Machine Learning Q and AI
Machine Learning Q and AI
Expand Your Machine Learning & AI Knowledge With 30 In-Depth Questions and Answers
About the Book
Expand Your Machine Learning Knowledge
Machine learning and AI are moving at a rapid pace. Researchers and practitioners are constantly struggling to keep up with the breadth of concepts and techniques. This book provides bite-sized bits of knowledge for your journey from machine learning beginner to expert, covering topics from various machine learning areas. Even experienced machine learning researchers and practitioners will encounter something new that they can add to their arsenal of techniques.
Who Is This Book For?
Machine Learning Q and AI is for people who are already familiar with machine learning and want to learn something new. However, this is not a math or coding book. You won't need to solve any proofs or run any code while reading. In other words, this book is a perfect travel companion or something you can read on your favorite reading chair with your morning coffee.
Print and Digital Books
I'm pleased to announce my collaboration with No Starch Press for the print end ebook editions of my book. This edition has undergone professional editing to enhance its quality. It's now available for at the No Starch Press's website:
About the Author
Table of Contents
-
Preface
- Who Is This Book For?
- What Will You Get Out of This Book?
- How To Read This Book
- Sharing Feedback and Supporting This Book
- Acknowledgements
- About the Author
- Copyright and Disclaimer
- Credits
- Introduction
-
Chapter 1. Neural Networks and Deep Learning
- Q1. Embeddings, Representations, and Latent Space
- Q2. Self-Supervised Learning
- Q3. Few-Shot Learning
- Q4. The Lottery Ticket Hypothesis
- Q5. Reducing Overfitting with Data
- Q6. Reducing Overfitting with Model Modifications
- Q7. Multi-GPU Training Paradigms
- Q8. The Keys to Success of Transformers
- Q9. Generative AI Models
- Q10. Sources of Randomness
-
Chapter 2. Computer Vision
- Q11. Calculating the Number of Parameters
- Q12. The Equivalence of Fully Connected and Convolutional Layers
- Q13. Large Training Sets for Vision Transformers
-
Chapter 3. Natural Language Processing
- Q14. The Distributional Hypothesis
- Q15. Data Augmentation for Text
- Q16. “Self”-Attention
- Q17. Encoder- And Decoder-Style Transformers
- Q18. Using and Finetuning Pretrained Transformers
- Q19. Evaluating Generative Language Models
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Chapter 4. Production, Real-World, And Deployment Scenarios
- Q20. Stateless And Stateful Training
- Q21. Data-Centric AI
- Q22. Speeding Up Inference
- Q23. Data Distribution Shifts
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Chapter 5. Predictive Performance and Model Evaluation
- Q24. Poisson and Ordinal Regression
- Q25. Confidence Intervals
- Q26. Confidence Intervals Versus Conformal Predictions
- Q27. Proper Metrics
- Q28. The K in K-Fold Cross-Validation
- Q29. Training and Test Set Discordance
- Q30. Limited Labeled Data
- Afterword
- Appendix A: Reader Quiz Solutions
- Appendix B: List of Questions
- Notes
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