# Coursera上博弈论相关课程（公开课）汇总推荐

1. 斯坦福大学的 博弈论（Game Theory）

This course is aimed at students, researchers, and practitioners who wish to understand more about strategic interactions. You must be comfortable with mathematical thinking and rigorous arguments. Relatively little specific math is required; but you should be familiar with basic probability theory (for example, you should know what a conditional probability is), and some very light calculus would be helpful.

Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Over four weeks of lectures, this advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered: social choice theory (i.e., collective decision making and voting systems), mechanism design, and auctions. In the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems. We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms. The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.

3. 东京大学的 博弈论入门课程（Welcome to Game Theory）

This course provides a brief introduction to game theory. Our main goal is to understand the basic ideas behind the key concepts in game theory, such as equilibrium, rationality, and cooperation. The course uses very little mathematics, and it is ideal for those who are looking for a conceptual introduction to game theory. Business competition, political campaigns, the struggle for existence by animals and plants, and so on, can all be regarded as a kind of “game,” in which individuals try to do their best against others. Game theory provides a general framework to describe and analyze how individuals behave in such “strategic” situations. This course focuses on the key concepts in game theory, and attempts to outline the informal basic ideas that are often hidden behind mathematical definitions. Game theory has been applied to a number of disciplines, including economics, political science, psychology, sociology, biology, and computer science. Therefore, a warm welcome is extended to audiences from all fields who are interested in what game theory is all about.

# Coursera上数据结构 & 算法课程（公开课）汇总推荐

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

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

“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）

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.

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).

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).

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).

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

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.

# Andrew Ng 深度学习公开课系列第五门课程序列模型开课

Andrew Ng 深度学习课程系列第五门课程序列模型（Sequence Models）在1月的尾巴终于开课 ，在跳票了几次之后，这门和NLP比较相关的深度学习课程终于开课了。这门课程属于Coursera上的深度学习专项系列 ，这个系列有5门课，目前终于完备，感兴趣的同学可以关注：Deep Learning Specialization

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization.

Andrew Ng 深度学习课程小记

Andrew Ng (吴恩达) 深度学习课程小结

# Andrew Ng 深度学习课程系列第四门课程卷积神经网络开课

Andrew Ng 深度学习课程系列第四门课程卷积神经网络（Convolutional Neural Networks）将于11月6日开课 ，不过课程资料已经放出，现在注册课程已经可以听课了 ，这门课程属于Coursera上的深度学习专项系列 ，这个系列有5门课，前三门已经开过好几轮，但是第4、第5门课程一直处于待定状态，新的一轮将于11月7号开始，感兴趣的同学可以关注：Deep Learning Specialization

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.

Andrew Ng 深度学习课程小记

Andrew Ng (吴恩达) 深度学习课程小结

# Andrew Ng (吴恩达) 深度学习课程小结

Andrew Ng (吴恩达) 深度学习课程从宣布到现在大概有一个月了，我也在第一时间加入了这个Coursera上的深度学习系列课程，并且在完成第一门课“Neural Networks and Deep Learning（神经网络与深度学习）”的同时写了关于这门课程的一个小结：Andrew Ng 深度学习课程小记。之后我断断续续的完成了第二门深度学习课程“Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization"和第三门深度学习课程“Structuring Machine Learning Projects”的相关视频学习和作业练习，也拿到了课程证书。平心而论，对于一个有经验的工程师来说，这门课程的难度并不高，如果有时间，完全可以在一个周内完成三门课程的相关学习工作。但是对于一个完全没有相关经验但是想入门深度学习的同学来说，可以预先补习一下Python机器学习的相关知识，如果时间允许，建议先修一下 CourseraPython系列课程Python for Everybody Specialization 和 Andrew Ng 本人的 机器学习课程

Dear Learners,

We hope that you are enjoying Structuring Machine Learning Projects and your experience in the Deep Learning Specialization so far!

As we are nearing the one month anniversary of the Deep Learning Specialization, we wanted to thank you for your feedback on the courses thus far, and communicate our timelines for when the next courses of the Specialization will be available.

We plan to begin the first session of Course 4, Convolutional Neural Networks, in early October, with Course 5, Sequence Models, following soon after. We hope these estimated course launch timelines will help you manage your subscription as appropriate.

If you’d like to maintain full access to current course materials on Coursera’s platform for Courses 1-3, you should keep your subscription active. Note that if you only would like to access your Jupyter Notebooks, you can save these locally. If you do not need to access these materials on platform, you can cancel your subscription and restart your subscription later, when the new courses are ready. All of your course progress in the Specialization will be saved, regardless of your decision.

Thank you for your patience as we work on creating a great learning experience for this Specialization. We look forward to sharing this content with you in the coming weeks!

Happy Learning,

Coursera

Variance的问题，神经网络中解决过拟合 (Overfitting) 的 Regularization 和 Dropout 方法，以及Gradient Check等：

“Understand what multi-task learning and transfer learning are
Recognize bias, variance and data-mismatch by looking at the performances of your algorithm on train/dev/test sets”

# Andrew Ng 深度学习课程小记

2011年秋季，Andrew Ng 推出了面向入门者的MOOC雏形课程机器学习: Machine Learning，随后在2012年4月，Andrew Ng 在Coursera上推出了改进版的Machine Learning(机器学习)公开课: Andrew Ng' Machine Learning: Master the Fundamentals，这也同时宣告了Coursera平台的诞生。当时我也是第一时间加入了这门课程，并为这门课程写了一些笔记：Coursera公开课笔记: 斯坦福大学机器学习 。同时也是受这股MOOC浪潮的驱使，建立了“课程图谱”，因此结识了不少公开课爱好者和MOOC大神。而在此之前，Andrew Ng 在斯坦福大学的授课视频“机器学习”也流传甚广，但是这门面向斯坦福大学学生的课程难道相对较高。直到2012年Coursera, Udacity等MOOC平台的建立，把课程视频，作业交互，编程练习有机结合在一起，才产生了更有生命力的MOOC课程。Andrew Ng 在为新课程深度学习写的宣传文章“deeplearning.ai: Announcing new Deep Learning courses on Coursera”里提到，这门机器学习课程自从开办以来，大约有180多万学生学习过，这是一个惊人的数字。

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

We will help you master Deep Learning, understand how to apply it, and build a career in AI.

1. Introduction to deep learning
2. Neural Networks Basics
3. Shallow neural networks
4. Deep Neural Networks

update: 这个周六（2017.08.20）完成了第四周课程和相关作业，也达到了拿证书的要求，不过需要上传相关证件验证ID，暂时还没有操作。下面是关于第四周课程的一点补充。