1 Introducing deep learning and the PyTorch Library

 

This chapter covers

  • How deep learning changes our approach to machine learning
  • Understanding why PyTorch is a good fit for deep learning
  • Examining a typical deep learning project
  • The hardware you’ll need to follow along with the examples

PyTorch is a Python library that facilitates building deep learning projects. It emphasizes flexibility and allows deep learning models to be expressed in idiomatic Python. This approachability and ease of use found early adopters in the research community, and in the years since its first release, it has grown into one of the most prominent deep learning tools across a broad range of applications.

This book teaches you to build complete deep learning projects from start to finish using PyTorch. You’ll learn to work with tensors (PyTorch’s fundamental data structure), design neural network architectures, train models on your data, and deploy them to production. We include several major hands-on projects namely: building your own language model, working with image generation models, and creating a medical image segmentation system from scratch, giving you practical experience with the entire deep learning workflow.

1.1 What is deep learning?

1.2 The shift from machine learning to deep learning

1.3 About this book

1.4 Why PyTorch?

1.4.1 The deep learning competitive landscape

1.5 How PyTorch supports deep learning projects

1.6 Hardware and software requirements

1.6.1 Using Jupyter Notebooks

1.7 Exercises

1.8 Summary