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

US $41.40
ApproximatelyEUR 37.01
Condition:
New
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    Free USPS Media MailTM.
    Located in: Somerset, New Jersey, United States
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    eBay item number:226572288511

    Item specifics

    Condition
    New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
    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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    Zebrasbooks-2

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