Deep Learning for Coders with Fastai and Pytorch

By Jeremy Howard, Sylvain Gugger,

Book cover of Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

Book description

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library…

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Why read it?

2 authors picked Deep Learning for Coders with Fastai and Pytorch as one of their favorite books. Why do they recommend it?

Jeremy Howard is the lead author and has always been a world-class educator. This book is based on his fast.ai course, which has managed to splice all rigor, simplicity, and cutting edge techniques into one course. It also uses its custom fast.ai framework built on PyTorch, which is the dominant language for researchers. This book is very practically oriented and gets you off the ground very quickly with your own projects!

From Jakub's list on applied deep learning.

The difference between an ML beginner and an ML expert is that the ML expert doesn’t try to build something that they can simply reuse. But the expert also has the judgment to recognize scenarios where it is worth building something — this is usually because the current, generic, state-of-the-art (SoTA) models won’t be good enough.

Jeremy shows you what the state of the art (SoTA) looks like across a wide variety of ML fields, and how to use SoTA models to get what you need. If the first three books will make you a good ML engineer, this book…

From Valliappa's list on to become a machine learning engineer.

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