Scan code number of public attention, the corresponding number in a public reply No. homepage download data.
Re: 001
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"Statistical learning methods" Lee Hang
Statistical learning is an important subject in the field of computers and their applications. This book systematically introduces the main methods of statistical learning, especially supervised learning methods, including Perceptron, k-nearest neighbor, naive Bayes method, decision trees, logistic regression and maximum entropy models, support vector machines, upgrade method, em algorithm, hidden Markov models and conditions with the airport. In addition to Chapter 1 Introduction and summary of the last chapter, each chapter introduces a method. Narrative starts with specific problems or examples, Deep clarify thinking, given the necessary mathematical derivation, easy to readers to grasp the essence of statistical learning methods, learn to use. To meet the needs of readers further study, the book also describes some of the research, given the small amount of exercise, a list of major references.
Re: 002
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"Machine Learning" Zhou Zhihua
Machine learning is an important branch of computer science and artificial intelligence. This book serves as introductory textbook in the field, in a covering every aspect of the basics of machine learning as much as possible. In order to make as many readers of this book to understand machine learning, the author tries to use as little mathematical knowledge. However, a small amount of probability, statistics, algebra, optimization, logic, knowledge seems inevitable. Therefore, this book is more 3rd grade suitable for University of science and engineering undergraduate and graduate students, as well as people with similar backgrounds interested in machine learning. for the convenience of the reader, this book appendix gives some basic knowledge related to mathematics Introduction
Re: 003
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"Machine learning real" Peter
Machine learning is a very important field of artificial intelligence research. Information or data capture mode in the context of today's era of big data and extract valuable from the past to make this area of research analysts and mathematicians dedicated more and more people's attention. This book by rows of well-cut examples of daily tasks abandon Python code academic language with efficient reusable explain how to deal with statistical data analysis and data visualization. Readers can learn some of the core of machine learning algorithms and apply some strategic tasks such as classification, forecasts and recommendations, etc.
Re: 004
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"Higher Mathematics" (sixth edition of the book) Tongji University
Re: 005
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"Higher Mathematics" (sixth edition of the two volumes) Tongji University
Re: 006
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"Linear Algebra" (fifth edition) Tongji
Re: 007
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《深度学习》 中文版 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville
本书旨在向读者交付有关深度学习的交互式学习体验。书中不仅阐述深度学习的算法原理，还演示它们的实现和运行。与传统图书不同，本书的每一节都是一个可以下载并运行的 Jupyter记事本，它将文字、公式、图像、代码和运行结果结合在了一起。此外，读者还可以访问并参与书中内容的讨论。
全书的内容分为3个部分：第一部分介绍深度学习的背景，提供预备知识，并包括深度学习最基础的概念和技术；第二部分描述深度学习计算的重要组成部分，还解释近年来令深度学习在多个领域大获成功的卷积神经网络和循环神经网络；第三部分评价优化算法，检验影响深度学习计算性能的重要因素，并分别列举深度学习在计算机视觉和自然语言处理中的重要应用。
本书同时覆盖深度学习的方法和实践，主要面向在校大学生、技术人员和研究人员。阅读本书需要读者了解基本的Python编程或附录中描述的线性代数、微分和概率基础。