Chris Albon

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Learn how to write idiomatic, effective Python code by leveraging its best features. Python's simplicity quickly lets you become productive with it, but this often means you aren't using everything the language has to offer. By taking you through Python's key language features and libraries, this practical book shows you how to make your code shorter, faster, and more readable all at the same time--what experts consider "Pythonic." Many programmers who learn Python basics fall into the trap of reinventing the wheel because of past experience in other languages, and try to bend the language to patterns that don't really apply to it. Author Luciano Ramalho, a Python Software Foundation member and Python programmer for 15 years, helps you drop your accent from another language so you can code Python fluently.Learn practical applications of generators for database processingRethink some design patterns in a Python contextExamine attribute descriptors and when to use them: the key to ORMsExplore Pythonic objects: protocols versus interfaces, abstract base classes and multiple inheritance

Learn the necessary math skills for political science and sociology with A Mathematics Course for Political and Social Research. This unique textbook is designed specifically for social science students and researchers, covering fundamental building blocks of math, basic algebra, calculus, linear algebra, and probability, among other essential subjects. With practical examples, numerous exercises, and a complete online solutions manual, it's an ideal reference for beginners and experts alike. Don't miss out on the opportunity to build (or refresh) your math skills in the social sciences with this invaluable resource.

Все началось со страха. Однажды, вернувшись с лесной прогулки, юный Квоут, актер из бродячей труппы, нашел на месте разбитого на ночь лагеря страшное пепелище. И изуродованные трупы друзей-актеров, его странствующей семьи. И тени странных созданий, прячущихся во мраке леса. Так впервые в жизнь юноши вторгаются чандрианы, загадочное племя, чьим именем пугают детей и о жутких делах которых рассказывается в древних преданиях. Теперь отыскать убийц и воздать им по заслугам становится целью Квоута. Но чтобы воевать с демонами, нужно овладеть знаниями, недоступными для простого смертного,- изучить магическое искусство и научиться повелевать стихиями...

http://www.deeplearningbook.org/ .. This book can be useful for a variety of readers, but we wrote it with two main target audiences in mind. One of these target audiences is university students (undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Deep learning has already proven useful in many software disciplines including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. This book has been organized into three parts in order to best accommodate a variety of readers. Part I introduces basic mathematical tools and machine learning concepts. Part II describes the most established deep learning algorithms that are essentially solved technologies. Part III describes more speculative ideas that are widely believed to be important for future research in deep learning. We do assume that all readers come from a computer science background. We assume familiarity with programming, a basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory.