This is the 2nd edition of "Speech and Language Processing, 2000" (http://www.douban.com/subject/1810715/). An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, usingthe examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. A useful reference for professionals in any of the areas of speech and language processing.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

人类与信息遭遇的历史由来已久。詹姆斯•格雷克笔下的这段历史出人意料地从非洲的鼓语讲起（第1章）。非洲土著部落在尚未直接跨越到移动电话之前，曾用鼓声来传递讯息，但他们是如何做到的呢？后续章节进而讲述了这段历史上几个影响深远的关键事件，包括文字的发明（第2章）、罗伯特•考德里的第一本英语词典（第3章）、查尔斯•巴贝奇的差分机与爱达•拜伦的程序（第4章）、沙普兄弟的信号塔与摩尔斯电码（第5章）。 但人类开始自觉地理解和利用信息始于克劳德•香农在1948年创立的信息论（第6、7章）。香农的信息论不仅推动了信息技术的发展，也引发了许多学科的信息转向（第8章），改变了人们对于诸如麦克斯韦妖（第9章）、生命的编码（第10章）、模因（第11章）、随机性（第12章）、量子信息论（第13章）等的理解。部分科学家甚至认为，构成世界的基础不是物质，不是能量，而是信息。正如物理学家约翰•惠勒所说，“万物源自比特”。 现如今，信息如洪流般淹没了我们，使我们深陷信息焦虑、信息过载、信息疲劳的困扰。但回顾历史，这并不是件新鲜事，人们也总是能想出应对手段。维基百科（第14章）、Google（第15章）便是我们的应对之一。无论对于信息的未来持何态度，有一点是确定无疑的，即我们人类是信息的造物。 作为《混沌》、《费曼传》、《越来越快》、《牛顿传》等畅销书的作者，格雷克不仅在书中细致还原了历史细节，通俗解释了各种理论，还生动刻画了几位不为大众所知的人物：可编程计算机先驱、超越时代的查尔斯•巴贝奇，第一位程序员、诗人拜伦之女爱达•拜伦，计算机科学之父、天妒英才的阿兰•图灵，以及全书的主人公、信息论之父克劳德•香农。

“解密”并非专属于军队、特工和科学家，因为我们就生活在各种“密码”编织的世界中，人人都是“解密专家”。“解密”推动着世界发展和人类进步。在当今科技高速发展的社会中，人类与“密码”的关系越来越紧密，“密码”将在未来扮演更重要的角色。 本书新奇而全面地展现了从自然界到古文明，从人类起源到数字时代，从工业革命到未来科技所产生的形式各异的密码、符号、文字、图形等，以及它们如何为人类所用，并推动人类生存和发展。 全书的122个“密码”主题，将大千世界的“密码文化”融为一体，涵盖语文、数学、物理、计算机科学、地理、军事、金融、生物、艺术、商业等20多个学科和领域。本书是一本兼具权威性、知识性、趣味性和艺术性的科普读物。

改编自Vamei博客的《Python快速教程》。本书以Python为样本，不仅介绍了编程的基本概念，还着重讲解编程语言的主流范式：面向过程、面向对象、面向函数。读者不仅可以轻松学会Python，以后再学习其他编程语言时也会更加容易。