# 那些值得推荐和收藏的线性代数学习资源

Introduction to Linear Algebra (3rd Ed.) by Gilbert Strang.

https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
(注：这里我修正了一下链接，原文链接已经没有了）

http://open.163.com/special/opencourse/daishu.html

“线性代数”，同微积分一样，是高等数学中两大入门课程之一，不仅是一门非常好的数学课程，也是一门非常好的工具学科，在很多领域都有广泛的用途。它的研 究对象是向量，向量空间（或称线性空间），线性变换和有限维的线性方程组。本课程讲述了矩阵理论及线性代数的基本知识，侧重于那些与其他学科相关的内容， 包括方程组、向量空间、行列式、特征值、相似矩阵及正定矩阵。

2. 3Blue1Brown: Essence of linear algebra（线性代数的本质）

https://www.bilibili.com/video/av5987715/

《英文版的线性代数电子书：Immersive Linear Algebra》该书是今天 Hacker News 首页头条。号称是全球第一个全交互式图形的线代电子书。

http://coursegraph.com/coursera-matrix-algebra-engineers

This course is all about matrices, and concisely covers the linear algebra that an engineer should know. We define matrices and how to add and multiply them, and introduce some special types of matrices. We describe the Gaussian elimination algorithm used to solve systems of linear equations and the corresponding LU decomposition of a matrix. We explain the concept of vector spaces and define the main vocabulary of linear algebra. We develop the theory of determinants and use it to solve the eigenvalue problem. After each video, there are problems to solve and I have tried to choose problems that exemplify the main idea of the lecture. I try to give enough problems for students to solidify their understanding of the material, but not so many that students feel overwhelmed and drop out. I do encourage students to attempt the given problems, but if they get stuck, full solutions can be found in the lecture notes for the course. The mathematics in this matrix algebra course is presented at the level of an advanced high school student, but typically students would take this course after completing a university-level single variable calculus course.

http://www.math.ust.hk/~machas/matrix-algebra-for-engineers.pdf

http://coursegraph.com/coursera-linear-algebra-machine-learning

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

http://open.163.com/special/Khan/linearalgebra.html

# 自然语言处理对于IBM超级计算机沃森（Watson）意味着什么？

这几天估计很多人都在关注IBM超级计算机沃森（Watson）在美国最受欢迎的智力竞猜电视节目《危险边缘》中的表现，而在经历了三天的比赛后，沃森终于击败了该节目历史上两位最成功的选手肯-詹宁斯和布拉德-鲁特，成为《危险边缘》节目新的王者：IBM超级计算机在智力问答比赛中击败人类。与这场“人机大战”相关的信息中，几乎都会提及“自然语言处理”，毕竟沃森首先需要突破的就是能“理解人类的语言”，这当然是“自然语言处理”的份内之事。而在我看来，IBM沃森看起来更像一个超级的“自动问答”系统，当然，沃森背后凝聚的岂止是“自动问答”，它是一个包含了海量数据处理，机器学习，信息提取，文本分析，知识推理，自动问答等众多技术的的超级“人工智能”结合体。
下午在看到这个消息时，我有一个很强烈的念头，要写一篇“IBM超级计算机沃森（Watson）背后的自然语言处理技术”，当然，即使写出来，也只能是一个旁观者的角度，需要一定的素材去挖掘。不过刚好有一篇相关的新闻给了我一些启示“IBM宣布八所大学参与沃森计算机系统的开发”：

“我们很高兴与这些在其各自领域表现优异的大学和专家们进行合作，他们可帮助推动作为 IBM沃森系统的支柱的问答技术的进步”，IBM沃森项目组负责人 David Ferrucci 博士表示，“《危险边缘》Jeopardy! 挑战的成功将突破与计算技术的处理和理解人类语言的能力有关的障碍，并将对科学、技术和商业带来深远的影响。”

这篇文章下面对于每所大学的贡献都给与了简要的描述，通读下来，会发现“自然语言处理”技术在其中扮演着重要的角色。特别是麻省理工学院：

这里面提到的自然语言问答系统START很有意思，有兴趣的读者可以试着问两个问题看看：”What is start" and "How old are you"! 继续阅读

# MIT自然语言处理第五讲：最大熵和对数线性模型（第三部分）

Natural Language Processing: Maximum Entropy and Log-linear Models 继续阅读

# MIT自然语言处理第五讲：最大熵和对数线性模型（第二部分）

Natural Language Processing: Maximum Entropy and Log-linear Models 继续阅读

# MIT自然语言处理第五讲：最大熵和对数线性模型（第一部分）

Natural Language Processing: Maximum Entropy and Log-linear Models 继续阅读