Machine Learning Q and AI Learning Material Detailed Introduction

Basic Information

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

Author: Sebastian Raschka

Publisher: No Starch Press

Copyright: © 2024-2025 by Sebastian Raschka

Access Method: Free Online Reading

Book Overview

This is a practical guide focusing on core concepts of machine learning and artificial intelligence. Presented in the form of 30 key questions and answers, it provides refined knowledge points for learners ranging from machine learning beginners to experts. Sebastian Raschka, a renowned educator in the field of machine learning, distills complex AI-related topics into easy-to-understand, practical takeaways.

Learning Objectives

• Help learners keep pace with the rapid advancements in machine learning and AI.
• Cover topics across various machine learning domains.
• Provide a learning path from beginner to expert for newcomers.
• Even experienced machine learning researchers and practitioners can discover new techniques and methods.

Book Structure

Part I: Neural Networks and Deep Learning

• Chapter 1: Embeddings, Latent Space, and Representations
• Chapter 2: Self-Supervised Learning
• Chapter 3: Few-Shot Learning
• Chapter 4: The Lottery Ticket Hypothesis
• Chapter 5: Reducing Overfitting with Data
• Chapter 6: Reducing Overfitting with Model Modifications
• Chapter 7: Multi-GPU Training Paradigms
• Chapter 8: The Success of Transformers
• Chapter 9: Generative AI Models
• Chapter 10: Sources of Randomness

Part II: Computer Vision

• Chapter 11: Calculating the Number of Parameters
• Chapter 12: Fully Connected and Convolutional Layers
• Chapter 13: Large Training Sets for Vision Transformers

Part III: Natural Language Processing

• Chapter 14: The Distributional Hypothesis
• Chapter 15: Data Augmentation for Text
• Chapter 16: Self-Attention
• Chapter 17: Encoder- and Decoder-Style Transformers
• Chapter 18: Using and Fine-Tuning Pretrained Transformers
• Chapter 19: Evaluating Generative Large Language Models

Part IV: Production and Deployment

• Chapter 20: Stateless and Stateful Training
• Chapter 21: Data-Centric AI
• Chapter 22: Speeding Up Inference
• Chapter 23: Data Distribution Shifts

Part V: Predictive Performance and Model Evaluation

• Chapter 24: Poisson and Ordinal Regression
• Chapter 25: Confidence Intervals
• Chapter 26: Confidence Intervals vs. Conformal Predictions
• Chapter 27: Proper Metrics
• Chapter 28: The k in k-Fold Cross-Validation
• Chapter 29: Training and Test Set Discordance
• Chapter 30: Limited Labeled Data

Expert Recommendations

Cameron R. Wolfe (Author of Deep Learning Focus)

"Sebastian has a gift for distilling complex, AI-related topics into practical takeaways that can be understood by anyone. His new book, Machine Learning Q and AI, is another great resource for AI practitioners of any level."

Chip Huyen (Author of Designing Machine Learning Systems)

"Sebastian uniquely combines academic depth, engineering agility, and the ability to demystify complex ideas. He can go deep into any theoretical topics, experiment to validate new ideas, then explain them all to you in simple words. If you're starting your journey into machine learning, Sebastian is your guide."

Chris Albon (Director of Machine Learning, The Wikimedia Foundation)

"One could hardly ask for a better guide than Sebastian, who is, without exaggeration, the best machine learning educator currently in the field. On each page, Sebastian not only imparts his extensive knowledge but also shares the passion and curiosity that mark true expertise."

Ronald T. Kneusel (Author of How AI Works)

"Sebastian Raschka's new book, Machine Learning Q and AI, is a one-stop shop for overviews of crucial AI topics beyond the core covered in most introductory courses…If you have already stepped into the world of AI via deep neural networks, then this book will give you what you need to locate and understand the next level."

Learning Features

1. Q&A-based Learning: Systematically covers important concepts in machine learning and AI through 30 core questions.
2. Practical Orientation: Focuses on practical applications, providing actionable technical insights.
3. Balancing Depth and Breadth: Offers both theoretical depth and practical breadth.
4. Progressive Learning: Suitable for learners of all levels, from beginners to experts.
5. Free Access: Provides free online reading.

Target Audience

• Machine learning beginners
• AI practitioners and researchers
• Students looking to systematically learn modern deep learning techniques
• Tech professionals who need to keep up with AI development trends
• Engineers interested in AI applications in production environments

Learning Suggestions

1. It is recommended to study in chapter order, as each section has a logical progression.
2. After studying each chapter, it is advisable to practice with real-world projects.
3. Pay attention to the Production and Deployment content in Part IV, which covers practical application knowledge often missing from many tutorials.
4. The Model Evaluation content in Part V is crucial for model selection and optimization in practical work.

Access Methods

• Free Online Reading: Directly access the author's website
• Purchase Physical Book: Amazon or No Starch Press
• Support the Author: Subscribe to Sebastian's Substack blog or write an Amazon review