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Sylvain Gugger
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Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD (English Edition) 1st 版, Kindle版

英語版 Jeremy Howard (著), Sylvain Gugger (著) 形式: Kindle版
4.7 5つ星のうち4.7 542個の評価
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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
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Previous page
  1. ISBN-13
    978-1492045526
  2. 版
    第1
  3. 付箋メモ
    有効になっていません
  4. 出版社
    O'Reilly Media
  5. 発売日
    2020/6/29
  6. 言語
    英語
  7. ファイルサイズ
    33114 KB
  8. すべての詳細を表示
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著者について

Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a faculty member at Singularity University, and a Young Global Leader with the World Economic Forum.

Jeremy&;s most recent startup, Enlitic, was the first company to apply deep learning to medicine, and has been selected one of the world&;s top 50 smartest companies by MIT Tech Review two years running. He was previously the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group&;purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.

He has many television and other video appearances, including as a regular guest on Australia&;s highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.

Sylvain is a former teacher and a Research Scientist at fast.ai, with a focus on making deep learning more accessible by designing and improving techniques that allow models to train fast on limited resources.

Prior to this, Sylvain wrote several books covering the entire curriculum he was teaching in France (published at Éditions Dunod) until 2015 in CPGE. CPGE are a French specific two-year program whereby handpicked students who graduated high school follow an intense preparation before sitting for the competitive exam to enter the top engineering and business schools of the country. Sylvain taught computer science and mathematics in that program for seven years.

Sylvain is an alumni from École Normale Supérieure (Paris, France) where he studied mathematics and has a Master&;s Degree in mathematics from University Paris XI (Orsay, France).

--このテキストは、paperback版に関連付けられています。

登録情報

  • ASIN ‏ : ‎ B08C2KM7NR
  • 出版社 ‏ : ‎ O'Reilly Media; 第1版 (2020/6/29)
  • 発売日 ‏ : ‎ 2020/6/29
  • 言語 ‏ : ‎ 英語
  • ファイルサイズ ‏ : ‎ 33114 KB
  • 同時に利用できる端末数 ‏ : ‎ 無制限
  • Text-to-Speech（テキスト読み上げ機能） ‏ : ‎ 有効
  • X-Ray ‏ : ‎ 有効にされていません
  • Word Wise ‏ : ‎ 有効にされていません
  • 付箋メモ ‏ : ‎ 有効になっていません
  • 本の長さ ‏ : ‎ 1053ページ
  • カスタマーレビュー:
    4.7 5つ星のうち4.7 542個の評価

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Redhotchilipoppy
5つ星のうち5.0 Excellent
スウェーデンで2024年2月11日にレビュー済み
Amazonで購入
Well written and entertaining.
Worth every penny even though the whole book is available for free online aswell.
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J. Yoon
5つ星のうち5.0 Best one book for actually doing Deep Learning
2021年4月27日にアメリカ合衆国でレビュー済み
Amazonで購入
While no one should be limited to just one book, if I had to choose, this is the one book for rolling up my sleeves and actually doing Deep Learning. It's also not overly long. The authors carefully chose just the necessary materials and not too much. Each important concept is carefully explained. Some of the concepts are covered multiple times, with progressive depth, as the chapters advance. The book is chock full of Jupyter notebooks that you can use as templates with **your own data**. You can swap out sections from one chapter with sections from other chapters to tailor it to **your** data project. The Fastai library is an open-source Python packages used heavily in the book, also written by fast.ai. It's there to help new data scientist get started faster. It handles many of the common tasks in a data science project, and acts as an easier interface (API) to PyTorch, Pandas, and NumPy. It also sets many hyperparameter with defaults that work in the majority of cases. While later on, users can go straight to the PyTorch source, initially these defaults and the unified Fastai API saves a lot of time.

