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实变函数习题集（王友方）实变函数习题集（王友方）实变函数习题集（王友方）

本书是为正在学习数学分析(微积分)的读者、正在复习数学分析(微积分)准备报考研究生的读者以及从事这方面教学工作的年轻教师编写

实变函数论（那汤松）实变函数论（那汤松）经典 基础教程

SAS软件研究开发（北京）有限公司资深技术人员经验结晶，SAP大中国区商业创新首席架构师鲁百年强烈推荐。 实战性强，结合商业案例细致呈现SAS的优化建模方法，深入讲解SAS数据处理、统计分析及时间序列，涵盖引领大数据潮流的SAS高性能分析，以及智能分析平台、解决方案、平台的安全性与高可用性等重要领域。

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. •Get up and running with TensorFlow, rapidly and painlessly •Learn how to use TensorFlow to build deep learning models from the ground up •Train popular deep learning models for computer vision and NLP •Use extensive abstraction libraries to make development easier and faster •Learn how to scale TensorFlow, and use clusters to distribute model training •Deploy TensorFlow in a production setting Table of Contents Chapter 1 Introduction Chapter 2 Go with the Flow: Up and running with TensorFlow Chapter 3 Understanding TensorFlow Basics Chapter 4 Convolutional Neural Networks Chapter 5 Working with Text and Sequences + TensorBoard visualization Chapter 6 TF Abstractions and Simplification Chapter 7 Queues, Threads, and Reading Data Chapter 8 Distributed TensorFlow Chapter 9 Serving Models Chapter 10 Miscellaneous

The science of extracting useful information from large data sets or databases is known as data mining. It is a new discipline, lying at the intersection of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. All of these are concerned with certain aspects of data analysis, so they have much in common—but each also has its own distinct flavor, emphasizing particular problems and types of solution.

本书是“Hadoop技术内幕”系列的第3本书，前面两本分别对Common、HDFS和MapReduce进行了深入分析和讲解，赢得了极好的口碑，Hadoop领域几乎人手一册，本书则对YARN展开了深入的探讨，是首部关于YARN的专著。仍然由资深Hadoop技术专家董西成执笔，根据最新的Hadoop2.0版本撰写，权威社区ChinaHadoop鼎力推荐。 本书从应用角度系统讲解了YARN的基本库和组件用法、应用程序设计方法、YARN上流行的各种计算框架（MapReduce、Tez、Storm、Spark），以及多个类YARN的开源资源管理系统（Corona和Mesos）；从源代码角度深入分析YARN的设计理念与基本架构、各个组件的实现原理，以及各种计算框架的实现细节。

Data Mining Concepts and Techniques 3rd Edition(数据挖掘概念与技术第三版) 中文版 电子版

Data Mining Concepts and Techniques 3rd Edition(数据挖掘概念与技术第三版) 英文版 有目录

本书结合理论和实践，由浅入深，全方位介绍了Hadoop 这一高性能的海量数据处理和分析平台。全书5部分24 章，第Ⅰ部分介绍Hadoop 基础知识，第Ⅱ部分介绍MapReduce,第Ⅲ部分介绍Hadoop 的运维，第Ⅳ部分介绍Hadoop 相关开源项目，第Ⅴ部分提供了三个案例

本书结合理论和实践，由浅入深，全方位介绍了Hadoop 这一高性能的海量数据处理和分析平台。全书5部分24 章，第Ⅰ部分介绍Hadoop 基础知识，第Ⅱ部分介绍MapReduce,第Ⅲ部分介绍Hadoop 的运维，第Ⅳ部分介绍Hadoop 相关开源项目，第Ⅴ部分提供了三个案例

TensorFlow Machine Learning Cookbook by Nick McClure English | 14 Feb. 2017 | ISBN: 1786462168 | 370 Pages Key Features Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Book Description TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach yo u how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production About the Author Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog, http://fromdata.org/, or through his Twitter account, @nfmcclure. Table of Contents Getting Started with TensorFlow The TensorFlow Way Linear Regression Support Vector Machines Nearest Neighbor Methods Neural Networks Natural Language Processing Convolutional Neural Networks Recurrent Neural Networks Taking TensorFlow to Production More with TensorFlow

本书是一本*佳的TensorFlow入门指南。几位作者都来自研发一线，他们用自己的宝贵经验，结合众多高质量的代码，生动讲解TensorFlow的底层原理，并从实践角度介绍如何将两种常见模型——深度卷积网络、循环神经网络应用到图像理解和自然语言处理的典型任务中。此外，还介绍了在模型部署和编程中可用的诸多实用技巧。, 全书分为四部分，共9章。第一部分（第1~2章）讨论TensorFlow的设计模式以及选择TensorFlow作为深度学习库的优势和面临的挑战，并给出详细的安装指南。第二部分（第3~4章）深入介绍TensorFlow API的基础知识和机器学习基础。第三部分（第5~6章）探讨如何用TensorFlow实现高级深度模型，涉及卷积神经网络（或CNN）模型和循环神经网络（或RNN）模型。第四部分（第7~8章）探讨TensorFlow API中*新推出的特性，包括如何准备用于部署的模型、一些有用的编程模式等。第9章给出一些进一步了解TensorFlow的学习资源。

