Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications.

Complete with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future.

By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.

Osoby które kupowały "Natural Language Understanding with Python. Combine natural language technology, deep learning, and large language models to create human-like comprehension", wybierały także:

• Windows Media Center. Domowe centrum rozrywki 66,67 zÅ‚, (8,00 zÅ‚ -88%)
• Ruby on Rails. Ćwiczenia 18,75 zÅ‚, (3,00 zÅ‚ -84%)
• Przywództwo w Å›wiecie VUCA. Jak być skutecznym liderem w niepewnym Å›rodowisku 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Scrum. O zwinnym zarzÄ…dzaniu projektami. Wydanie II rozszerzone 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Od hierarchii do turkusu, czyli jak zarzÄ…dzać w XXI wieku 58,64 zÅ‚, (12,90 zÅ‚ -78%)

Spis treści

Natural Language Understanding with Python. Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systems eBook -- spis treści

• 1. Natural Language Understanding, Related Technologies, and Natural Language Applications
• 2. Identifying Practical Natural Language Understanding Problems
• 3. Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning
• 4. Selecting Libraries and Tools for Natural Language Understanding
• 5. Natural Language Data – Finding and Preparing Data
• 6. Exploring and Visualizing Data
• 7. Selecting Approaches and Representing Data
• 8. Rule-Based Techniques
• 9. Machine Learning Part 1 - Statistical Machine Learning
• 10. Machine Learning Part 2 – Neural Networks and Deep Learning Techniques
• 11. Machine Learning Part 3 – Transformers and Large Language Models
• 12. Applying Unsupervised Learning Approaches
• 13. How Well Does It Work? – Evaluation
• 14. What to Do If the System Isn't Working
• 15. Summary and Looking to the Future