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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 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction 2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP 3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing 4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance 5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques 6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models 7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation 8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG 9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs 10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI 11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts 12. Index 13. Other Books You May Enjoy

Large datasets and their indelible mark on NLP and LLMs

The era of big data and the subsequent rise of NLP and LLMs are deeply linked. The transformation of NLP and LLMs into today’s powerful developments cannot be discussed without mentioning the vast datasets that became available. Let’s explore this relationship.

Purpose – training, benchmarking, and domain expertise

At its core, the emergence of large datasets has provided the raw material required to train increasingly sophisticated models. Typically, the larger the dataset, the more comprehensive and diverse the information the model can learn from.

Large datasets not only serve as training grounds but also provide benchmarks for evaluating model performance. This has led to standardized measures, giving researchers clear targets and allowing for apples-to-apples comparisons between models. There is a collection of benchmarks that are common and can be used for evaluating LLMs. One famous and very...

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