Python数据分析与挖掘实战,压缩包里面包含文档pdf与文档配套的代码 10余位数据挖掘领域资深专家和科研人员，10余年大数据挖掘咨询与实施经验结晶。从数据挖掘的应用出发，以电力、航空、医疗、互联网、生产制造以及公共服务等行业真实案例为主线，深入浅出介绍Python数据挖掘建模过程，实践性极强。, 本书共15章，分两个部分：基础篇、实战篇。基础篇介绍了数据挖掘的基本原理，实战篇介绍了一个个真实案例，通过对案例深入浅出的剖析，使读者在不知不觉中通过案例实践获得数据挖掘项目经验，同时快速领悟看似难懂的数据挖掘理论。读者在阅读过程中，应充分利用随书配套的案例建模数据，借助相关的数据挖掘建模工具，通过上机实验，以快速理解相关知识与理论。, 基础篇（第1～5章），第1章的主要内容是数据挖掘概述；第2章对本书所用到的数据挖掘建模工具Python语言进行了简明扼要的说明；第3章、第4章、第5章对数据挖掘的建模过程，包括数据探索、数据预处理及挖掘建模的常用算法与原理进行了介绍。, 实战篇（第6～15章），重点对数据挖掘技术在电力、航空、医疗、互联网、生产制造以及公共服务等行业的应用进行了分析。在案例结构组织上，本书是按照先介绍案例背景与挖掘目标，再 阐述分析方法与过程，最后完成模型构建的顺序进行的，在建模过程的关键环节，穿插程序实现代码。最后通过上机实践，加深读者对数据挖掘技术在案例应用中的理解。

吴恩达Coursera深度学习课程的笔记。在Coursera上吴恩达老师的DeepLearning.ai课程项目中，第一部分《神经网络和深度学习》第四周课程“深层神经网络”部分关键点的笔记。笔记并不包含全部小视频课程的记录，如需学习笔记中舍弃的内容请至 Coursera 或者 网易云课堂。同时在阅读以下笔记之前，强烈建议先学习吴恩达老师的视频课程

实变函数简明教程 邓东皋 高等教育出版社 答案 完整手写版

《21天实战Caffe》-真正清晰版本。本人花了大气力才生成的清晰版本！ 我在网上找了N个版本，网络上能下载到的号称清晰版都是白花花的，看5分钟眼睛就要吐了！（谴责这帮挂羊头，卖狗肉的！）本人这个版本是优化清晰版，看得眼睛很舒服。 希望大家早日学成！

台湾教授的经典一颗，让你深入了解深度学习的魅力，人工智能的大门，或许今天开启。

是南大的客座教授李航的著作，对于像学习各个机器学习算法理论知识十分有帮助，掌握这些，了解这些，学习如何推导公式，瞬速通过bat的机器学习面试。 《统计学习方法》是计算机及其应用领域的一门重要的学科。《统计学习方法》全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。除第1章概论和最后一章总结外，每章介绍一种方法。叙述从具体问题或实例入手，由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文献。

http://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/0521773628

PDF版 Andrew NG- Machine Learning 2014 课程配套代码，可以配合课程的课件使用

edit by ripley. university oxford x

机器学习与模式识别的经典之作（必看） 经典中的经典 y

《Mining of Massive Datasets》Jure Leskovec 数据挖掘经典参考书。国外上学时教授推荐！！ The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensiti ve hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

Kevin Murphy 关于机器学习的新书，偏贝叶斯，不过内容比较前沿。 Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package–PMTK (probabilistic modeling toolkit)–that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. 作者：Kevin p. Murphy 出版日期：August 24, 2012 页数：1104 ISBN：978-0262018029

《Hadoop实战（第2版）》能满足读者全面学习最新的Hadoop技术及其相关技术（Hive、HBase等）的需求，是一本系统且极具实践指导意义的Hadoop工具书和参考书。第1版上市后广受好评，被誉为学习Hadoop技术的经典著作之一。与第1版相比，第2版技术更新颖，所有技术都针对最新版进行了更新；内容更全面，几乎每一个章节都增加了新内容，而且增加了新的章节；实战性更强，案例更丰富；细节更完美，对第1版中存在的缺陷和不足进行了修正。 本书内容全面，对Hadoop整个技术体系进行了全面的讲解，不仅包括HDFS、MapReduce、YARN等核心内容，而且还包括Hive、HBase、Mahout、Pig、ZooKeeper、Avro、Chukwa等与Hadoop技术相关的重要内容。实战性强，不仅为各个知识点精心设计了大量经典的小案例，而且还包括Yahoo！等多个大公司的企业级案例，可操作系极强。 《Hadoop实战（第2版）》全书一共19章：第1~2章首先对Hadoop进行了全方位的宏观介绍，然后介绍了Hadoop在三大主流操作系统平台上的安装与配置方法；第3~6章分别详细讲解了MapReduce计算模型、MapReduce的工作机制、MapRedu ...展开

This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book's website includes solutions to the problems, data sets, software advice, and other ancillary material.

The Elements of Statistical LearningThe Elements of Statistical LearningThe Elements of Statistical Learning

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深度学习　2017 年 9 月 4 日　用压缩工具打开

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Python Data Science HandbookPython Data Science HandbookPython Data Science Handbook

NeuralNetworksandLearningMachines(NeuralNetworksandLearningMachines(

贝叶斯，数据分析，第三版，Andrew贝叶斯思想在大数据中表现良好