Speech and Language Processing 2nd 文字版非扫描非草稿
没有水印,文字排版,非扫描,非草稿。自然语言处理领域神级教材,某些章节第三版没有,必须得看第二版
cs224n 2018版作业
cs224n 2018版作业包括解析,附带作业lecture和解答。
curl-7.46.0-win64
官方最新版本 安装时请使用默认设置,安装完成后可以打开一个命令行窗口,输入 curl,如果提示 curl: try 'curl --help' or 'curl --manual' for more information 就表示安装成功了。
统计自然语言处理(第2版)
《统计自然语言处理(第2版)》全面介绍了统计自然语言处理的基本概念、理论方法和最新研究进展,内容包括形式语言与自动机及其在自然语言处理中的应用、语言模型、隐马尔可夫模型、语料库技术、汉语自动分词与词性标注、句法分析、词义消歧、篇章分析、统计机器翻译、语音翻译、文本分类、信息检索与问答系统、自动文摘和信息抽取、口语信息处理与人机对话系统等,既有对基础知识和理论模型的介绍,也有对相关问题的研究背景、实现方法和技术现状的详细阐述。
采用Zbar第三方库,实现二维码定位,并识别内容,解决了中文乱码
采用Zbar第三方库,实现二维码定位,并识别内容,解决了Zbar中文乱码问题. 利用梯度信息进行条形码的定位,QR-code利用矩阵点信息定位
COLING 2018 Tutorial 4:Deep Bayesian Learning and Understanding
COLING 2018 Tutorial 4:Deep Bayesian Learning and Understanding
TensorFlow for Deep Learning
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.
After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects.
What You Will Learn
Work with data analysis techniques such as classification, clustering, regression, and forecasting
Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
Examine the different big data frameworks, including Hadoop and Spark
Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP
Who This Book Is For
Data scientists and software developers interested in the field of data analytics.
神奇的匹配:正则表达式求精之旅
本书从正则表达式的基本概念、基本语法入手,着重于数字验证、字符串验证、数字和字符串混合验证及HTML处理等各个方面的应用。并基于目前流行的程序语言和应用环境(如C#、ASP.NET、JSP、JavaScript或PHP),全面介绍了创建正则表达式的方法,以及正则表达式在Web环境中的各种应用。
最优化导论 第4版 清晰
作者: Edwin K. P. Chong / Stanislaw H. Zak
出版社: 电子工业出版社
原作名: An Introduction to Optimization,Foulth Edition
译者: 孙志强 / 白圣建 / 郑永斌 / 刘伟
出版年: 2015-10
页数: 415
定价: 89.00
装帧: 平装
丛书: 经典译丛·人工智能与智能系统
NLTK基础教程-用NLTK和Python库构建机器学习应用
NLTK 库是当前自然语言处理(NLP)领域zui为流行、使用zui为广泛的库之一, 同时Python语言经过一段时间的发展也已逐渐成为主流的编程语言之一。 本书主要介绍如何通过NLTK库与一些Python库的结合从而实现复杂的NLP任务和机器学习应用。全书共分为10章。第 1章对NLP进行了简单介绍。第 2章、第3章和第4章主要介绍一些通用的预处理技术、专属于NLP领域的预处理技术以及命名实体识别技术等。第5章之后的内容侧重于介绍如何构建一些NLP应用,涉及文本分类、数据科学和数据处理、社交媒体挖掘和大规模文本挖掘等方面。 本书适合 NLP 和机器学习领域的爱好者、对文本处理感兴趣的读者、想要快速学习NLTK的zishenPython程序员以及机器学习领域的研究人员阅读。 NLTK 库是当前自然语言处理(NLP)领域zui为流行、使用zui为广泛的库之一, 同时Python语言经过一段时间的发展也已逐渐成为主流的编程语言之一。
本书主要介绍如何通过NLTK库与一些Python库的结合从而实现复杂的NLP任务和机器学习应用。全书共分为10章。第1章对NLP进行了简单介绍。第2章、第3章和第4章主要介绍一些通用的预处理技术、专属于NLP领域的预处理技术以及命名实体识别技术等。第5章之后的内容侧重于介绍如何构建一些NLP应用,涉及文本分类、数据科学和数据处理、社交媒体挖掘和大规模文本挖掘等方面。
本书适合 NLP 和机器学习领域的爱好者、对文本处理感兴趣的读者、想要快速学习NLTK的zishenPython程序员以及机器学习领域的研究人员阅读。
Python核心编程 第3版 高清晰完整版pdf
本书讲解了如下内容: 学习专业的Python风格、实践以及好的编程习惯; 使用TCP、UDP、XML-RPC来开发客户端和服务器,并供高级的库(比如SocketServer和Twisted)使用使用Tkinter和其他可用的工具来开发GUI应用; 通过使用C/C++编写扩展,或者使用多线程来编写I/O密集型代码,提升应用的性能; 探究QSL和关系数据库、ORM,甚至是MongoDB这样的非关系型(NonSQL)数据库; 学习Web编程的基础知识,包括Web客户端和服务器,以及CGI和WSGI; 使用正则表达式和强大的文本处理工具,来创建和解析CSV、JSON和XML数据; 使用COM客户端编程与常见的Microsoft Office软件(比如Excel、PowerPoint、Outlook)进行交互; 使用Django框架深入了解Web开发,以及使用Google App Engine深入了解云计算; 使用Jyhon探索Java编程,以及在JVM上运行Python代码的方式; 连接Web服务,比如连接Yahoo!Fiance获取股票行情,或者连接Yahoo!Mail、Gmail以及其他邮件服务器来下载或发送邮件; 通过学习如何连接Twitter和Google+网络来拥抱社交媒体的热潮。
内容简介
《Python核心编程(第3版)》是经典畅销图书《Python核心编程(第二版)》的全新升级版本,总共分为3部分。第1部分为讲解了Python的一些通用应用,包括正则表达式、网络编程、Internet客户端编程、多线程编程、GUI编程、数据库编程、Microsoft Office编程、扩展Python等内容。第2部分讲解了与Web开发相关的主题,包括Web客户端和服务器、CGI和WSGI相关的Web编程、Django Web框架、云计算、高级Web服务。第3部分则为一个补充/实验章节,包括文本处理以及一些其他内容。 《Python核心编程(第3版)》适合具有一定经验的Python开发人员阅读。
京东购买链接:https://item.jd.com/11936238.html
PYTHON自然语言处理中文翻译 NLTK
PYTHON自然语言处理中文翻译 NLTK
CSS揭秘完整版
本书是一本注重实践的教程,作者为我们揭示了 47 个鲜为人知的 CSS 技巧,主要内容包括背景与边框、形状、 视觉效果、字体排印、用户体验、结构与布局、过渡与动画等。本书将带领读者循序渐进地探寻更优雅的解决方案,攻克每天都会遇到的各种网页样式难题。
本书的读者对象为前端工程师、网页开发人员。
机器学习实战(高清版)
《机器学习实战》主要介绍机器学习基础,以及如何利用算法进行分类,并逐步介绍了多种经典的监督学习算法,如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树(CART)算法等。第三部分则重点介绍无监督学习及其一些主要算法:k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具。
《
机器学习实战》通过精心编排的实例,切入日常工作任务,摒弃学术化语言,利用高效的可复用Python代码来阐释如何处理统计数据,进行数据分析及可视化。通过各种实例,读者可从中学会机器学习的核心算法,并能将其运用于一些策略性任务中,如分类、预测、推荐。另外,还可用它们来实现一些更高级的功能,如汇总和简化等。
Categorical Data Analysis by Example
Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code
This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject.
Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book:
