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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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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction FREE CHAPTER 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

The challenges of training language models

Training large language models is a complex and resource-intensive task that poses several challenges. Here are some of the key issues:

  • Computational resources: The training of large language models requires substantial computational resources. These models have billions of parameters that need to be updated during training, which involves performing a large amount of computation over an extensive dataset. This computation is usually carried out on high-performance GPUs or tensor processing units (TPUs), and the costs associated can be prohibitive.
  • Memory limitations: As the size of the model increases, the amount of memory required to store the model parameters, intermediate activations, and gradients during training also increases. This can lead to memory issues on even the most advanced hardware. Techniques such as model parallelism, gradient checkpointing, and offloading can be used to mitigate these issues, but they add complexity...
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