标签归档:数据结构

一键收藏自然语言处理学习资源大礼包

虽然知道大多数同学都有资料收藏癖,还是给大家准备一份自然语言处理学习大礼包,其实是之前陆陆续续分享的NLP学习资源,包括自然语言处理、深度学习、机器学习、数学相关的经典课程、书籍和学习笔记,这些资料基本上都是公开渠道可以获得的,整理到一起,方便NLP爱好者收藏把玩。当然,学习的前提依然是”学自然语言处理,其实更应该学好英语“

获取方法很简单,关注AINLP公众号,后台回复关键词:ALL4NLP,一键打包收藏NLP学习资源:

这些自然语言处理相关资源列表如下,欢迎收藏:

相关的资源的过往文章大致介绍如下,不限于下述文章:

斯坦福大学自然语言处理经典入门课程-Dan Jurafsky 和 Chris Manning 教授授课

哥伦比亚大学经典自然语言处理公开课,数学之美中盛赞的柯林斯(Michael Collins)教授授课

认真推荐一份深度学习笔记:简约而不简单

Andrew Ng 老师新推的通俗人工智能课程以及其他相关资料

那些值得推荐和收藏的线性代数学习资源

Philipp Koehn大神的神经网络机器翻译学习资料:NMT Book

凸优化及无约束最优化相关资料

斯坦福大学深度学习自然语言处理课程CS224N 2019 全20个视频分享

自然语言处理经典书籍《Speech and Language Processing》第三版最新版下载(含第二版)

强化学习圣经:《强化学习导论》第二版(附PDF下载)

新书下载 | 面向机器学习的数学(Mathematics for Machine Learning)

Springer面向公众开放正版电子书籍,附65本数学、编程、数据挖掘、数据科学、数据分析、机器学习、深度学习、人工智能相关书籍链接及打包下载

最后,欢迎关注AINLP,回复"all4nlp"获取:

Springer面向公众开放正版电子书籍,附65本数学、编程、数据挖掘、数据科学、数据分析、机器学习、深度学习、人工智能相关书籍链接及打包下载

施普林格(Springer)是世界著名的科技期刊、图书出版公司,这次疫情期间面向公众免费开放了一批社科人文,自然科学等领域的正版电子书籍(据说是400多本),towardsdatascience 上有学者将其中65本机器学习和数据科学以及统计相关的免费教材下载链接整理了出来,我试了一下,无需注册,可以直接下载相关的PDF书籍,相当方便:Springer has released 65 Machine Learning and Data books for free(https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189)。

看了一下这份书单包括的书籍还是很棒的,包括数学类(多元微积分和几何、计算几何、偏微分、代数、线性代数、线性规划、概率和统计、统计学、统计学习、数学建模等)、编程类(数据结构与算法、Python编程、R语言、编程语言基础、面向对象分析和设计、数据库等)、数据挖掘、数据分析、数据科学、机器学习、人工智能、深度学习、计算机视觉,机器人等相关的电子书,甚至包括如何学习LaTex,远比想象的丰富很多。

这份清单的第一本书籍就是经典的“统计学习基础(ESL,The Elements of Statistical Learning)”, 进入书籍页面后,直接点击“Download PDF” 即可单独下载该书电子版:

Reddit上有网友提供了一个Google Drive的打包下载链接,包括其中64本书籍的PDF打包下载,可以直接下载:

https://www.reddit.com/r/opendirectories/comments/g91u12/google_drive_with_64_books_from_springer_about
https://drive.google.com/drive/folders/1rDJvZsz8EEuVVgZ43pwSvFRRKUo2TIIY

如果还是不方便,可以关注AINLP公众号,回复"sprg"获取百度网盘链接:

这份书籍清单和链接如下,我简单翻译了一下书名,供感兴趣的朋友参考:

The Elements of Statistical Learning(统计学习基础)

Trevor Hastie, Robert Tibshirani, Jerome Friedman

http://link.springer.com/openurl?genre=book&isbn=978-0-387-84858-7

Introductory Time Series with R(时间序列导论-基于R语言讲解)

Paul S.P. Cowpertwait, Andrew V. Metcalfe

http://link.springer.com/openurl?genre=book&isbn=978-0-387-88698-5

A Beginner’s Guide to R(R语言初学者指南)

Alain Zuur, Elena N. Ieno, Erik Meesters

http://link.springer.com/openurl?genre=book&isbn=978-0-387-93837-0

Introduction to Evolutionary Computing(进化计算导论)

