Deep Learning for Coders with fastai and PyTorch

Spis treści książki

• Preface
  • Who This Book Is For
  • What You Need to Know
  • What You Will Learn
  • OReilly 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 Doesnt 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: YouTubes 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: Thats a Wrap!
• II. Understanding fastais 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
    • Stepping with a Learning Rate
    • An End-to-End SGD Example
      • Step 1: Initialize the parameters
      • Step 2: Calculate the predictions
      • Step 3: Calculate the loss
      • Step 4: Calculate the gradients
      • Step 5: Step the weights
      • Step 6: Repeat the process
      • Step 7: Stop
    • Summarizing Gradient Descent
  • The MNIST Loss Function
    • Sigmoid
    • SGD and Mini-Batches
  • Putting It All Together
    • Creating an Optimizer
  • Adding a Nonlinearity
    • Going Deeper
  • Jargon Recap
  • Questionnaire
    • Further Research
• 5. Image Classification
  • From Dogs and Cats to Pet Breeds
  • Presizing
    • Checking and Debugging a DataBlock
  • Cross-Entropy Loss
    • Viewing Activations and Labels
    • Softmax
    • Log Likelihood
    • Taking the log
  • Model Interpretation
  • Improving Our Model
    • The Learning Rate Finder
    • Unfreezing and Transfer Learning
    • Discriminative Learning Rates
    • Selecting the Number of Epochs
    • Deeper Architectures
  • Conclusion
  • Questionnaire
    • Further Research
• 6. Other Computer Vision Problems
  • Multi-Label Classification
    • The Data
    • Constructing a DataBlock
    • Binary Cross Entropy
  • Regression
    • Assembling the Data
    • Training a Model
  • Conclusion
  • Questionnaire
    • Further Research
• 7. Training a State-of-the-Art Model
  • Imagenette
  • Normalization
  • Progressive Resizing
  • Test Time Augmentation
  • Mixup
  • Label Smoothing
  • Conclusion
  • Questionnaire
    • Further Research
• 8. Collaborative Filtering Deep Dive
  • A First Look at the Data
  • Learning the Latent Factors
  • Creating the DataLoaders
  • Collaborative Filtering from Scratch
    • Weight Decay
    • Creating Our Own Embedding Module
  • Interpreting Embeddings and Biases
    • Using fastai.collab
    • Embedding Distance
  • Bootstrapping a Collaborative Filtering Model
  • Deep Learning for Collaborative Filtering
  • Conclusion
  • Questionnaire
    • Further Research
• 9. Tabular Modeling Deep Dive
  • Categorical Embeddings
  • Beyond Deep Learning
  • The Dataset
    • Kaggle Competitions
    • Look at the Data
  • Decision Trees
    • Handling Dates
    • Using TabularPandas and TabularProc
    • Creating the Decision Tree
    • Categorical Variables
  • Random Forests
    • Creating a Random Forest
    • Out-of-Bag Error
  • Model Interpretation
    • Tree Variance for Prediction Confidence
    • Feature Importance
    • Removing Low-Importance Variables
    • Removing Redundant Features
    • Partial Dependence
    • Data Leakage
    • Tree Interpreter
  • Extrapolation and Neural Networks
    • The Extrapolation Problem
    • Finding Out-of-Domain Data
    • Using a Neural Network
  • Ensembling
    • Boosting
    • Combining Embeddings with Other Methods
  • Conclusion
  • Questionnaire
    • Further Research
• 10. NLP Deep Dive: RNNs
  • Text Preprocessing
    • Tokenization
    • Word Tokenization with fastai
    • Subword Tokenization
    • Numericalization with fastai
    • Putting Our Texts into Batches for a Language Model
  • Training a Text Classifier
    • Language Model Using DataBlock
    • Fine-Tuning the Language Model
    • Saving and Loading Models
    • Text Generation
    • Creating the Classifier DataLoaders
    • Fine-Tuning the Classifier
  • Disinformation and Language Models
