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

Deep Learning for Coders with fastai and PyTorchDeep 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 filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith ChintalaAbout the authorsJeremy Howard is a founding researcher at fast.ai, an institute dedicated to making deep learning more accessible. He's also a distinguished research scientist at the University of San Francisco and a member of the World Economic Forum's Global Al Council.Sylvain Gugger is a research engineer at Hugging Face. Previously, he was a research scientist at fast.ai, focused on making deep learning more accessible by designing and improving techniques that allow models to train fast on limited resources.