The Hundred-Page Machine Learning Book pdf epub mobi txt 电子书 下载 2026
- MachineLearning
- 计算机
- 机器学习
- 数据科学
- 计算机科学
- 经典
- 软件开发
- 软件工程
- 机器学习
- 深度学习
- Python
- 数据科学
- 算法
- 模型
- 实践
- 入门
- 快速学习
- 代码
具体描述
Everything you really need to know in Machine Learning in a hundred pages.
This is the first of its kind "read first, buy later" book. You can find the book online, read it, and then come back to pay for it if you liked the book or found it useful for your work, business or studies.
Review
"This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program."--Deepak Agarwal, VP of Artificial Intelligence at LinkedIn
"This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field."--Karolis Urbonas, Head of Data Science at Amazon
"I wish such a book existed when I was a statistics graduate student trying to learn about machine learning. There is the right amount of math which demystify the centerpiece of an algorithm with succinct but very clear descriptions. I'm also impressed by the widespread coverage and good choices of important methods as an introductory book (not all machine learning books mention things like learning to rank or metric learning). Highly recommended to STEM major students."--Chao Han, VP, Head of R&D at Lucidworks
"This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program."--Sujeet Varakhedi, Head of Engineering at eBay
"The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning. In his book, Andriy Burkov distills the ubiquitous material on Machine Learning into concise and well-balanced intuitive, theoretical and practical elements that bring beginners, managers, and practitioners many life hacks."--Vincent Pollet, Head of Research at Nuance
作者简介
Andriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. Nine years ago, he got a Ph.D. in Artificial Intelligence, and for the last six years, he's been leading a team of machine learning developers at Gartner.
His specialty is natural language processing. His team works on building state-of-the-art multilingual text extraction and normalization systems for production, using both shallow and deep learning technologies.
目录信息
Preface
Chapter 1: Introduction
Part I: Supervised Learning
Chapter 2: Notation and Definitions
Chapter 3: Fundamental Algorithms
Chapter 4: Anatomy of a Learning Algorithm
Chapter 5: Basic Practice
Chapter 6: Neural Networks and Deep Learning
Chapter 7: Problems and Solutions
Chapter 8: Advanced Practice
Part II: Unsupervised and Other Forms of Learning
Chapter 9: Unsupervised Learning
Chapter 10: Other Forms of Learning
Chapter 11: Conclusion
· · · · · · (收起)
读后感
整体进度：[https://github.com/apachecn/ml-book-100-zh/issues/1] 贡献指南：[https://github.com/apachecn/ml-book-100-zh/blob/master/CONTRIBUTING.md] 项目仓库：[https://github.com/apachecn/ml-book-100-zh]
评分整体进度：[https://github.com/apachecn/ml-book-100-zh/issues/1] 贡献指南：[https://github.com/apachecn/ml-book-100-zh/blob/master/CONTRIBUTING.md] 项目仓库：[https://github.com/apachecn/ml-book-100-zh]
