• Deep learning_ adaptive computation and machine learning-The MIT Press (2016)

    mit press 原版书,不是所谓draft版本,对书品质要求高者必爱.......

    0
    96
    16MB
    2017-11-01
    14
  • [solution:2009-9-8 最新版] Pattern recognition,machine Learning

    A complete solutions manual for the www exercises in PDF format (version: 8 September, 2009).

    0
    103
    883KB
    2009-11-17
    2
  • [Dirk van Dalen] Logic and structure [2008最新版]

     From the reviews:“A good textbook can improve a lecture course enormously, especially when the material of the lecture includes many technical details。 Van Dalen's book, the success and popularity of which may be suspected from this steady interest in it,contains a thorough introduction to elementary classical logic in a relaxed way, suitable for mathematics students who just want to get to know logic。The presentation always points out the connections of logic to other parts of mathematics。The reader immediately see the logic is ”just another branch of mathematics and not something more sacred。” 作者简介:   DIRK VAN DALEN,studied at the University of Amsterdam,where he obtained his PhD。He has taught since 1960 at Utrecht University,where he is full professor。He also taught at MIT and Oxford.His technical work is mostly in the area of intuitionistic mathematics and logic。He uses to call attention to the benefits and challenges of constructive methods。His current project is a biography of L.E.I。Brouwet and the editing of Brouwer’s corre—spondence。

    0
    124
    2.35MB
    2009-11-17
    10
  • 一分钟搞定AJAX(中文)

    Ajax 由 HTML、JavaScript™ 技术、DHTML 和 DOM 组成,这一杰出的方法可以将笨拙的 Web 界面转化成交互性的 Ajax 应用程序。本系列的作者是一位 Ajax 专家,他演示了这些技术如何协同工作 —— 从总体概述到细节的讨论 —— 使高效的 Web 开发成为现实。他还揭开了 Ajax 核心概念的神秘面纱,包括 XMLHttpRequest 对象。 五年前,如果不知道 XML,您就是一只无人重视的丑小鸭。十八个月前,Ruby 成了关注的中心,不知道 Ruby 的程序员只能坐冷板凳了。今天,如果想跟上最新的技术时尚,那您的目标就是 Ajax。 但是,Ajax 不仅仅 是一种时尚,它是一种构建网站的强大方法,而且不像学习一种全新的语言那样困难。

    0
    87
    1.93MB
    2009-07-03
    2
  • STATISTICAL LEARNING methods

    We describe methods for learning probability models—primarily Bayesian networks— in Sections 20.2 and 20.3. Section 20.4 looks at learning methods that store and recall specific instances. Section 20.5 covers neural network learning and Section 20.6 introduces kernel machines. Some of the material in this chapter is fairly mathematical (requiring a basic understanding of multivariate calculus), although the general lessons can be understood without plunging into the details. It may benefit the reader at this point to review the material in Chapters 13 and 14 and to peek at the mathematical background in Appendix A.

    0
    84
    1.03MB
    2009-07-03
    2
  • Linear Algebra Methods for Data Mining

    Linear Algebra Methods for Data Mining, Spring 2007, University of Helsinki

    3
    96
    4.02MB
    2009-07-03
    10
  • AN INTRODUCTION TO GRAPHICAL model

    author:Michael I. Jordan Graphical models are a marriage between graph theory and probability theory  They clarify the relationship between neural networks and related network-based models such as HMMs, MRFs, and Kalman lters  Indeed, they can be used to give a fully probabilistic interpretation to many neural network architectures

    5
    126
    545KB
    2009-07-03
    9
  • An introduction to graphical models

    Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering { uncertainty and complexity { and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity { a complex system is built by combining simpler parts. Probability theory provides the glue whereby the parts are combined, ensuring that the system as a whole is consistent, and providing ways to interface models to data. The graph theoretic side of graphical models provides both an intuitively appealing interface by which humans can model highly-interacting sets of variables as well as a data structure that lends itself naturally to the design of ecient general-purpose algorithms.

    5
    173
    186KB
    2009-07-03
    11
  • 正则表达式30分钟入门教程

    别被下面那些复杂的表达式吓倒,只要跟着我一步一步来,你会发现正则表达式其实并没有你 想像中的那么困难。当然,如果你看完了这篇教程之后,发现自己明白了很多,却又几乎什么都记不得,那也是很正常的——我认为,没接触过正则表达式的人在看 完这篇教程后,能把提到过的语法记住80%以上的可能性为零。这里只是让你明白基本的原理,以后你还需要多练习,多使用,才能熟练掌握正则表达式。

    0
    56
    79KB
    2008-12-22
    0
  • 免费 商业的未来 Chris Anderson

    本文是《长尾理论》作者Chris Anderson为他的下一本书“FREE”写的预告:经典的吉列模式正浪觞于各行各业,互联网技术成本的直线下降促使数字浪潮愈演愈烈,免费日益成为标准而非离经叛道,新商业模式正在崛起。这是一个动荡的变革年代。毫无疑问,我们的未来有赖于互联网;未来的商业,更离不开互联网这片奇异的免费乐土。我们正在参与历史。

    5
    105
    666KB
    2008-12-22
    10
关注 私信
上传资源赚积分or赚钱