Python Deep Learning - Second Edition
by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
What this book covers
Chapter 1, Machine Learning – an Introduction, will introduce you to the basic ML concepts and terms that we'll be using throughout the book. It will give an overview of the most popular ML algorithms and applications today. It will also introduce the DL library that we'll use throughout the book.
Chapter 2, Neural Networks, will introduce you to the mathematics of neural networks. We'll learn about their structure, how they make predictions (that's the feedforward part), and how to train them using gradient descent and backpropagation (explained through derivatives). The chapter will also discuss how to represent operations with neural networks as vector operations.
Chapter 3, Deep Learning Fundamentals, will explain ...
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