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Grokking Deep Learning
book

Grokking Deep Learning

by Andrew W. Trask
February 2019
Intermediate to advanced content levelIntermediate to advanced
336 pages
9h 29m
English
Manning Publications
Content preview from Grokking Deep Learning

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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Publisher Resources

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