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Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
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  • Title: Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
  • Author(s): Jeremy Howard and Sylvain Gugger
  • Publisher: O'Reilly Media; 1st edition (August 11, 2020); eBook (GitHub Edition: Jupyter Notebooks)
  • Permission: The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use.
  • Paperback: 624 pages
  • eBook: Jupyter Notebooks
  • Language: English
  • ISBN-10: 1492045527
  • ISBN-13: 978-1492045526
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Book Description

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 the Authors
  • Jeremy Howard is an entrepreneur, business strategist, developer, and educator.
  • Sylvain Gugger is a former teacher and a Research Scientist at
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