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

US $56.12
Approximately£42.15
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Last updated on 11 Jan, 2024 12:00:03 GMTView all revisionsView all revisions

Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
Book Title
Deep Learning for Coders with fastai and PyTorch: AI Applications
Publication Date
2020-07-31
ISBN
9781492045526

About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1492045527
ISBN-13
9781492045526
eBay Product ID (ePID)
28038665481

Product Key Features

Number of Pages
621 Pages
Language
English
Publication Name
Deep Learning for Coders with Fastai and Pytorch : Ai Applications Without a Phd
Publication Year
2020
Subject
Programming / General, Machine Theory, Image Processing, Computer Science, Neural Networks, Data Visualization, Programming Languages / Python
Type
Textbook
Subject Area
Computers
Author
Jeremy Howard, Sylvain Gugger
Format
Trade Paperback

Dimensions

Item Height
1.5 in
Item Weight
38.2 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2022-278837
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.312
Synopsis
Deep 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 Number
QA76.9.D343H69 2020

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