Natural Language Processing With Ai Agents Techniques For Real World Problems
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Natural Language Processing with AI Agents: Techniques for Real-World Problems
This book provides a comprehensive exploration of Natural Language Processing (NLP) and its application in building intelligent AI agents capable of understanding and generating human-like interactions. It covers fundamental concepts in NLP, such as tokenization, part-of-speech tagging, and named entity recognition, followed by core machine learning techniques for language understanding. The book delves into the key architectures in NLP, from traditional machine learning approaches like Naïve Bayes and SVMs to advanced deep learning models, including RNNs, LSTMs, and transformers, with a special focus on large language models (LLMs) that have transformed the field. The second section discusses the development of NLP-powered AI agents, focusing on conversational AI and chatbots, highlighting the difference between rule-based and AI-driven models. It explores designing conversational agents and managing multi-turn dialogues. The section also covers speech recognition systems, combining NLP with automatic speech recognition (ASR) for creating voice-enabled AI agents. Techniques for natural language understanding (NLU), intent detection, and semantic parsing are explored, emphasizing how AI agents interpret and respond to user queries effectively. The book also examines the role of NLP in content generation, including natural language generation (NLG) for text summarization and AI-driven content creation. Advanced applications such as sentiment analysis, question-answering systems, multimodal NLP, and emotion detection are explored, demonstrating the broad potential of NLP agents across industries like healthcare, customer support, and robotics. The final part of the book provides practical guidance on training, fine-tuning, and deploying NLP-based AI systems at scale, with insights into cloud-based solutions and real-time processing. It concludes with a discussion of the future of NLP, focusing on AI ethics, the potential of generative AI, and the evolving trends in human-AI collaboration. This book serves as a comprehensive guide for both practitioners and researchers, offering insights into the cutting-edge techniques and applications of NLP and AI agents in solving real-world problems.
Natural Language Processing in Action
Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines About the Reader This book requires a basic understanding of deep learning and intermediate Python skills. About the Author Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Table of Contents PART 1 - WORDY MACHINES Packets of thought (NLP overview) Build your vocabulary (word tokenization) Math with words (TF-IDF vectors) Finding meaning in word counts (semantic analysis) PART 2 - DEEPER LEARNING (NEURAL NETWORKS) Baby steps with neural networks (perceptrons and backpropagation) Reasoning with word vectors (Word2vec) Getting words in order with convolutional neural networks (CNNs) Loopy (recurrent) neural networks (RNNs) Improving retention with long short-term memory networks Sequence-to-sequence models and attention PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES) Information extraction (named entity extraction and question answering) Getting chatty (dialog engines) Scaling up (optimization, parallelization, and batch processing)
Real-World Natural Language Processing
Author: Masato Hagiwara
language: en
Publisher: Simon and Schuster
Release Date: 2021-12-14
Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! The field of Natural language processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assitants, text generation and more. "Real-world natural language processing" shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you'll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you'll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process.