Preface
Foreword
Part 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
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
Disinformation
Identifying and Addressing Ethical Issues
Analyze a Project You Are Working On
Processes to Implement
The Power of Diversity
……
Part II. Understanding fastai's applications
Part III. Foundations of Deep Learning
Part IV. Deep learning from Scratch
Index