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.