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Deep Learning for Coders with fastai and PyTorch - 9781492045526

Description: Deep Learning for Coders with fastai and PyTorchAI Applications Without a PhD Author(s): Sylvain Gugger, Jeremy Howard Format: Paperback Publisher: O'Reilly Media, United States Imprint: O'Reilly Media ISBN-13: 9781492045526, 978-1492045526 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

Price: 42.68 GBP

Location: Aldershot

End Time: 2024-10-04T07:45:00.000Z

Shipping Cost: 26.41 GBP

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Deep Learning for Coders with fastai and PyTorch - 9781492045526

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Book Title: Deep Learning for Coders with fastai and PyTorch

Item Height: 233 mm

Item Width: 178 mm

Author: Jeremy Howard, Sylvain Gugger

Publication Name: Deep Learning for Coders with Fastai and Pytorch: Ai Applications without a Phd

Format: Paperback

Language: English

Publisher: O'reilly Media, INC International Concepts USA

Subject: Computer Science

Publication Year: 2020

Type: Textbook

Number of Pages: 350 Pages

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