Deep Learning with PyTorch: A hands-on intro to cutting-edge AI Posted on : Nov 26 - 2020

If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook’s AI Research Lab that is powerful, easy to learn, and very versatile.

When it comes to training material, however, PyTorch lags behind TensorFlow, Google’s flagship deep learning library.

There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions.

Deep Learning with PyTorch is split across two main sections, first teaching the basics of deep learning and then delving into an advanced, real-world application of medical imaging analysis. Soumith Chintala, the co-creator of PyTorch, has described the book as “a definitive treatise on PyTorch.”

On both the basics and advanced concepts, the book delivers on its promises. But in the in-between area, things get a bit complicated.

An excellent overview of deep learning

The first part of Deep Learning with PyTorch spans across eight chapters and provides an introduction to key deep learning concepts. The authors have done a terrific job of breaking down complicated topics, such as gradient descent, with cartoonish drawings and concrete examples. A lot of the stuff you’ll see in this section overlaps with deep learning intros in Python machine learning books but with more depth added. View More