基于Gentle AdaBoost的多语种识别及语言关系研究，孙乐，胡浩基，本文将一种新的特征提取方法应用于语种识别，采用Gentle AdaBoost算法搭建多语种识别系统，并通过两两语种的识别率分析语言之间的距离

A Gentle Introduction to Category Theory 1994 Fokkinga

Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away! Table of Contents Chapter 1 Introduction Chapter 2 Deep Neural Networks Chapter 3 Elman Neural Networks Chapter 4 Jordan Neural Netwo

Robot Operating System

This highly accessible introduction to Lisp is suitable both for novices approaching their first programming language and experienced programmers interested in exploring a key tool for artificial intelligence research. The text offers clear, reader-friendly explanations of such essential concepts as cons cell structures, evaluation rules, programs as data, and recursive and applicative programming styles. The treatment incorporates several innovative instructional devices, such as the use of function boxes in the first two chapters to visually distinguish functions from data, use of evaltrace notation in later chapters to illustrate the operation of evaluation rules, and "Dragon stories" to explain recursion. The book contains nearly 400 diagrams and illustrations, and 77 pages of answers to exercises. Advanced topics and "toolkit" sections, and a variety of complete programs, extend readers' programming power.