MARC details
| 000 -LEADER |
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| fixed length control field |
04374nam a2200241Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
NULRC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250520103029.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250520s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781492045526 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
NULRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q 325.5 .H69 2020 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Howard, Jeremy |
| Relator term |
author |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Deep learning for coders with fastai and pytorch : |
| Remainder of title |
AI applications without a PhD / |
| Statement of responsibility, etc. |
Jeremy Howard and Sylvain Gugger |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Sebastopol, California : |
| Name of publisher, distributor, etc. |
O'Reilly Media, Incorporated, |
| Date of publication, distribution, etc. |
c2020 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
594 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
USD39 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Intro -- Preface -- Who This Book Is For -- What You Need to Know -- What You Will Learn -- O'Reilly Online Learning -- How to Contact Us -- Foreword -- I. Deep Learning in Practice -- 1. Your Deep Learning Journey -- Deep Learning Is for Everyone -- Neural Networks: A Brief History -- Who We Are -- How to Learn Deep Learning -- Your Projects and Your Mindset -- The Software: PyTorch, fastai, and Jupyter (And Why It Doesn't Matter) -- Your First Model -- Getting a GPU Deep Learning Server -- Running Your First Notebook -- What Is Machine Learning? -- What Is a Neural Network? A Bit of Deep Learning Jargon -- Limitations Inherent to Machine Learning -- How Our Image Recognizer Works -- What Our Image Recognizer Learned -- Image Recognizers Can Tackle Non-Image Tasks -- Jargon Recap -- Deep Learning Is Not Just for Image Classification -- Validation Sets and Test Sets -- Use Judgment in Defining Test Sets -- A Choose Your Own Adventure Moment -- Questionnaire -- Further Research -- 2. From Model to Production -- The Practice of Deep Learning -- Starting Your Project -- The State of Deep Learning -- Computer vision -- Text (natural language processing) Combining text and images -- Tabular data -- Recommendation systems -- Other data types -- The Drivetrain Approach -- Gathering Data -- From Data to DataLoaders -- Data Augmentation -- Training Your Model, and Using It to Clean Your Data -- Turning Your Model into an Online Application -- Using the Model for Inference -- Creating a Notebook App from the Model -- Turning Your Notebook into a Real App -- Deploying Your App -- How to Avoid Disaster -- Unforeseen Consequences and Feedback Loops -- Get Writing! -- Questionnaire -- Further Research -- 3. Data Ethics -- Key Examples for Data Ethics Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits -- Feedback Loops: YouTube's Recommendation System -- Bias: Professor Latanya Sweeney "Arrested" -- Why Does This Matter? -- Integrating Machine Learning with Product Design -- Topics in Data Ethics -- Recourse and Accountability -- Feedback Loops -- Bias -- Historical bias -- Measurement bias -- Aggregation bias -- Representation bias -- Addressing different types of bias -- Disinformation -- Identifying and Addressing Ethical Issues -- Analyze a Project You Are Working On -- Processes to Implement -- Ethical lenses The Power of Diversity -- Fairness, Accountability, and Transparency -- Role of Policy -- The Effectiveness of Regulation -- Rights and Policy -- Cars: A Historical Precedent -- Conclusion -- Questionnaire -- Further Research -- Deep Learning in Practice: That's a Wrap! -- II. Understanding fastai's Applications -- 4. Under the Hood: Training a Digit Classifier -- Pixels: The Foundations of Computer Vision -- First Try: Pixel Similarity -- NumPy Arrays and PyTorch Tensors -- Computing Metrics Using Broadcasting -- Stochastic Gradient Descent -- Calculating Gradients. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MACHINE LEARNING |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gugger, Sylvain |
| Relator term |
co-author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |
