sstoney的博客

A Beginners Guide to Python 3 Programming Second Edition, 2023 by John Hunt, source code from github

A Beginners Guide to Python 3 Programming Second Edition, 2023, John Hunt

Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely The MIT Press, 2021 I KNOWLEDGE GRAPH FUNDAMENTALS 1 Introduction to Knowledge Graphs 1.1 Graphs 1.2 Representing Knowledge as Graphs 1.3 Examples of Knowledge Graphs 1.3.1 Example 1: Scientific Publications and Academics 1.3.2 Example 2: ECommerce, Products, and Companies 1.3.3 Example 3: Social Networks 1.3.4 Example 4: Geopolitical Events 1.4 How to Read This Text 1.5 Concluding Notes 1.6 Software and Resources 1.7 Bibliographic Notes

Professor DingPeng's UC Berkeley lecture

Daniel A. Spielman, Yale University, Incomplete Draft, dated December 4, 2019

Keith D. Cooper and Linda Torcz from Rice University

量子信息理论著作，John Watrous, Institute for Quantum Computing, University of Waterloo

Abstract Dynamic Programming THIRD EDITION Dimitri P. Bertsekas

论文写作的文献管理神器，可收集整理各类文献资源，为word、latex等应用，2016新版

为latex管理参考文献的软件，2016新版

Probability: Theory and Examples Rick Durrett Edition 4.1, April 21, 2013 Typos corrected, three new sections in Chapter 8.

Ioannis Karatzas, Steven E. Shreve Brownian Motion and Stochastic Calculus, 2nd Edition 1996

Stochastic Differential Equations, An Introduction with Applications, Fifth Edition, Corrected Printing, Springer-Verlag Heidelberg New York 2000, by Bernt Øksendal. This is a very clear version.

APPLIED STOCHASTIC PROCESSES by G.A. Pavliotis, Department of Mathematics, Imperial College London, January 18, 2009.

Welcome! This is the documentation for Python 2.7.11, last updated Mar 31, 2016.

The 2015 book written by Professor Pedro Domingoes, to explain How the Quest for the ultimate learning machine will remake our world. Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. Machine learning is the automation of discovery—the scientific method on steroids—that enables intelligent robots and computers to program themselves. No field of science today is more important yet more shrouded in mystery. Pedro Domingos, one of the field’s leading lights, lifts the veil for the first time to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He charts a course through machine learning’s five major schools of thought, showing how they turn ideas from neuroscience, evolution, psychology, physics, and statistics into algorithms ready to serve you. Step by step, he assembles a blueprint for the future universal learner—the Master Algorithm—and discusses what it means for you, and for the future of business, science, and society. If data-ism is today’s rising philosophy, this book will be its bible. The quest for universal learning is one of the most significant, fascinating, and revolutionary intellectual developments of all time. A groundbreaking book, The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen, but how to be at its forefront.

2014新版的JabRef2.10，功能比较强，需要java支持，适合latex生成bib

经典书籍Pattern Recognition and Machine learning的最完整勘误表，原书的错误影响阅读，可以参照勘误表

Combinatorial stochastic processes, by Prof. James Pitman at Statistics Department of UC Berkeley. It's the mathematical foundation of Dirichlet process, Chinese restaurant process and many other nonparametric methods.

MapReduce is a computing paradigm for processing data that resides on hundreds of computers, which has been popularized recently by Google, Hadoop, and many others. The paradigm is extraordinarily powerful, but it does not provide a general solution to what many are calling “big data,” so while it works particularly well on some problems, some are more challenging. This book will teach you what problems are amenable to the MapReduce paradigm, as well as how to use it effectively.

2013新版的JabRef2.9.2，功能比较强，需要java支持，适合latex生成bib

MIT教授写的并行与分布算法书，97年版的，需要一定数学基础。

Matt Carter, 2007 Edinburgh University Press Ltd 22 George Square, Edinburgh This is a book about minds. It is also about computers. Centrally, we will be interested in examining the relation between minds and computers.

对Petri网原理进行讲解，pdg格式，2002年的版本