This audiobook is narrated by a digital voice.
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.
This audiobook is narrated by a digital voice.
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.
Natural Language Processing with AI Agents: Techniques for Real-World Problems
Natural Language Processing with AI Agents: Techniques for Real-World Problems
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Product Details
| BN ID: | 2940194601660 |
|---|---|
| Publisher: | Anand Vemula |
| Publication date: | 02/01/2025 |
| Edition description: | Unabridged |
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