The Hundred-Page Machine Learning Book

I. THÔNG TIN SẢN PHẨM

Mã sản phẩm    : STT262
Nhà xuất bản: Andriy Burkov (January 13, 2019)
Số trang    : 135 trang
Tác giả        : Andriy Burkov
Ngôn Ngữ    : Tiếng Anh
ISBN             :199957950X‏
Hình thức    : Bìa Mềm, IN ĐEN TRẮNG
Loại         : Sách gia công
Giấy in        : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng    : Bản in rõ nét, giá rất tốt cho mọi người.

II. MÔ TẢ SẢN PHẨM

1) Mô tả sản phẩm
As its title says, it's the hundred-page machine learning book. It was written by an expert in machine learning holding a Ph.D. in Artificial Intelligence with almost two decades of industry experience in computer science and hands-on machine learning.

This is a unique book in many aspects. It is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.

The book contains only those parts of the huge body of material on machine learning developed since the 1960s that have proven to have a significant practical value. A beginner in machine learning will find in this book just enough details to get a comfortable level of understanding of the field and start asking the right questions. Practitioners with experience will use this book as a collection of pointers to the directions of further self-improvement.

The book also comes in handy when brainstorming at the beginning of a project, when you try to answer the question whether a given technical or business problem is "machine-learnable" and, if yes, which techniques you should try to solve it.

The book comes with a wiki which contains pages that extend some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources. Thanks to the continuously updated wiki this book like a good wine keeps getting better after you buy it.

Table of Contents
Chapter 1: Introduction
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
Chapter 9: Unsupervised Learning
Chapter 10: Other Forms of Learning
Chapter 11: Conclusion

2) Tác giả
Andriy Burkov holds a PhD in Artificial Intelligence, he works as a senior data scientist and machine learning team leader at Gartner.