فهرست مطالب

• Preface
• Foreword
• Part I. Deep Learning in Practice
• Chapter 1. Your Deep Learning Journey
• Deep Learning Is for Everyone
• Neural Networks: A Brief History
• Who We Are
• How to Learn Deep Learning
• The Software: PyTorch, fastai, and Jupyter (And Why It Doesn’t Matter)
• Your First Model
• Deep Learning Is Not Just for Image Classification
• Validation Sets and Test Sets
• A Choose Your Own Adventure Moment
• Questionnaire
• Chapter 2. From Model to Production
• The Practice of Deep Learning
• Gathering Data
• From Data to DataLoaders
• Training Your Model, and Using It to Clean Your Data
• Turning Your Model into an Online Application
• How to Avoid Disaster
• Get Writing!
• Questionnaire
• Chapter 3. Data Ethics
• Key Examples for Data Ethics
• Integrating Machine Learning with Product Design
• Topics in Data Ethics
• Identifying and Addressing Ethical Issues
• Role of Policy
• Conclusion
• Questionnaire
• Deep Learning in Practice: That’s a Wrap!
• Part II. Understanding fastai’s Applications
• Chapter 4. Under the Hood: Training a Digit Classifier
• Pixels: The Foundations of Computer Vision
• First Try: Pixel Similarity
• Computing Metrics Using Broadcasting
• Stochastic Gradient Descent
• The MNIST Loss Function
• Putting It All Together
• Adding a Nonlinearity
• Jargon Recap
• Questionnaire
• Chapter 5. Image Classification
• From Dogs and Cats to Pet Breeds
• Presizing
• Cross-Entropy Loss
• Model Interpretation
• Improving Our Model
• Conclusion
• Questionnaire
• Chapter 6. Other Computer Vision Problems
• Multi-Label Classification
• Regression
• Conclusion
• Questionnaire
• Chapter 7. Training a State-of-the-Art Model
• Imagenette
• Normalization
• Progressive Resizing
• Test Time Augmentation
• Mixup
• Label Smoothing
• Conclusion
• Questionnaire
• Chapter 8. Collaborative Filtering Deep Dive
• A First Look at the Data
• Learning the Latent Factors
• Creating the DataLoaders
• Collaborative Filtering from Scratch
• Interpreting Embeddings and Biases
• Bootstrapping a Collaborative Filtering Model
• Deep Learning for Collaborative Filtering
• Conclusion
• Questionnaire
• Chapter 9. Tabular Modeling Deep Dive
• Categorical Embeddings
• Beyond Deep Learning
• The Dataset
• Decision Trees
• Random Forests
• Model Interpretation
• Extrapolation and Neural Networks
• Ensembling
• Conclusion
• Questionnaire
• Chapter 10. NLP Deep Dive: RNNs
• Text Preprocessing
• Training a Text Classifier
• Disinformation and Language Models
• Conclusion
• Questionnaire
• Chapter 11. Data Munging with fastai’s Mid-Level API
• Going Deeper into fastai’s Layered API
• TfmdLists and Datasets: Transformed Collections
• Applying the Mid-Level Data API: SiamesePair
• Conclusion
• Questionnaire
• Understanding fastai’s Applications: Wrap Up
• Part III. Foundations of Deep Learning
• Chapter 12. A Language Model from Scratch
• The Data
• Our First Language Model from Scratch
• Improving the RNN
• Multilayer RNNs
• LSTM
• Regularizing an LSTM
• Conclusion
• Questionnaire
• Chapter 13. Convolutional Neural Networks
• The Magic of Convolutions
• Our First Convolutional Neural Network
• Color Images
• Improving Training Stability
• Conclusion
• Questionnaire
• Chapter 14. ResNets
• Going Back to Imagenette
• Building a Modern CNN: ResNet
• Conclusion
• Questionnaire
• Chapter 15. Application Architectures Deep Dive
• Computer Vision
• Natural Language Processing
• Tabular
• Conclusion
• Questionnaire
• Chapter 16. The Training Process
• Establishing a Baseline
• A Generic Optimizer
• Momentum
• RMSProp
• Adam
• Decoupled Weight Decay
• Callbacks
• Conclusion
• Questionnaire
• Foundations of Deep Learning: Wrap Up
• Part IV. Deep Learning from Scratch
• Chapter 17. A Neural Net from the Foundations
• Building a Neural Net Layer from Scratch
• The Forward and Backward Passes
• Conclusion
• Questionnaire
• Chapter 18. CNN Interpretation with CAM
• CAM and Hooks
• Gradient CAM
• Conclusion
• Questionnaire
• Chapter 19. A fastai Learner from Scratch
• Data
• Module and Parameter
• Loss
• Learner
• Conclusion
• Questionnaire
• Chapter 20. Concluding Thoughts
• Appendices
• A. Creating a Blog
• B. Data Project Checklist
• Index