نقد و بررسی اجمالیکتاب Deep Learning for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch
by Jeremy Howard , Sylvain Gugger
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.
Review
“If you are looking for a guide that starts at the ground floor and takes you to the cutting edge of research, this is the book for you. Don’t let those PhDs have all the fun—you too can use deep learning to solve practical problems.” — Hal Varian, Emeritus Professor, UC Berkeley; Chief Economist, Google
“As artificial intelligence has moved into the era of deep learning, it behooves all of us to learn as much as possible about how it works. Deep Learning for Coders provides a terrific way to initiate that, even for the uninitiated, achieving the feat of simplifying what most of us would consider highly complex” — Eric Topol, Author of Deep Medicine; Professor: Scripps Research
“Jeremy and Sylvain take you on an interactive–in the most literal sense as each line of code can be run in a notebook–journey through the loss valleys and performance peaks of deep learning. Peppered with thoughtful anecdotes and practical intuitions from years of developing and teaching machine learning, the book strikes the rare balance of communicating deeply technical concepts in a conversational and light-hearted way. In a faithful translation of fast.ai’s award-winning online teaching philosophy, the book provides you with state-of-the-art practical tools and the real-world examples to put them to use. Whether you’re a beginner or a veteran, this book will fast-track your deep learning journey and take you to new heights–and depths.” — Sebastian Ruder, Research Scientist, Deepmind
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