Deep learning for coders with fastai and PyTorch : AI applications without a PhD / Jeremy Howard and Sylvain Gugger.

Format:

Book

Author/Creator:

Howard, Jeremy, author.

Gugger, Sylvain, author.

Language:

English

Subjects (All):

Data mining.

Natural language processing (Computer science).

Machine learning.

Physical Description:

1 online resource (xxii, 594 pages)

Edition:

1st edition

Place of Publication:

Sebastopol, California : O'Reilly Media, Inc., [2020]

System Details:

text file

Summary:

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

Contents:

Part 1. Deep Learning Journey. Your Deep Learning Journey

From Model to Production

Data Ethics

Part 2. Understanding fastai's Applications. Under the Hood: Training a Digit Classifier

Image Classification

Other Computer Vision Problems

Training a State-of-the-Art Model

Collaborative Filtering Deep Dive

Tabular Modeling Deep Dive

NLP Deep Dive: RNNs

Data Munging with fastai's Mid-Level API

Part 3. Foundations of Deep Learning. A Language Model from Scratch

Convolutional Neural Networks

ResNets

Application Architectures Deep Dive

The Training Process

Part 4. Deep Learning from Scratch. A Neural Net from the Foundations

CNN Interpretation with CAM

A fastai Learner from Scratch

Concluding Thoughts.

Notes:

Includes index.

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781492045472

1492045470

9781492045496

1492045497

9781492045519

1492045519

OCLC:

1180552952