While the authors say this is a good introduction to anyone with **1 year of Python coding experience,** I think it helps to have some traditional deep learning class exposure. I took Coursera's Andrew Ng deep learning classes before, and that knowledge really helped. This book is written top-down, which is opposite of traditional college classes, so it could be very confusing if you have not taken any traditional-style class in deep learning. Also the heavy use of Fastai library makes the code more difficult to learn in some dimension, even though on net it saves a lot of learning time. :-( At times it's difficult to tell how to do the same thing in PyTorch without using Fastai as a front-end API. You will need to learn how to do it all in PyTorch eventually (chapters 17, 18, 19), but not until you have several working projects under your belt. So yes, it's the best one book for rapidly getting started and DOING real deep learning with your **own data.** :-D

**Update August 5., 2022:**
There is a 2022 Spring version of class using this book on YouTube. Search for Fastai or Jeremy Howard. Fastbook notebooks on Github is updated to April 2022. Previous date was August 2020, same as book.

A reviewer said that too many codes in the book were outdated and produced errors in February 2021. That was not at all my experience. One of the error example given, I can confirmed was an error. There was an extra blank space at the beginning of a code line. But the other example given for DataBlock api, I can confirm is not an error since I've ran that code many times. I've also ran almost all of the code from chapters 1 to 12 during 2021. I found only 2 code errors. I led a Meetup group using this book. Also, I recommend downloading the Jupyter notebooks from GitHub to get the latest version, but this would not have affected the error rate in the code during 2021, since both the book and Github code had the same version, dated August 2020. Of course people should use books they like and enjoy! And not everyone likes this book.

In my experience in leading a Meetup group based on this book over 14 months, about 50% LOVE this book (75% experienced coders in some language and 25% brand new to 1 year Python coders). About 25% HATE this book (half and half experienced and beginner coders), mainly because of the top-down teaching approach, and some people feel that using Fastai library hides too much of the code detail in part 1 (chp 1-10). Straight PyTorch and bottom-up approach would have worked better for these people. About 25% are in the middle. They like the Fastai top-layer API library (a must for complete beginner coders) and/or the top-down teaching approach.

I fall into the "will have the most hard time" category according to Jeremy Howard at a Lex Friedman interview: About 1 year of full-time coding experience in Python without a deeper coding experience in other languages, and not a total beginner coder. However, I appreciate the top-down teaching approach and Fastai library that offers an easier coding layer for beginners. I had to read small parts of source code for Fastai library to understand DataBlocks API, but I learned about coding patterns and URL fetch commands too. I think it requires more work for someone in my category (want to understand the code under the hood, but am not an experienced coder). For me, it was totally worth it.

For a total beginner, this book and class is the only option to learn deep learning. All other classes/books require some programming experience and especially Python programming experience (1 year minimum, 2 years recommended). So I would completely recommend the Fastai book to a total beginner. You may feel uncomfortable with the top-down teaching approach since most college classes are taught bottom-up. But as long as you try to live with it, you can run cool deep learning projects by learning to use pre-built example notebooks, pre-trained models, and tutorial, and substituting the example data with your own. Think of it as learning to drive a car on your highway of choice, instead of learning to build the engine first to race your car on formula one!
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David Aiken
5つ星のうち5.0 :)
2021年1月12日にカナダでレビュー済み
Amazonで購入
A good buy.
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Raul De Castro
5つ星のうち5.0 El mejor contenido "manos a la obra" al momento
2020年10月27日にメキシコでレビュー済み
Amazonで購入
Excelente impresión y por supuesto, una de las mejores elecciones de libro del tema. Entre un vasto cuerpo de.cosas por aprender, consideraría a esta obra como el eje fundamental y práctico para entrar o profundizar en la practica de Deep Learning.
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Raul De Castro
5つ星のうち5.0 El mejor contenido "manos a la obra" al momento
2020年10月27日にメキシコでレビュー済み
Excelente impresión y por supuesto, una de las mejores elecciones de libro del tema. Entre un vasto cuerpo de.cosas por aprender, consideraría a esta obra como el eje fundamental y práctico para entrar o profundizar en la practica de Deep Learning.
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RUBENS MAU
5つ星のうち5.0 Great book.
2020年9月17日にブラジルでレビュー済み
Amazonで購入
You buy a great book - and you get a fantastic course and community supporting the fast.ai library.
Best way to learn Deep Learning or go deep in the fundamentals.
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