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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

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Book Description

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.

This open book is licensed under a GNU Free Documentation License (GNU FDL). Free download in PDF format is not available. You can read Deep Learning for Coders with Fastai and PyTorch book online for free.

Table of Contents

Part I
Deep Learning in Practice
Chapter 1
Your Deep Learning Journey
Chapter 2
From Model to Production
Chapter 3
Data Ethics
Part II
Understanding fastai's Applications
Chapter 4
Under the Hood: Training a Digit Classifier
Chapter 5
Image Classification
Chapter 6
Other Computer Vision Problems
Chapter 7
Training a State-of-the-Art Model
Chapter 8
Collaborative Filtering Deep Dive
Chapter 9
Tabular Modeling Deep Dive
Chapter 10
NLP Deep Dive: RNNs
Chapter 11
Data Munging with fastai's Mid-Level API
Part III
Foundations of Deep Learning
Chapter 12
A Language Model from Scratch
Chapter 13
Convolutional Neural Networks
Chapter 14
Chapter 15
Application Architectures Deep Dive
Chapter 16
The Training Process
Part IV
Deep Learning from Scratch
Chapter 17
A Neural Net from the Foundations
Chapter 18
CNN Interpretation with CAM
Chapter 19
A fastai Learner from Scratch
Chapter 20
Concluding Thoughts

Book Details

Deep Learning for Coders with Fastai and PyTorch
Computer Science
O'Reilly Media
ISBN13 Digital
ISBN10 Digital

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