Posted on Jan 1, 2018


Python Machine Learning


Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world.

If you’ve read the first edition of this book, you’ll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.



《奇点临近》是一本有思维方法论启示的书;是一本站在历史的高度,正面思考科技力量的书;是一本充满想象与预言,但又不失科学论证的书。《奇点临近》提供了一个崭新的视角:21世纪既是数百年以来科技、创意的顶点,又是对人类终极命运真挚的愿景。 《奇点临近》特点:奇特与警示的结论,书中六个纪元的划分奇特又富于哲理;严谨与独特的论述方法,通过分析科学发展趋势,演绎并预测未来;警世之语与探讨性对话,通过智者的眼睛去审视自然、科学以及世界;章尾与未来的对话,是一种思想的博弈:通过设想中的未来去理解当今的技术发展和进化中的人类。



机器学习》是计算机科学与人工智能的重要分支领域。《机器学习》作为该领域的入门教材,在内容上尽可能涵盖机器学习基础知识的各方面。全书共16章,大致分为3个部分:第1 部分(第1~3 章)介绍机器学习的基础知识;第2部分(第4~10章)讨论一些经典而常用的机器学习方法(决策树、神经网络、支持向量机、贝叶斯分类器、集成学习、聚类、降维与度量学习);第3 部分(第11~16章)为进阶知识,内容涉及特征选择与稀疏学习、计算学习理论、半监督学习、概率图模型、规则学习以及强化学习等。 每章都附有习题并介绍了相关阅读材料,以便有兴趣的读者进一步钻研探索。 《机器学习》可作为高等院校计算机、自动化及相关专业的本科生或研究生教材,也可供对机器学习感兴趣的研究人员和工程技术人员阅读参考。

Deep Learning


An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.”Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” – Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep 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.

Pattern Recognition and Machine Learning


This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.




Complete Fingerstyle Guitar Method: Beginning Fingerstyle Guitar


If you’ve been inspired to begin playing fingerstyle guitar or are an experienced player wanting to enhance your knowledge, this book is the perfect place to start. Using standard music notation and TAB, the examples guide you through basic chord theory, modes, drop-D tuning, alternating bass and more.



斯蒂凡·库斯特卡,(STEFAN KOSTKA)斯蒂凡·库斯特卡在科罗拉多大学和得克萨斯大学获音乐学士和硕士学位,在威斯康辛大学获音乐理论哲学博士学位。1969年到1973年,他曾任伊斯特曼音乐学院教师,此后一直任教于奥斯汀得克萨斯大学。库斯特卡博士起初在伊斯特曼音乐学院和得克萨斯大学教授计算机音乐应用课程,近年来,更专注于无调性音乐理论和当代音乐风格与技术的研究,并写出了他的第二部专著《20世纪音乐的素材与技法》(Materials and Techniques of Twentieth-Century,Music),同时继续从事计算机应用于音乐理论的研究。库斯特卡博士在多个专业音乐组织中任职,曾任得克萨斯音乐理论学会主席。多萝茜·佩恩,(DOROTHY PAYNE)多萝茜·佩恩在伊斯特曼音乐学院获钢琴演奏学士和硕士学位,继而在该校获音乐理论哲学博士学位。从1994年开始,她任教于南卡罗来纳大学。佩恩曾担任过的管理职务包括南卡罗来纳大学教务长、亚利桑那大学音乐学院院长,以及康涅狄格大学音乐系主任。她还曾在奥斯汀得克萨斯大学、伊斯特曼音乐学院和太平洋路德大学担任过教职。除了作为一名活跃的演奏家,佩恩还经常在专业学会的会议上开设关于音乐理论教学的讲座和工作坊,并且是美国音乐院校协会访问评估员、任命委员会成员和执行委员会秘书。