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 theory behind recurrent networks and shows how to use them to make predictions about sequence data. It also discusses long short-term memory networks (LSTMs) and has you build a language model to predict text. 

Chapter 6, Getting the Most out of PyTorch, examines some advanced features, such as using PyTorch in multiprocessor and parallel environments. You will build a flexible solution for image classification using out-of-the-box pre-trained models.