chapter one

1 Introducing Deep Learning and the PyTorch Library

 

This chapter covers:

  • What this book will teach you
  • PyTorch’s role as a library for building deep learning projects
  • The strengths and weaknesses of PyTorch
  • The hardware you’ll need to follow along with the examples

We are living through exciting times. The landscape of what computers can do is changing by the week. Tasks that only a few years ago were thought to require higher cognition are getting solved by machines at near- to super-human levels of performance. For example, describing a photographic image with a sentence in idiomatic English; playing complex strategy games; and diagnosing a tumor from a radiological scan are all now approachable by a computer. Even more impressively, the ability to solve such tasks is acquired by computers through examples, rather than encoded by a human as a set of hand-crafted rules.

1.1  What is PyTorch?

1.2  What is this book?

1.3  Why PyTorch

1.3.1  The Deep Learning Revolution

1.3.2  Immediate vs. deferred execution

1.3.3  The deep learning competitive landscape

1.4  PyTorch has the batteries included

1.4.1  Hardware for deep learning

1.4.2  Using Jupyter notebooks

1.5  Conclusion

1.6  Exercises

1.7  Summary