Machine Learning Q and AI
Expand Your Machine Learning & AI Knowledge With 30 In-Depth Questions and Answers
240
READERS
237
PAGES
60 DAYS
GUARANTEE
ENGLISH
PDF
EPUB
WEB
Book Description
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.
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
Q15. The Distributional Hypothesis
Q16. Data Augmentation for Text
Q17. “Self”-Attention
Q18. Encoder- And Decoder-Style Transformers
Q19. Using and Finetuning Pretrained Transformers
Q20. Evaluating Generative Language Models
Chapter 4. Production, Real-World, And Deployment Scenarios
Q21. Stateless And Stateful Training
Q22. Data-Centric AI
Q23. Speeding Up Inference
Q24. Data Distribution Shifts
Chapter 5. Predictive Performance and Model Evaluation
Q25. Poisson and Ordinal Regression
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
