Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, ´´natural images´´ are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You´ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.
This book describes novel software architectures for the integration of deep and shallow natural language processing (NLP) components in language technology. The generic markup language XML and the XML transformation language XSLT are used for flexible combination of linguistic markup produced by multiple NLP components. Shallow NLP components such as tokenizers, part-of-speech taggers, named entity recognizers and shallow parsers are combined with a deep parser, operating grammars written in the spirit of the Head-Driven Phrase Structure Grammar (HPSG) theory. The integration paradigm enables synergy leading to more robust deep parsing with increased coverage. It also constitutes a division of labor: the deep grammar models general, correct language use, while shallow systems are responsible for domain-specific extensions. Applications are presented in question answering, information extraction, natural language understanding, ontologies and the Semantic Web. The book addresses to software engineers, computational linguists and language technology engineers.
Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem.
The two-volume set of LNCS 11239 and LNCS 11240 constitutes the revised proceedings of the 16th International Conference on Theory of Cryptography, TCC 2018, held in Panaji, India, in November 2018. The total of 50 revised full papers presented in the proceedings were carefully reviewed and selected from 168 submissions. The Theory of Cryptography Conference deals with the paradigms, approaches, and techniques used to conceptualize natural cryptographic problems and provide algorithmic solutions to them and much more.
For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. 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 text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material. Supplements: Click on the Resources tab to View Downloadable Files: * Solutions * Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13 and 24 Now Available! * For additional resourcse visit the author website:
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Engage with chatbots using deep learning Who This Book Is For Data scientists and developers who want to adapt and build deep learning applications.
This book constitutes the refereed proceedings of the 14th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2018, held in Poznan, Poland, in September 2018, during the IFIP World Computer Congress. It consists of 38 carefully reviewed papers selected from 102 submissions. The papers are organized in topical sections, namely big data and cloud computing; architectures, structures and algorithms for efficient data processing; artificial intelligence, data mining and knowledge discovery; text mining, natural language processing, ontologies and semantic web; image analysis and multimedia mining.
No matter where you are on the organizational ladder, the odds are high that you´ve delivered a high-stakes presentation to your peers, your boss, your customers, or the general public. Presentation software is one of the few tools that requires professionals to think visually on an almost daily basis. But unlike verbal skills, effective visual expression is not easy, natural, or actively taught in schools or business training programs. slide:ology fills that void. Written by Nancy Duarte, President and CEO of Duarte Design, the firm that created the presentation for Al Gore´s Oscar-winning film, An Inconvenient Truth, this book is full of practical approaches to visual story development that can be applied by anyone. The book combines conceptual thinking and inspirational design, with insightful case studies from the world´s leading brands. With slide:ology you´ll learn to: - Connect with specific audiences - Turn ideas into informative graphics - Use sketching and diagramming techniques effectively - Create graphics that enable audiences to process information easily - Develop truly influential presentations - Utilize presentation technology to your advantage Millions of presentations and billions of slides have been produced and most of them miss the mark. slide:ology will challenge your traditional approach to creating slides by teaching you how to be a visual thinker. And it will help your career by creating momentum for your cause.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book´s supporting website to help course instructors prepare their lectures.