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
Deep Learning in Practice
Your Deep Learning Journey
From Model to Production
Understanding fastai's Applications
Under the Hood: Training a Digit Classifier
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
Foundations of Deep Learning
A Language Model from Scratch
Convolutional Neural Networks
Application Architectures Deep Dive
The Training Process
Deep Learning from Scratch
A Neural Net from the Foundations
CNN Interpretation with CAM
A fastai Learner from Scratch