What this book covers
Chapter 1, Introduction to PyTorch, gets you up and running with PyTorch, demonstrates its installation on a variety of platforms, and explores key syntax elements and how to import and use data in PyTorch.
Chapter 2, Deep Learning Fundamentals, is a whirlwind tour of the basics of deep learning, covering the mathematics and theory of optimization, linear networks, and neural networks.
Chapter 3, Computational Graphs and Linear Models, demonstrates how to calculate the error gradient of a linear network and how to harness it to classify images.
Chapter 4, Convolutional Networks, examines the theory of convolutional networks and how to use them for image classification.
Chapter 5, Other NN Architectures, discusses the ...
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