Neural Networks For Vision Speech And Natural Language
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Neural Networks for Vision, Speech and Natural Language
Author: R. Linggard
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This book is a collection of chapters describing work carried out as part of a large project at BT Laboratories to study the application of connectionist methods to problems in vision, speech and natural language processing. Also, since the theoretical formulation and the hardware realization of neural networks are significant tasks in themselves, these problems too were addressed. The book, therefore, is divided into five Parts, reporting results in vision, speech, natural language, hardware implementation and network architectures. The three editors of this book have, at one time or another, been involved in planning and running the connectionist project. From the outset, we were concerned to involve the academic community as widely as possible, and consequently, in its first year, over thirty university research groups were funded for small scale studies on the various topics. Co-ordinating such a widely spread project was no small task, and in order to concentrate minds and resources, sets of test problems were devised which were typical of the application areas and were difficult enough to be worthy of study. These are described in the text, and constitute one of the successes of the project.
Deep Learning for Natural Language Processing
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.
Neural Network Methods for Natural Language Processing
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.