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Mastering NLP from Foundations to LLMs

You're reading from  Mastering NLP from Foundations to LLMs

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Pages 340 pages
Edition 1st Edition
Languages
Authors (2):
Lior Gazit Lior Gazit
Profile icon Lior Gazit
Meysam Ghaffari Meysam Ghaffari
Profile icon Meysam Ghaffari
View More author details

Table of Contents (14) Chapters

Preface Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts Index Other Books You May Enjoy

Summary

As the world of NLP and LLMs continues to grow rapidly, so do the various practices of system design. In this chapter, we reviewed the design process of LLM applications and pipelines. We discussed the components of these approaches, touching on both API-based closed source and local open source solutions. We then gave you hands-on experience with code.

We later delved deeper into the system design process and introduced LangChain. We reviewed what LangChain comprises and experimented with an example pipeline in code.

To complement the system design process, we surveyed leading cloud services that allow you to experiment, develop, and deploy LLM-based solutions.

In the next chapter, we’ll focus on particular practical use cases, accompanied with code.

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