Python Based Machine Learning And Deep Learning For Natural Language Processing
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Python-based Machine Learning and Deep Learning for Natural Language Processing
Author: Nitin Dixit
language: en
Publisher: Xoffencer international book publication house
Release Date: 2023-02-22
NLP is an interdisciplinary topic that integrates computer science, artificial intelligence, and linguistics to create algorithms and models that can process and interpret human language. The purpose of natural language processing (NLP) is to allow computers to comprehend, interpret, and produce human language, which includes speech and text. Chatbots for customer service, sentiment analysis for marketing and social media, named entity recognition for information extraction, machine translation for multilingual communication, and speech recognition for handsfree contact with technology are just a few examples. Advances in machine learning, deep learning, and big data have fueled the development of NLP approaches, which continue to improve to meet the demands of new applications. Python is one of the most popular programming languages for natural language processing (NLP) because of its ease of use, readability, and the availability of strong libraries and tools such as NLTK, spaCy, and Gensim. 1.1 OVERVIEW Natural Language Processing (NLP) is a branch of computer science, artificial intelligence, and computational linguistics dealing with computer-human interaction. NLP's purpose is to enable computers to analyse, comprehend, and produce human language, which includes speech and text. This has resulted in a wide range of applications in various industries, including customer service chatbots, sentiment analysis for marketing and social media, named entity recognition for information extraction, machine translation for multilingual communication, and speech recognition for hands-free technology interaction
Natural Language Processing with PyTorch
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. 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. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
Natural Language Processing Recipes
Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing. By using the recipes in thisbook, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. What You Will Learn Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural language generation problems Who This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing through coding exercises.