A.E. Eiben, J.E. Smith

http://link.springer.com/openurl?genre=book&isbn=978-3-662-44874-8

Data Analysis(数据分析)

Siegmund Brandt

http://link.springer.com/openurl?genre=book&isbn=978-3-319-03762-2

Linear and Nonlinear Programming(线性和非线性规划)

David G. Luenberger, Yinyu Ye

http://link.springer.com/openurl?genre=book&isbn=978-3-319-18842-3

Introduction to Partial Differential Equations(偏微分方程简介)

David Borthwick

http://link.springer.com/openurl?genre=book&isbn=978-3-319-48936-0

Fundamentals of Robotic Mechanical Systems(机器人机械系统基础)

Jorge Angeles

http://link.springer.com/openurl?genre=book&isbn=978-3-319-01851-5

Data Structures and Algorithms with Python(Python数据结构和算法)

Kent D. Lee, Steve Hubbard

http://link.springer.com/openurl?genre=book&isbn=978-3-319-13072-9

Introduction to Partial Differential Equations(偏微分方程简介)

Peter J. Olver

http://link.springer.com/openurl?genre=book&isbn=978-3-319-02099-0

Methods of Mathematical Modelling(数学建模方法)

Thomas Witelski, Mark Bowen

http://link.springer.com/openurl?genre=book&isbn=978-3-319-23042-9

LaTeX in 24 Hours(24小时掌握LaTeX)

Dilip Datta

http://link.springer.com/openurl?genre=book&isbn=978-3-319-47831-9

Introduction to Statistics and Data Analysis(统计与数据分析导论)

Christian Heumann, Michael Schomaker, Shalabh

http://link.springer.com/openurl?genre=book&isbn=978-3-319-46162-5

Principles of Data Mining(数据挖掘原理)

Max Bramer

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-7307-6

Computer Vision(计算机视觉)

Richard Szeliski

http://link.springer.com/openurl?genre=book&isbn=978-1-84882-935-0

Data Mining(数据挖掘)

Charu C. Aggarwal

http://link.springer.com/openurl?genre=book&isbn=978-3-319-14142-8

Computational Geometry(计算几何)

Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars

http://link.springer.com/openurl?genre=book&isbn=978-3-540-77974-2

Robotics, Vision and Control(机器人,视觉与控制)

Peter Corke

http://link.springer.com/openurl?genre=book&isbn=978-3-319-54413-7

Statistical Analysis and Data Display(统计分析和数据展示)

Richard M. Heiberger, Burt Holland

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2122-5

Statistics and Data Analysis for Financial Engineering(金融工程统计与数据分析)

David Ruppert, David S. Matteson

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2614-5

Stochastic Processes and Calculus(随机过程与微积分)

Uwe Hassler

http://link.springer.com/openurl?genre=book&isbn=978-3-319-23428-1

Statistical Analysis of Clinical Data on a Pocket Calculator(袖珍计算器上的临床数据统计分析)

Ton J. Cleophas, Aeilko H. Zwinderman

http://link.springer.com/openurl?genre=book&isbn=978-94-007-1211-9

Clinical Data Analysis on a Pocket Calculator(袖珍计算器的临床数据分析)

Ton J. Cleophas, Aeilko H. Zwinderman

http://link.springer.com/openurl?genre=book&isbn=978-3-319-27104-0

The Data Science Design Manual(数据科学设计手册)

Steven S. Skiena

http://link.springer.com/openurl?genre=book&isbn=978-3-319-55444-0

An Introduction to Machine Learning(机器学习导论)

Miroslav Kubat

http://link.springer.com/openurl?genre=book&isbn=978-3-319-63913-0

Guide to Discrete Mathematics(离散数学指南)

Gerard O’Regan

http://link.springer.com/openurl?genre=book&isbn=978-3-319-44561-8

Introduction to Time Series and Forecasting(时间序列和预测简介)

Peter J. Brockwell, Richard A. Davis

http://link.springer.com/openurl?genre=book&isbn=978-3-319-29854-2

Multivariate Calculus and Geometry(多元微积分和几何)

Seán Dineen

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6419-7

Statistics and Analysis of Scientific Data(科学数据统计与分析)

Massimiliano Bonamente

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-6572-4

Modelling Computing Systems(建模计算系统)

Faron Moller, Georg Struth

http://link.springer.com/openurl?genre=book&isbn=978-1-84800-322-4

Search Methodologies(搜索方法论)

Edmund K. Burke, Graham Kendall

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6940-7

Linear Algebra Done Right(线性代数应该这样学)

