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About Jeremy Howard
I'm a data scientist, researcher, developer, educator, and entrepreneur. I am a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible, and am a Distinguished Research Scientist at the University of San Francisco, am the chair of WAMRI, and am the Chief Scientist at platform.ai.
I have a young daughter, and live in San Francisco, after spending most of my life in Australia. You might have seen me on TV during my brief period of fame as the co-founder of the global Masks4All movement.
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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