This book is the result of teaching experience and lecture notes collected and updated for approximately 20 years. Formal languages are divided into four types. Each has its computer processor (automaton). This book presents the theory of formal languages (including some issues related to complexity and computability) in five chapters, one introductory and one for each language type and its processor. This is a fundamentally theoretical discipline and very important for computing, hence the need to arouse the interest of students. Illustrations, solved and proposed exercises are essential for this purpose. This book is intended for undergraduate computer courses (Computer Science, Computer Engineering, Information Systems, Informatics) and also as a supporting book for graduate computer courses. Formal Language Theory is part of the Theory of Computation and as such, its knowledge is essential for all professionals and academics in the area. The author sincerely hopes that this book will serve the knowledge needs of Formal Languages and Automata disciplines and contribute as a textbook to their better progress. Good reading and good studies!
If you´re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn´t feel that you need be an expert to understand the content.
Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, (NLP) expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning - Models for textual similarity - Deep memory-based NLP - Semantic role labeling - Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He´s the technical coordinator of two large European Union-funded research security-related projects. He´s currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.
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