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Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard, Sylvain Gugger
- Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD
- Jeremy Howard, Sylvain Gugger
- Page: 582
- Format: pdf, ePub, mobi, fb2
- ISBN: 9781492045526
- Publisher: O'Reilly Media, Incorporated
Epub google books download Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard, Sylvain Gugger English version 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 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
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Today, we are making this course, Practical Deep Learning for Coders 2018, generally of interactive notebooks, and is now available for free (with no ads) at course.fast.ai. The new course is built on top of Pytorch, and uses a new library (called fastai) that we International Fellowship applications for Part 2 now open.
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