Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
Howard, Jeremy; Gugger, Sylvain
Language: English
Published by O'Reilly Media, 2020
- Softcover
- Used
Seller: Goodwill of Central and Coastal Virginia, Richmond, VA, U.S.A.Goodwill of Central and Coastal Virginia
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- Title
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
- Author
- Howard, Jeremy; Gugger, Sylvain
- Publisher
- O'Reilly Media
- Publication year
- 2020
- Condition
- acceptable
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1492045527
- ISBN 13
- 9781492045526
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
"Synopsis" may belong to another edition of 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.
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).
"About the title" may belong to another edition of this title.
Goodwill of Central and Coastal Virginia
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