• Uses an example to illustrate each new topic in categorical data
• Provides a clear explanation of an important subject
• Is understandable to most readers with minimal statistical and mathematical backgrounds
• Contains examples that are accompanied by R code and resulting output
• Includes starred sections that provide more background details for interested readers
Categorical Data Analysis by Example is a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields.
GRAHAM J. G. UPTON is formerly Professor of Applied Statistics, Department of Mathematical Sciences, University of Essex. Dr. Upton is author of The Analysis of Cross-tabulated Data (1978) and joint author of Spatial Data Analysis by Example (2 volumes, 1995), both published by Wiley. He is the lead author of The Oxford Dictionary of Statistics (OUP, 2014). His books have been translated into Japanese, Russian, and Welsh.
Enhancing Adobe Acrobat DC Forms with JavaScript
Take your PDF forms to the next level. In this book, you’ll learn various ways to further improve your PDF Forms using simple JavaScript coding. You’ll also discover how a few lines of code can speed up your workflow when working with multiple PDFs in Action Wizard.
Enhancing Adobe Acrobat DC Forms with JavaScript covers up-to-date, real working examples that you can easily download, practice with, and edit to suit your own projects. Using screenshots from Adobe Acrobat DC, users or previous versions will also be able to utilize these techniques. This book also shows workarounds and solutions to various form issues you might encounter. Feel empowered by it and improve your PDF documents!
JavaScript has been a part of Adobe Acrobat for many versions. However, few people use its features and focus mainly on using the basic form properties, never delving deeper into Acrobat’s full capabilities. While information on the web can be helpful, if you don’t know enough about how to use JavaScript in Acrobat you will be left with poor results. JavaScript can be difficult to learn, but it does not need to be scary. This book explains it in simple steps at a beginner to intermediate level so you can take full advantage of Acrobat’s capabilities in your own projects.
What You’ll Learn
• Create calculations, rating forms, and QR code stamps using the form elements
• Explore simplified field notation and basic JavaScript for Acrobat
• Work with buttons that can be used for navigation
• Utilize complex forms that include drop down and list boxes in combination with other form fields
• Work with Action Wizard and JavaScript
• Improve form navigation and printing of forms
• Add various types of alerts and custom validations to improve client-entered-data
Who This Book Is For
Anyone who needs to create forms for clients or websites: students, lawyers, accountants, and human resource personnel
Deep Learning with Python
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.
What You Will Learn
Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe
Gain the fundamentals of deep learning with mathematical prerequisites
Discover the practical considerations of large scale experiments
Take deep learning models to production
Who This Book Is For
Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
Python机器学习实践指南
机器学习是近年来渐趋热门的一个领域,同时Python 语言经过一段时间的发展也已逐渐成为主流的编程语言之一。本书结合了机器学习和Python 语言两个热门的领域,通过利用两种核心的机器学习算法来将Python 语言在数据分析方面的优势发挥到极致。
全书共有10 章。第1 章讲解了Python 机器学习的生态系统,剩余9 章介绍了众多与机器学习相关的算法,包括各类分类算法、数据可视化技术、推荐引擎等,主要包括机器学习在公寓、机票、IPO 市场、新闻源、内容推广、股票市场、图像、聊天机器人和推荐引擎等方面的应用。
本书适合Python 程序员、数据分析人员、对算法感兴趣的读者、机器学习领域的从业人员及科研人员阅读。
Thoughtful Machine Learning with Python
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:
Reference real-world examples to test each algorithm through engaging, hands-on exercises
Apply test-driven development (TDD) to write and run tests before you start coding
Explore techniques for improving your machine-learning models with data extraction and feature development
Watch out for the risks of machine learning, such as underfitting or overfitting data
Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms