Behavioural Safety Demystified With An Introduction To Nlp

Behavioural safety is a vitally important subject, but yet we let it languish in obscurity and terminology; we confound anyone wanting to learn more about it by throwing academic jargon in their face. That's why I've written this book to bring the subject to life. This book will demystify behavioural safety! I want to peel back the layers of academic-sounding rules and theories, shift through the masses of white papers and really get down to the crux of the matter in a jargon-less way. In a way that you'll find easy to appreciate and, hopefully, also enjoy! At the same time, I'll also give you an easy-to-understand introduction to Neuro Linguistic Programming which can really transform the way you work.

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

Write modern natural language processing applications using deep learning algorithms and TensorFlow Key Features Focuses on more efficient natural language processing using TensorFlow Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches Provides choices for how to process and evaluate large unstructured text datasets Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence Book Description Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. What you will learn Core concepts of NLP and various approaches to natural language processing How to solve NLP tasks by applying TensorFlow functions to create neural networks Strategies to process large amounts of data into word representations that can be used by deep learning applications Techniques for performing sentence classification and language generation using CNNs and RNNs About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks How to write automatic translation programs and implement an actual neural machine translator from scratch The trends and innovations that are paving the future in NLP Who this book is for This book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.

Do you struggle to lose weight and wonder why? Do your bad habits and lack of confidence hold you back? Do you find yourself repeating bad patterns of behavior? Fix Your Lifewill show you how easy it can be to rid yourself of life's irritating problems by using the latest psychological techniques of NLP. This is an ideal introduction to the subject, as the author Alicia Eaton cuts through the technical jargon that's usually associated with NLP and explains how the techniques and strategies used by some of the world's most successful people, can easily be incorporated into your daily life. As well as explaining how our minds work and why it's so easy to fall into bad patterns of behavior, the author presents the NLP techniques as 'Apps for the Mind'. So, just as you'd download an App for your phone or computer to expand its' capabilities, you'll now be able to download an 'App for your Mind' to enable you to achieve more than ever before. Client stories from the author's Harley Street practice demonstrate how to fix fears and phobias such as public-speaking or fear of flying; deal with bad habits such as shopping addiction or Facebook obsessions and even apply your very own hypnotic gastric band to combat overeating. Readers are encouraged to view this book as a 'first aid kit for the mind' that can support them, plus friends and family, for many years.

Offers techniques for expanding ideas, altering perceptions, and improving communication and listening skills to achieve fulfillment and success

This the best selling introduction to Neuro-Linguistic Programming (NLP), written in an informal and entertaining style. This book will intorduce the reader to a remarkable new approach to the study of human communications and therapeutic change. Managers, sales people, consultants, therapists, parents, educators -- anyone interested in or involved with influential communications and personal change will benefit from reading this unusual book.

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.