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 Description:
The 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.
Xiaoquan Kong is a machine learning expert specializing in NLP applications. He has extensive experience of leading teams to build NLP platforms for several Fortune Global 500 companies. He is a Google Developer Expert in machine learning and has been actively involved in contributing to TensorFlow for many years. He also has actively contributed to the development of the Rasa framework since the early stages and became a Rasa Superhero in 2018. He manages the Rasa Chinese community and has also participated in the Chinese localization of TensorFlow documents as a technical reviewer.
Guan is currently working on Al applications and research for the insurance industry. Prior to that, he was a machine learning researcher at several industry Al labs. He was raised and educated in Mainland China, lived in Hong Kong for 10 years before relocating to Singapore in 2020. Guan holds BSc degrees in Physics and Computer Science from Peking University, and an MPhil degree in Physics from HKUST. Guan is an active tech blogger and community contributor to open source projects including Rasa, receiving more than10,000 stars for his own projects on Github.
