Machine Learning Q and AI

Description

Table of Contents Preface Who Is This Book For? What Will You Get Out of This Book? How To Read This Book Discussion Forum 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 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