Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD

by Jeremy Howard, Sylvain Gugger

Deep Learning for Coders with fastai and AI Applications Without a PhD is purchased directly from the publisher or approved distributor and spiraled by a 3rd party. Seller is not affiliated with, endorsed by, or pre-authorized by the publisher or author for the spiral listing. 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 to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

621 pages, Spiral-bound

Published August 25, 2020

About the author

Jeremy Howard is Senior Lecturer, School of Art History, University of St Andrews, Scotland.