Machine Learning For Natural Language Processing Insights Into Text And Speech Analysis
Download Machine Learning For Natural Language Processing Insights Into Text And Speech Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning For Natural Language Processing Insights Into Text And Speech Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
MACHINE LEARNING FOR NATURAL LANGUAGE PROCESSING: INSIGHTS INTO TEXT AND SPEECH ANALYSIS
Author: Mr. Harish Reddy Gantla
language: en
Publisher: Xoffencerpublication
Release Date: 2024-05-16
The fourth industrial revolution, according to the World Economic Forum, is about to begin. This will blend the physical and digital worlds in ways we couldn’t imagine a few years ago. Advances in machine learning and AI will help usher in these existing changes. Machine learning is transformative which opens up new scenarios that were simply impossible a few years ago. Profound gaining addresses a significant change in perspective from customary programming improvement models. Instead of having to write explicit top-down instructions for how software should behave, deep learning allows your software to generalize rules of operations. Deep learning models empower the engineers to configure, characterized by the information without the guidelines to compose. Deep learning models are conveyed at scale and creation applications—for example, car, gaming, medical services, and independent vehicles. Deep learning models employ artificial neural networks, which are computer architectures comprising multiple layers of interconnected components. By avoiding data transmission through these connected units, a neural network can learn how to approximate the computations required to transform inputs to outputs. Deep learning models require top-notch information to prepare a brain organization to carry out a particular errand. Contingent upon your expected applications, you might have to get thousands to millions of tests. This chapter takes you on a journey of AI from where it got originated. It does not just involve the evolution of computer science, but it involves several fields say biology, statistics, and probability. Let us start its span from biological neurons; way back in 1871, Joseph von Gerlach proposed the reticulum theory, which asserted that “the nervous system is a single continuous network rather than a network of numerous separate cells.” According to him, our human nervous system is a single system and not a network of discrete cells. Camillo Golgi was able to examine neural tissues in greater detail than ever before, thanks to a chemical reaction he discovered. He concluded that the human nervous system was composed of a single cell and reaffirmed his support for the reticular theory. In 1888, Santiago Ramon y Cajal used Golgi’s method to examine the nervous system and concluded that it is a collection of distinct cells rather than a single cell.
Deep Learning for NLP and Speech Recognition
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
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.