Conversational AI with Rasa : build, automate, and deploy AI-powered text and voice-based assistants and chatbots /

Create next-level AI assistants and transform how customers communicate with businesses with the power of natural language understanding and dialogue management using Rasa Key Features Understand the architecture and put the underlying principles of the Rasa framework to practice Learn how to quickl...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kong, Xiaoquan (Autor), Wang, Guan (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2021.
Temas:
Natural language processing (Computer science)
Natural Language Processing
Traitement automatique des langues naturelles.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Conversational AI with Rasa :  |b build, automate, and deploy AI-powered text and voice-based assistants and chatbots /  |c Xiaoquan Kong, Guan Wang. 
264 1 |a Birmingham :  |b Packt Publishing,  |c 2021. 
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520 |a Create next-level AI assistants and transform how customers communicate with businesses with the power of natural language understanding and dialogue management using Rasa Key Features Understand the architecture and put the underlying principles of the Rasa framework to practice Learn how to quickly build different types of chatbots such as task-oriented, FAQ-like, and knowledge graph-based chatbots Explore best practices for working with Rasa and its debugging and optimizing aspects Book DescriptionThe Rasa framework enables developers to create industrial-strength chatbots using state-of-the-art natural language processing (NLP) and machine learning technologies quickly, all in open source. Conversational AI with Rasa starts by showing you how the two main components at the heart of Rasa work – Rasa NLU (natural language understanding) and Rasa Core. You'll then learn how to build, configure, train, and serve different types of chatbots from scratch by using the Rasa ecosystem. As you advance, you'll use form-based dialogue management, work with the response selector for chitchat and FAQ-like dialogs, make use of knowledge base actions to answer questions for dynamic queries, and much more. Furthermore, you'll understand how to customize the Rasa framework, use conversation-driven development patterns and tools to develop chatbots, explore what your bot can do, and easily fix any mistakes it makes by using interactive learning. Finally, you'll get to grips with deploying the Rasa system to a production environment with high performance and high scalability and cover best practices for building an efficient and robust chat system. By the end of this book, you'll be able to build and deploy your own chatbots using Rasa, addressing the common pain points encountered in the chatbot life cycle. What you will learn Use the response selector to handle chitchat and FAQs Create custom actions using the Rasa SDK Train Rasa to handle complex named entity recognition Become skilled at building custom components in the Rasa framework Validate and test dialogs end to end in Rasa Develop and refine a chatbot system by using conversation-driven deployment processing Use TensorBoard for tuning to find the best configuration options Debug and optimize dialogue systems based on Rasa Who this book is for This book is for NLP professionals as well as machine learning and deep learning practitioners who have knowledge of natural language processing and want to build chatbots with Rasa. Anyone with beginner-level knowledge of NLP and deep learning will be able to get the most out of the book. 
505 0 |a Table of Contents Introduction to Chatbots and the Rasa Framework Natural Language Understanding in Rasa Rasa Core Handling Business Logic Working with Response Selector to Handle chitchat and FAQs Knowledge Base Actions to Handle Question Answering Entity Roles and Groups for Complex Named Entity Recognition Customization of Rasa Testing and Production Deployment Conversation-Driven Development and Interactive Learning Debugging, Optimization, and the Community Ecosystem. 
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650 6 |a Traitement automatique des langues naturelles. 
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700 1 |a Wang, Guan,  |e author. 
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