Chapter 4. Introduction to neural learning: gradient descent

In this chapter

  • Do neural networks make accurate predictions?
  • Why measure error?
  • Hot and cold learning
  • Calculating both direction and amount from error
  • Gradient descent
  • Learning is just reducing error
  • Derivatives and how to use them to learn
  • Divergence and alpha

“The only relevant test of the validity of a hypothesis is comparison of its predictions with experience.”

Milton Friedman, Essays in Positive Economics (University of Chicago Press, 1953)

Predict, compare, and learn

In chapter 3, you learned about the paradigm “predict, compare, learn,” and we dove deep into the first step: predict. In the process, you learned a myriad of things, including the major parts of neural networks ...

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