Part I Preliminaries 1 Introduction 1.1 Prologue: Rationalist and Empiricist Approaches 1.2 Scientific Content 1.2.1 Questions that linguistics should answer 1.2.2 Non 1.2.3 Language and cognition as probabilistic phenomena 1.3 The Ambiguity of Language: Why NLP is Difficult 1.4 Dirty Hands 1.4.1 Lexical resources 1.4.2 Word counts 1.4.3 Zipf’s laws 1.4.4 Collocations 1.4.5 Concordances 1.5 Further Reading 1.6 Exercises 2 Mathematical Foundations 2.1 Elementary Probability Theory 2.1.1 Probability spaces 2.1.2

Ensemble Methods Foundations and Algorithms的读书笔记，介绍boosting,bagging，adaboost等理论

差分隐私基础 Dwork

Artificial Intelligence foundations of computational agents AI经典、基础书籍

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http://www.amazon.com/Foundations-Python-Network-Programming-Brandon/dp/1430258543 这本书是2014年底出版的，基于最新的 python3.4 版本。 配书源码链接 https://github.com/brandon-rhodes/fopnp 目录 Chapter 1: Introduction to Client-Server Networking Chapter 2: UDP Chapter 3: TCP Chapter 4: Socket Names and DNS Chapter 5: Network Data and Network Errors Chapter 6: TLS/SSL Chapter 7: Server Architecture Chapter 8: Caches and Message Queues Chapter 9: HTTP Clients Chapter 10: HTTP Servers Chapter 11: The World Wide Web Chapter 12: Building and Parsing E-Mail Chapter 13: SMTP Chapter 14: POP Chapter 15: IMAP Chapter 16: Telnet and SSH Chapter 17: FTP Chapter 18: RPC Instead, this book focuses on network programming, using Python 3 for every example script and snippet of code at the Python prompt. These examples are intended to build a comprehensive picture of how network clients, network servers, and network tools can best be constructed from the tools provided by the language. Readers can study the transition from Python 2 to Python 3 by comparing the scripts used in each chapter of the second edition of this book with the listings here in the third edition—both of which are available at https://github.com/brandon-rhodes/fopnp/tree/m/ thanks to the excellent Apress policy of making source code available online. The goal in each of the following chapters is simply to show you how Python 3 can best be used to solve modern network programming problems.

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Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s resea

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