Deep Learning for Coders with Fastai and PyTorch

Jeremy Howard is a business owner, entrepreneur, developer, and instructor. Jeremy is a co-founder of fast.ai, a research organization devoted to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a faculty member of Singularity University, and a World Economic Forum Young Global Leader.

Sylvain Gugger is a fast.ai Research Scientist that focuses on making deep learning more accessible by creating and improving strategies that allow models to train quickly on constrained resources.

Deep learning is frequently regarded as the sole domain of math PhDs and large tech corporations. However, as this hands-on guide reveals, Python programmers may produce excellent deep learning results with little math knowledge, small quantities of data, and minimum code. How? Fastai is the first library to give a consistent interface to the most popular deep learning applications.

In Deep Learning for Coders with Fastai and PyTorch, the inventors of fastai, Jeremy Howard and Sylvain Gugger, teach you how to train a model on a variety of tasks using fastai and PyTorch. You'll also delve further and deeper into deep learning theory to acquire a thorough knowledge of the algorithms at work.

• Models in computer vision, natural language processing, tabular data, and collaborative filtering should be trained.
• Discover the most recent deep learning techniques that are most relevant in practice.
• Understanding how deep learning models function can help you improve accuracy, speed, and dependability.
• Learn how to convert your models into web applications.
• Consider the ethical implications of your work as you build deep learning algorithms from scratch. Gain insight from the foreword by PyTorch cofounder Soumith Chintala.

Author: Jeremy Howard and Sylvain Gugger

Link to buy: https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527/

Ratings: 4.8 out of 5 stars (from 400 reviews)

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