Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Technical Reviewer
BRIEF CONTENTS
CONTENTS IN DETAIL
FOREWORD
ACKNOWLEDGMENTS
INTRODUCTION
   Who Is This Book For?
   What Will You Get Out of This Book?
   How to Read This Book
   Online Resources
PART I: NEURAL NETWORKS AND DEEP LEARNING
1. EMBEDDINGS, LATENT SPACE, AND REPRESENTATIONS
   Embeddings
   Latent Space
   Representation
   Exercises
   References
2. SELF-SUPERVISED LEARNING
   Self-Supervised Learning vs. Transfer Learning
   Leveraging Unlabeled Data
   Self-Prediction and Contrastive Self-Supervised Learning
   Exercises
   References
3. FEW-SHOT LEARNING
   Datasets and Terminology
   Exercises
4. THE LOTTERY TICKET HYPOTHESIS
   The Lottery Ticket Training Procedure
   Practical Implications and Limitations
   Exercises
   References
5. REDUCING OVERFITTING WITH DATA
   Common Methods
      Collecting More Data
      Data Augmentation
      Pretraining
   Other Methods
   Exercises
   References
6. REDUCING OVERFITTING WITH MODEL MODIFICATIONS
   Common Methods
      Regularization
      Smaller Models
      Caveats with Smaller Models
      Ensemble Methods
   Other Methods
   Choosing a Regularization Technique
   Exercises
   References
7. MULTI-GPU TRAINING PARADIGMS
   The Training Paradigms
      Model Parallelism
      Data Parallelism
      Tensor Parallelism
      Pipeline Parallelism
      Sequence Parallelism
   Recommendations
   Exercises
   References
8. THE SUCCESS OF TRANSFORMERS
   The Attention Mechanism
   Pretraining via Self-Supervised Learning
   Large Numbers of Parameters
   Easy Parallelization
   Exercises
   References
9. GENERATIVE AI MODELS
   Generative vs. Discriminative Modeling
   Types of Deep Generative Models
      Energy-Based Models
      Variational Autoencoders
      Generative Adversarial Networks
      Flow-Based Models
      Autoregressive Models
      Diffusion Models
      Consistency Models
   Recommendations
   Exercises
   References
10. SOURCES OF RANDOMNESS
   Model Weight Initialization
   Dataset Sampling and Shuffling
   Nondeterministic Algorithms
   Different Runtime Algorithms
   Hardware and Drivers
   Randomness and Generative AI
   Exercises
   References
PART II: COMPUTER VISION
11. CALCULATING THE NUMBER OF PARAMETERS
   How to Find Parameter Counts
      Convolutional Layers
      Fully Connected Layers
   Practical Applications
   Exercises
12. FULLY CONNECTED AND CONVOLUTIONAL LAYERS
   When the Kernel and Input Sizes Are Equal
   When the Kernel Size Is 1
   Recommendations
   Exercises
13. LARGE TRAINING SETS FOR VISION TRANSFORMERS
   Inductive Biases in CNNs
   ViTs Can Outperform CNNs
   Inductive Biases in ViTs
   Recommendations
   Exercises
   References
PART III: NATURAL LANGUAGE PROCESSING
14. THE DISTRIBUTIONAL HYPOTHESIS
   Word2vec, BERT, and GPT
   Does the Hypothesis Hold?
   Exercises
   References
15. DATA AUGMENTATION FOR TEXT
   Synonym Replacement
   Word Deletion
   Word Position Swapping
   Sentence Shuffling
   Noise Injection
   Back Translation
   Synthetic Data
   Recommendations
   Exercises
   References
16. SELF-ATTENTION
   Attention in RNNs
   The Self-Attention Mechanism
   Exercises
   References
17. ENCODER- AND DECODER-STYLE TRANSFORMERS
   The Original Transformer
      Encoders
      Decoders
   Encoder-Decoder Hybrids
   Terminology
   Contemporary Transformer Models
   Exercises
   References
18. USING AND FINE-TUNING PRETRAINED TRANSFORMERS
   Using Transformers for Classification Tasks
   In-Context Learning, Indexing, and Prompt Tuning
   Parameter-Efficient Fine-Tuning
   Reinforcement Learning with Human Feedback
   Adapting Pretrained Language Models
   Exercises
   References
19. EVALUATING GENERATIVE LARGE LANGUAGE MODELS
   Evaluation Metrics for LLMs
      Perplexity
      BLEU Score
      ROUGE Score
      BERTScore
   Surrogate Metrics
   Exercises
   References
PART IV: PRODUCTION AND DEPLOYMENT
20. STATELESS AND STATEFUL TRAINING
   Stateless (Re)training
   Stateful Training
   Exercises
21. DATA-CENTRIC AI
   Data-Centric vs. Model-Centric AI
   Recommendations
   Exercises
   References
22. SPEEDING UP INFERENCE
   Parallelization
   Vectorization
   Loop Tiling
   Operator Fusion
   Quantization
   Exercises
   References
23. DATA DISTRIBUTION SHIFTS
   Covariate Shift
   Label Shift
   Concept Drift
   Domain Shift
   Types of Data Distribution Shifts
   Exercises
   References
PART V: PREDICTIVE PERFORMANCE AND MODEL EVALUATION
24. POISSON AND ORDINAL REGRESSION
   Exercises
25. CONFIDENCE INTERVALS
   Defining Confidence Intervals
   The Methods
      Method 1: Normal Approximation Intervals
      Method 2: Bootstrapping Training Sets
      Method 3: Bootstrapping Test Set Predictions
      Method 4: Retraining Models with Different Random Seeds
   Recommendations
   Exercises
   References
26. CONFIDENCE INTERVALS VS. CONFORMAL PREDICTIONS
   Confidence Intervals and Prediction Intervals
   Prediction Intervals and Conformal Predictions
   Prediction Regions, Intervals, and Sets
   Computing Conformal Predictions
   A Conformal Prediction Example
   The Benefits of Conformal Predictions
   Recommendations
   Exercises
   References
27. PROPER METRICS
   The Criteria
   The Mean Squared Error
   The Cross-Entropy Loss
   Exercises
28. THE K IN K-FOLD CROSS-VALIDATION
   Trade-offs in Selecting Values for k
   Determining Appropriate Values for k
   Exercises
   References
29. TRAINING AND TEST SET DISCORDANCE
   Exercises
30. LIMITED LABELED DATA
   Improving Model Performance with Limited Labeled Data
      Labeling More Data
      Bootstrapping the Data
      Transfer Learning
      Self-Supervised Learning
      Active Learning
      Few-Shot Learning
      Meta-Learning
      Weakly Supervised Learning
      Semi-Supervised Learning
      Self-Training
      Multi-Task Learning
      Multimodal Learning
      Inductive Biases
   Recommendations
   Exercises
   References
AFTERWORD
APPENDIX: ANSWERS TO THE EXERCISES
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Chapter 21
Chapter 22
Chapter 23
Chapter 24
Chapter 25
Chapter 26
Chapter 27
Chapter 28
Chapter 29
Chapter 30
INDEX