Sheldon Axler

http://link.springer.com/openurl?genre=book&isbn=978-3-319-11080-6

Linear Algebra(线性代数)

Jörg Liesen, Volker Mehrmann

http://link.springer.com/openurl?genre=book&isbn=978-3-319-24346-7

Algebra(代数)

Serge Lang

http://link.springer.com/openurl?genre=book&isbn=978-1-4613-0041-0

Understanding Analysis(理解分析学)

Stephen Abbott

http://link.springer.com/openurl?genre=book&isbn=978-1-4939-2712-8

Linear Programming(线性规划)

Robert J Vanderbei

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-7630-6

Understanding Statistics Using R(通过R语言学习统计学)

Randall Schumacker, Sara Tomek

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6227-9

An Introduction to Statistical Learning(统计学习导论)

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-7138-7

Statistical Learning from a Regression Perspective(回归视角的统计学习)

Richard A. Berk

http://link.springer.com/openurl?genre=book&isbn=978-3-319-44048-4

Applied Partial Differential Equations(应用偏微分方程)

J. David Logan

http://link.springer.com/openurl?genre=book&isbn=978-3-319-12493-3

Robotics(机器人技术)

Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo

http://link.springer.com/openurl?genre=book&isbn=978-1-84628-642-1

Regression Modeling Strategies(回归建模策略)

Frank E. Harrell , Jr.

http://link.springer.com/openurl?genre=book&isbn=978-3-319-19425-7

A Modern Introduction to Probability and Statistics(概率统计的现代视角导论)

F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester

http://link.springer.com/openurl?genre=book&isbn=978-1-84628-168-6

The Python Workbook(Python手册)

Ben Stephenson

http://link.springer.com/openurl?genre=book&isbn=978-3-319-14240-1

Machine Learning in Medicine — a Complete Overview(医学中的机器学习-完整概述)

Ton J. Cleophas, Aeilko H. Zwinderman

http://link.springer.com/openurl?genre=book&isbn=978-3-319-15195-3

Object-Oriented Analysis, Design and Implementation(面向对象的分析,设计与实现)

Brahma Dathan, Sarnath Ramnath

http://link.springer.com/openurl?genre=book&isbn=978-3-319-24280-4

Introduction to Data Science(数据科学导论)

Laura Igual, Santi Seguí

http://link.springer.com/openurl?genre=book&isbn=978-3-319-50017-1

Applied Predictive Modeling(应用预测建模)

Max Kuhn, Kjell Johnson

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-6849-3

Python For ArcGIS(面向ArcGIS的Python指南)

Laura Tateosian

http://link.springer.com/openurl?genre=book&isbn=978-3-319-18398-5

Concise Guide to Databases(简明数据库指南)

Peter Lake, Paul Crowther

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-5601-7

Digital Image Processing(数字图像处理)

Wilhelm Burger, Mark J. Burge

http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6684-9

Bayesian Essentials with R(通过R学习贝叶斯基础)

Jean-Michel Marin, Christian P. Robert

http://link.springer.com/openurl?genre=book&isbn=978-1-4614-8687-9

Robotics, Vision and Control(机器人,视觉与控制)

Peter Corke

http://link.springer.com/openurl?genre=book&isbn=978-3-642-20144-8

Foundations of Programming Languages(编程语言基础)

Kent D. Lee

http://link.springer.com/openurl?genre=book&isbn=978-3-319-70790-7

Introduction to Artificial Intelligence(人工智能导论)

Wolfgang Ertel

http://link.springer.com/openurl?genre=book&isbn=978-3-319-58487-4

Introduction to Deep Learning(深度学习导论)

Sandro Skansi

http://link.springer.com/openurl?genre=book&isbn=978-3-319-73004-2

Linear Algebra and Analytic Geometry for Physical Sciences(物理科学的线性代数和解析几何)

Giovanni Landi, Alessandro Zampini

http://link.springer.com/openurl?genre=book&isbn=978-3-319-78361-1

Applied Linear Algebra(应用线性代数)

Peter J. Olver, Chehrzad Shakiban

http://link.springer.com/openurl?genre=book&isbn=978-3-319-91041-3

Neural Networks and Deep Learning(神经网络与深度学习)

Charu C. Aggarwal

http://link.springer.com/openurl?genre=book&isbn=978-3-319-94463-0

Data Science and Predictive Analytics(数据科学与预测分析)

Ivo D. Dinov

http://link.springer.com/openurl?genre=book&isbn=978-3-319-72347-1

Analysis for Computer Scientists(面向计算机科学家的分析学)

