces the Cross-Entropy Cost Function, and discusses how by using this, it avoids learning slowdown. Unfortunately, I can't comment much further on this because frankly, I got completely lost in this discussion.

He spends a GREAT DEAL of text discussing Cross-Entropy, including a discussion about the fact that he uses different Learning Rates on the Quadratic Cost Function (.15), versus the Cross-Entropy Cost Function (.005) - and discusses that the rates being different doesn't matter because it is more about how the speed of learning changes, than the actual speed of learning.

After a while, he mentioned an alternative to Cross-Entropy called Softmax. Now, this term seemed familiar. In doing a backcheck, I found that Softmax was used in the first book I read, called AI Crash Course by Haydelin de Ponteves.  I remembered both Softmax and Argmax being mentioned.

Softmax introduces a layer of neurons parallel to the output neurons. So if you had 4 output neurons, you would have 4 Softmax neurons preceding the 4 output neurons.  What Softmax does, is return a Probability Distribution. All of the neurons add up to 1, and if one neuron decreases, there must be an alternative increase amongst the other Softmax neurons.  This could be useful, for example, in cases where the AI is guessing which animal type it is: Dog, Cat, Parrot, Snake. You might see a higher correlation between Dog and Cat, and a lower one with the Parrot and Snake. 

Nielsen then goes on to discuss Overfitting (OverTraining) and Regularization, which is designed to combat Overfitting. He discusses four approaches to Regularization, which I won't echo here, as I clearly will need to consult elsewhere for simpler discussions, definitions and examples of these.