  • Conclusion
  • Questionnaire
    • Further Research
• 11. Data Munging with fastais Mid-Level API
  • Going Deeper into fastais Layered API
    • Transforms
    • Writing Your Own Transform
    • Pipeline
  • TfmdLists and Datasets: Transformed Collections
    • TfmdLists
    • Datasets
  • Applying the Mid-Level Data API: SiamesePair
  • Conclusion
  • Questionnaire
    • Further Research
  • Understanding fastais Applications: Wrap Up
• III. Foundations of Deep Learning
• 12. A Language Model from Scratch
  • The Data
  • Our First Language Model from Scratch
    • Our Language Model in PyTorch
    • Our First Recurrent Neural Network
  • Improving the RNN
    • Maintaining the State of an RNN
    • Creating More Signal
  • Multilayer RNNs
    • The Model
    • Exploding or Disappearing Activations
  • LSTM
    • Building an LSTM from Scratch
    • Training a Language Model Using LSTMs
  • Regularizing an LSTM
    • Dropout
    • Activation Regularization and Temporal Activation Regularization
    • Training a Weight-Tied Regularized LSTM
  • Conclusion
  • Questionnaire
    • Further Research
• 13. Convolutional Neural Networks
  • The Magic of Convolutions
    • Mapping a Convolutional Kernel
    • Convolutions in PyTorch
    • Strides and Padding
    • Understanding the Convolution Equations
  • Our First Convolutional Neural Network
    • Creating the CNN
    • Understanding Convolution Arithmetic
    • Receptive Fields
    • A Note About Twitter
  • Color Images
  • Improving Training Stability
    • A Simple Baseline
    • Increase Batch Size
    • 1cycle Training
    • Batch Normalization
  • Conclusion
  • Questionnaire
    • Further Research
• 14. ResNets
  • Going Back to Imagenette
  • Building a Modern CNN: ResNet
    • Skip Connections
    • A State-of-the-Art ResNet
    • Bottleneck Layers
  • Conclusion
  • Questionnaire
    • Further Research
• 15. Application Architectures Deep Dive
  • Computer Vision
    • cnn_learner
    • unet_learner
    • A Siamese Network
  • Natural Language Processing
  • Tabular
  • Conclusion
  • Questionnaire
    • Further Research
• 16. The Training Process
  • Establishing a Baseline
  • A Generic Optimizer
  • Momentum
  • RMSProp
  • Adam
  • Decoupled Weight Decay
  • Callbacks
    • Creating a Callback
    • Callback Ordering and Exceptions
  • Conclusion
  • Questionnaire
    • Further Research
  • Foundations of Deep Learning: Wrap Up
• IV. Deep Learning from Scratch
• 17. A Neural Net from the Foundations
  • Building a Neural Net Layer from Scratch
    • Modeling a Neuron
    • Matrix Multiplication from Scratch
    • Elementwise Arithmetic
    • Broadcasting
      • Broadcasting with a scalar
      • Broadcasting a vector to a matrix
      • Broadcasting rules
    • Einstein Summation
  • The Forward and Backward Passes
    • Defining and Initializing a Layer
    • Gradients and the Backward Pass
    • Refactoring the Model
    • Going to PyTorch
  • Conclusion
  • Questionnaire
    • Further Research
• 18. CNN Interpretation with CAM
  • CAM and Hooks
  • Gradient CAM
  • Conclusion
  • Questionnaire
    • Further Research
• 19. A fastai Learner from Scratch
  • Data
    • Dataset
  • Module and Parameter
    • Simple CNN
  • Loss
  • Learner
    • Callbacks
    • Scheduling the Learning Rate
  • Conclusion
  • Questionnaire
    • Further Research
• 20. Concluding Thoughts
• A. Creating a Blog
  • Blogging with GitHub Pages
    • Creating the Repository
    • Setting Up Your Home Page
    • Creating Posts
    • Synchronizing GitHub and Your Computer
  • Jupyter for Blogging
• B. Data Project Checklist
  • Data Scientists
  • Strategy
  • Data
  • Analytics
  • Implementation
  • Maintenance
  • Constraints
• Index

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