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Mastering NLP from Foundations to LLMs

You're reading from   Mastering NLP from Foundations to LLMs Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Length 340 pages
Edition 1st Edition
Languages
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Authors (2):
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Meysam Ghaffari Meysam Ghaffari
Author Profile Icon Meysam Ghaffari
Meysam Ghaffari
Lior Gazit Lior Gazit
Author Profile Icon Lior Gazit
Lior Gazit
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Toc

Table of Contents (14) Chapters Close

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 FREE CHAPTER 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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

activation function 145

exponential linear unit (ELU) 146

hyperbolic tangent (tanh) function 146

layer 147

Leaky ReLU 146

rectified linear unit (ReLU) function 146

sigmoid function 145

softmax function 146

AdaBoost 74

advanced LangChain configurations and pipelines, applying

paid LLM (OpenAI's GPT) and free LLM (from Hugging Face), selecting between 222, 223

QA chain, creating 223

required Python libraries, installing 222

advanced methods, using with chains

element of memory, inserting 225-227

LLM, used for answering questions 224

output structure, requesting 224

AllenNLP 91

Amatriain, Xavier

insights, on NLP and LLMs 281, 294, 295

Amazon Machine Images (AMIs) 215

Amazon SageMaker 215

anomaly detection 81

artificial intelligence...

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