Deep learning for coders with fastai and PyTorch : AI applications without a PhD
Author(s)
Bibliographic Information
Deep learning for coders with fastai and PyTorch : AI applications without a PhD
O'Reilly, 2020
- Other Title
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Deep learning for coders with fastai & PyTorch
Available at 14 libraries
Note
"Foreword by Soumith Chintala"-- Cover
"powerd by jupyter"-- Cover
Pagination of later printing: xxiv, 594 p
Includes index
Description and Table of Contents
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 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
by "Nielsen BookData"