评分整体进度：[https://github.com/apachecn/ml-book-100-zh/issues/1] 贡献指南：[https://github.com/apachecn/ml-book-100-zh/blob/master/CONTRIBUTING.md] 项目仓库：[https://github.com/apachecn/ml-book-100-zh]
评分整体进度：[https://github.com/apachecn/ml-book-100-zh/issues/1] 贡献指南：[https://github.com/apachecn/ml-book-100-zh/blob/master/CONTRIBUTING.md] 项目仓库：[https://github.com/apachecn/ml-book-100-zh]
评分整体进度：[https://github.com/apachecn/ml-book-100-zh/issues/1] 贡献指南：[https://github.com/apachecn/ml-book-100-zh/blob/master/CONTRIBUTING.md] 项目仓库：[https://github.com/apachecn/ml-book-100-zh]
用户评价
这本《The Hundred-Page Machine Learning Book》的出版，无疑给许多像我这样，在机器学习的广阔海洋中摸索前行的人带来了一线曙光。我最初接触机器学习时，面对那些厚重的教科书和晦涩难懂的数学公式，常常感到望而生畏，仿佛被一道无形的墙挡在了知识的殿堂之外。这本书的出现，以其精炼的篇幅和直观的讲解方式，极大地降低了入门的门槛。它没有试图涵盖所有前沿的细枝末节，而是精准地抓住了核心概念和最实用的算法框架。我特别欣赏作者在组织材料上的匠心独运，从基础的线性模型到更复杂的神经网络结构，逻辑衔接得天衣无缝，每一步的推导都像是有人在身边耐心地为你指点迷津，让你在不知不觉中构建起一个坚实的知识体系。对于时间有限的专业人士或者希望快速建立全局观的学生来说，这简直是一份无可替代的“速查手册”。它让你明白，要真正掌握机器学习，并不一定非得沉溺于无休止的细节钻研，清晰的理解和正确的框架思维才是通往成功的钥匙。
评分说实话，我原本对这类“精简版”的技术书籍抱持着相当的怀疑态度，总觉得要在有限的篇幅内讲清楚机器学习的精髓，无异于痴人说梦。然而，当我真正翻开《The Hundred-Page Machine Learning Book》后，我的看法彻底被颠覆了。它更像是一份由经验丰富的大师精心提炼的“内功心法”，而非堆砌知识点的“武功秘籍”。书中对一些关键算法的描述，比如支持向量机（SVM）或者随机森林（Random Forest），没有采用那种教科书式的冗长论述，而是直击其背后的数学直觉和实际应用场景，那种清晰度，简直是令人拍案叫绝。我过去花费数周时间都没能彻底理清的梯度下降法的各种优化变体，在这本书里竟然被用寥寥数语和几张清晰的图示就解释得明明白白。这不仅仅是信息压缩，更是一种智慧的提炼，它教会了我如何用最少的代价，获取最大的认知收益，这对于我后续进行项目实践时的快速决策制定，起到了至关重要的指导作用。
评分这本书的价值，绝不仅仅在于它“薄”这个表面特征。真正让我感到震撼的是其内容的深度和广度之间的完美平衡。它没有陷入纯理论的泥潭，也没有流于空洞的感性描述，而是精准地把握住了“工程实践”与“理论基础”之间的那个黄金分割点。我发现，书中对于模型评估和正则化策略的讨论尤其精辟，这些往往是初学者在实际工作中遇到最多麻烦的地方。作者用一种近乎艺术化的方式，将偏差-方差的权衡（Bias-Variance Tradeoff）描绘得生动形象，让我对如何诊断模型性能有了全新的认识。以往我总是在试错中寻找答案，现在，我可以基于书中提供的清晰框架，有条不紊地设计实验，快速定位问题所在。这种从“知道”到“做到”的飞跃，是许多浮于表面的入门书籍所无法给予的。它仿佛是一张高精度地图，让你清楚地知道前方是悬崖还是坦途。
评分从一名老读者兼资深业余爱好者的角度来看，这本书的排版和结构设计堪称教科书级别的典范。它不仅仅是文字的堆砌，更是一种精心编排的阅读体验。每一章节的过渡都极其自然，仿佛在讲述一个连贯的故事，而非一系列孤立的知识点。我注意到作者非常善于利用图表来辅助说明复杂的概念，这些图表简洁、高效，往往比几段文字更有说服力。例如，在解释特征工程的重要性时，书中展示的对比案例，直观地揭示了数据质量对模型性能的决定性影响，这比任何理论上的强调都更有力量。这本书的阅读过程，就像是进行了一次高效的“知识健行”，既有强度的攀登，也有令人心旷神怡的风景（豁然开朗的瞬间）。它不是让你轻松地度过时间，而是让你高效地利用时间，并在合上书本时，真切地感受到知识和能力的增长。
评分我是一名有着多年软件开发经验的工程师，转行数据科学的路上，最让我头疼的就是那些需要深厚数学背景才能理解的概念。我需要的不是一篇关于拓扑学的论文摘要，而是一个能在我的代码中马上用起来的、可解释的工具。恰恰是《The Hundred-Page Machine Learning Book》满足了我的这种“实用主义”需求。它的语言风格非常务实，没有过多的学术腔调，直接切入重点。比如在讲到深度学习基础时，它没有长篇大论地铺陈反向传播的每一个矩阵乘法细节，而是侧重于解释其背后的“信息流”和“学习机制”，这对于我这种更关注“如何实现”和“为什么这样设计”的实践者来说，简直是福音。读完这本书，我感觉自己仿佛获得了一副“翻译器”，能够迅速地将复杂的数学描述转化为可操作的算法步骤，极大地提升了我整合现有开源工具库的能力。
评分一本简介的入门书，定义概念都很清晰，可以当作参考用的工具书。
评分比一般的cheatsheet 要全，部分解释说明需要结合个人笔记和心得。
评分一本简介的入门书，定义概念都很清晰，可以当作参考用的工具书。
评分读完了英文版第一反应，那群算法工程师真的这么多都会吗，大家其实都自学的这么多方法吧，得上多少课才能学完这些。除了reinforcement learning没有讲，其他常用的都介绍了，而且挑的是新的实用的。其实缺点也有，毕竟很多细节都没有，推导粗略，想理解就自己继续探索，文字较为随性，哈哈哈哈????看完印象最深是，一本好书就像一瓶红酒这个比喻
评分(´▽｀)
相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2026 book.quotespace.org All Rights Reserved. 小美书屋 版权所有