Machine Learning Q and AI 学习资料详细介绍

基本信息

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

作者： Sebastian Raschka

出版社： No Starch Press

版权： © 2024-2025 by Sebastian Raschka

访问方式： 免费在线阅读

课程概述

这是一本专注于机器学习和人工智能核心概念的实用指南，通过30个关键问题和答案的形式，为从机器学习初学者到专家的学习者提供了精炼的知识点。Sebastian Raschka 作为机器学习领域的知名教育者，将复杂的AI相关主题提炼成易于理解的实用要点。

学习目标

• 帮助学习者跟上机器学习和AI快速发展的步伐
• 涵盖各种机器学习领域的主题
• 为初学者提供从入门到专家的学习路径
• 即使是经验丰富的机器学习研究者和实践者也能发现新的技术和方法

课程结构

第一部分：神经网络和深度学习 (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

第二部分：计算机视觉 (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

第三部分：自然语言处理 (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

第四部分：生产和部署 (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

第五部分：预测性能和模型评估 (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

专家推荐

Cameron R. Wolfe (Deep Learning Focus 作者)

"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 (《Designing Machine Learning Systems》作者)

"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 (The Wikimedia Foundation 机器学习总监)

"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 (《How AI Works》作者)

"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."

学习特色

1. 问答式学习： 通过30个核心问题，系统性地覆盖机器学习和AI的重要概念
2. 实用导向： 注重实际应用，提供可操作的技术要点
3. 深度与广度并重： 既有理论深度，也有实践广度
4. 渐进式学习： 适合不同水平的学习者，从初学者到专家
5. 免费访问： 提供免费在线阅读

适合人群

• 机器学习初学者
• AI从业者和研究者
• 希望系统学习现代深度学习技术的学生
• 需要跟上AI发展趋势的技术专业人士
• 对生产环境中的AI应用感兴趣的工程师

学习建议

1. 建议按照章节顺序学习，每个部分都有其逻辑递进关系
2. 每章学习后可以结合实际项目进行实践
3. 关注第四部分的生产和部署内容，这是很多教程缺少的实际应用知识
4. 第五部分的模型评估内容对于实际工作中的模型选择和优化非常重要

获取方式

• 免费在线阅读： 直接访问作者网站
• 购买实体书： Amazon 或 No Starch Press
• 支持作者： 订阅 Sebastian's Substack blog 或撰写 Amazon 评论