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
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