Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD - Softcover

Howard, Jeremy; Gugger, Sylvain

Softcover

ISBN 10: 1492045527 ISBN 13: 9781492045526

Publisher: O'Reilly Media, 2020

Synopsis

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

About this title

About the Author

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, and data science and web development tutorials and discussions.

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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).

From the Inside Flap

Deep learning is a powerful new technology, and we believe it should be applied across many disciplines. Domain experts are the most likely to find new applications of it, and we need more people from all backgrounds to get involved and start using it.

That's why Jeremy cofounded fast.ai, to make deep learning easier to use through free online courses and software. Sylvain is a research engineer at Hugging Face. Previously he was a research scientist at fast.ai and a former mathematics and computer science teacher in a program that prepares students for entry into France's elite universities. Together, we wrote this book in the hope of putting deep learning into the hands of as many people as possible.


Who This Book Is For


If you are a complete beginner to deep learning and machine learning, you are most welcome here. Our only expectation is that you already know how to code, preferably in Python. In this book, we will be showing you how to achieve world-class results, including techniques from the latest research. As we will show, this doesn't require advanced mathematical training or years of study. It just requires a bit of common sense and tenacity.


What You Need to Know

As we said before, the only prerequisite is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course. It doesn't matter if you remember little of it right now; we will brush up on it as needed. Khan Academy has great free resources online that can help. We are not saying that deep learning doesn't use math beyond high school level, but we will teach you (or direct you to resources that will teach you) the basics you need as we cover the subjects that require them.

The book starts with the big picture and progressively digs beneath the surface, so you may need, from time to time, to put it aside and go learn some additional topic (a way of coding something or a bit of math). That is completely OK, and it's the way we intend the book to be read. Start browsing it, and consult additional resources only as needed.

All the code examples shown in this book are available online in the form of Jupyter notebooks (don't worry; you will learn all about what Jupyter is in Chapter 1). This is an interactive version of the book, where you can actually execute the code and experiment with it. See the book's website for more information. The website also contains up-to-date information on setting up the various tools we present and some additional bonus chapters.

What You Will Learn

After reading this book, you will know the following:

• How to train models that achieve state-of-the-art results in
• - Computer vision, including image classification (e.g., classifying pet photos by breed) and image localization and detection (e.g., finding the animals in an image)
• - Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and language modeling
• - Tabular data (e.g., sales prediction) with categorical data, continuous data, and mixed data, including time series
• Collaborative filtering (e.g., movie recommendation)
• How to turn your models into web applications
• Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models
• The latest deep learning techniques that really matter in practice
• How to read a deep learning research paper
• How to implement deep learning algorithms from scratch
• How to think about the ethical implications of your work, to help ensure that you're making the world a better place and that your work isn't misused for harm

Edition details

• Publisher: O'Reilly Media
• Publication date: 2020
• Language: English
• ISBN 10: 1492045527
• ISBN 13: 9781492045526
• Binding: Paperback
• Edition number: 1
• Number of pages: 621