About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-101492045527

ISBN-139781492045526

eBay Product ID (ePID)28038665481

Product Key Features

Number of Pages621 Pages

LanguageEnglish

Publication NameDeep Learning for Coders with Fastai and Pytorch : Ai Applications Without a Phd

Publication Year2020

SubjectProgramming / General, Machine Theory, Image Processing, Computer Science, Neural Networks, Data Visualization, Programming Languages / Python

TypeTextbook

Subject AreaComputers

AuthorJeremy Howard, Sylvain Gugger

FormatTrade Paperback

Dimensions

Item Height1.5 in

Item Weight38.2 Oz

Item Length9.2 in

Item Width7 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2022-278837

Dewey Edition23

IllustratedYes

Dewey Decimal006.312

SynopsisDeep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks--including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions., 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

LC Classification NumberQA76.9.D343H69 2020