Michael Oberguggenberger, Alexander Ostermann

http://link.springer.com/openurl?genre=book&isbn=978-3-319-91155-7

Excel Data Analysis(Excel数据分析)

Hector Guerrero

http://link.springer.com/openurl?genre=book&isbn=978-3-030-01279-3

A Beginners Guide to Python 3 Programming(Python 3编程入门指南)

John Hunt

http://link.springer.com/openurl?genre=book&isbn=978-3-030-20290-3

Advanced Guide to Python 3 Programming(Python 3编程高级指南)

John Hunt

http://link.springer.com/openurl?genre=book&isbn=978-3-030-25943-3

感兴趣的同学可以关注下方公众号,回复"sprg"获取打包下载网盘链接:

Coursera上数据结构 & 算法课程(公开课)汇总推荐

数据结构和算法是基本功,Coursera上有很多数据结构和算法方面的经典课程,这里做个总结。

1. 普林斯顿大学 Sedgewick 教授的 算法1: Algorithms, Part I

这门算法课程已经开过很多轮,好评如潮 ,应该算得上是 Coursera 上的明星算法课程了,感兴趣的同学可以参考课程图谱上的旧版 课程评论,强烈推荐:

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

2. 普林斯顿大学 Sedgewick 教授的 算法2: Algorithms, Part II

系列课程,依然强烈推荐,感兴趣的同学可以参考早期课程的评价:http://coursegraph.com/coursera_algs4partII

“Part II较Part I在部分Programming Assignments上增加了timing和memory的难度,API100%不再意味着全部100%,这正是这门课程的精华之处:不是灌输算法知识,而是通过实际操作的过程让学员深入理解数据结构和算法调优在经济上的意义。个人很喜欢论坛上大家在Performance Thread里贴出自己的report然后交流优化心得的过程,很有圆桌会议的架势。这门课的教授Robert Sedgewick师出名门,是Knuth在斯坦福的博士。老爷子年岁已近70,一直活跃在论坛上解答和讨论问题,敬业程度让人赞叹。”

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

3. 斯坦福大学的 算法专项课程(Algorithms Specialization)

斯坦福大学的算法专项课程系列(Algorithms Specialization),这个系列包含4门子课程,涵盖基础的算法主题和高级算法主题,此前评价非常高,五颗星推荐,感兴趣的同学可以关注: Learn To Think Like A Computer Scientist-Master the fundamentals of the design and analysis of algorithms.

Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists. About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.

可参考老版课程评论:Algorithms: Design and Analysis, Part 1Algorithms: Design and Analysis, Part 2

3.1 Divide and Conquer, Sorting and Searching, and Randomized Algorithms

The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

3.2 Graph Search, Shortest Paths, and Data Structures

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

3.3 Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

3.4 Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

4. 北京大学的 程序设计与算法专项课程系列

据说是国内学生选择最多的中文程序设计课程,这个系列包含7门子课程,分别是计算导论与C语言基础, C程序设计进阶 ,C++程序设计, 算法基础, 数据结构基础, 高级数据结构与算法, 程序开发项目实践,最后一个项目实践课程联合腾讯公司设计一个实际的应用问题:搜索引擎设计。感兴趣的同学可以关注:

本专项课程旨在系统培养你的程序设计与编写能力。系列课程从计算机的基础知识讲起,无论你来自任何学科和行业背景,都能快速理解;同时我们又系统性地介绍了C程序设计,C++程序设计,算法基础,数据结构与算法相关的内容,各门课之间联系紧密,循序渐进,能够帮你奠定坚实的程序开发基础;课程全部配套在线编程测试,将有效地训练和提升你编写程序的实际动手能力。并通过结业实践项目为你提供应用程序设计解决复杂现实问题的锻炼,从而积累实际开发的经验。因此,我们希望本专项课程能够帮助你完成从仅了解基本的计算机知识到能够利用高质量的程序解决实际问题的转变。

5. 加州大学圣地亚哥分校的 数据结构与算法专项课程系列(Data Structures and Algorithms Specialization)

这个系列包含5门子课程和1门毕业项目课程,包括算法工具箱,数据结构 ,图算法,字符串算法 ,高级算法与算法复杂度,算法毕业项目 等,感兴趣的同学可以关注: Master Algorithmic Programming Techniques-Learn algorithms through programming and advance your software engineering or data science career

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

注:本文首发“课程图谱博客”:http://blog.coursegraph.com ,同步发布到这里, 本文链接地址:http://blog.coursegraph.com/coursera上数据结构-算法课程-算法公开课-汇总推荐 http://blog.coursegraph.com/?p=736