Deep learning for coders with fastai and PyTorch

Intro

Preface

Who This Book Is For

What You Need to Know

What You Will Learn

O'Reilly Online Learning

How to Contact Us

Foreword

I. Deep Learning in Practice

1. Your Deep Learning Journey

Deep Learning Is for Everyone

Neural Networks: A Brief History

Who We Are

How to Learn Deep Learning

Your Projects and Your Mindset

The Software: PyTorch, fastai, and Jupyter (And Why It Doesn't Matter)

Your First Model

Getting a GPU Deep Learning Server

Running Your First Notebook

What Is Machine Learning?

What Is a Neural Network?

A Bit of Deep Learning Jargon

Limitations Inherent to Machine Learning

How Our Image Recognizer Works

What Our Image Recognizer Learned

Image Recognizers Can Tackle Non-Image Tasks

Jargon Recap

Deep Learning Is Not Just for Image Classification

Validation Sets and Test Sets

Use Judgment in Defining Test Sets

A Choose Your Own Adventure Moment

Questionnaire

Further Research

2. From Model to Production

The Practice of Deep Learning

Starting Your Project

The State of Deep Learning

Computer vision

Text (natural language processing)

Combining text and images

Tabular data

Recommendation systems

Other data types

The Drivetrain Approach

Gathering Data

From Data to DataLoaders

Data Augmentation

Training Your Model, and Using It to Clean Your Data

Turning Your Model into an Online Application

Using the Model for Inference

Creating a Notebook App from the Model

Turning Your Notebook into a Real App

Deploying Your App

How to Avoid Disaster

Unforeseen Consequences and Feedback Loops

Get Writing!

Questionnaire

Further Research

3. Data Ethics

Key Examples for Data Ethics

Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits

Feedback Loops: YouTube's Recommendation System

Bias: Professor Latanya Sweeney "Arrested"

Why Does This Matter?

Integrating Machine Learning with Product Design

Topics in Data Ethics

Recourse and Accountability

Feedback Loops

Bias

Historical bias

Measurement bias

Aggregation bias

Representation bias

Addressing different types of bias

Disinformation

Identifying and Addressing Ethical Issues

Analyze a Project You Are Working On

Processes to Implement

Ethical lenses

The Power of Diversity

Fairness, Accountability, and Transparency

Role of Policy

The Effectiveness of Regulation

Rights and Policy

Cars: A Historical Precedent

Conclusion

Questionnaire

Further Research

Deep Learning in Practice: That's a Wrap!

II. Understanding fastai's Applications

4. Under the Hood: Training a Digit Classifier

Pixels: The Foundations of Computer Vision

First Try: Pixel Similarity

NumPy Arrays and PyTorch Tensors

Computing Metrics Using Broadcasting

Stochastic Gradient Descent

Calculating Gradients