Large Language Models

Train and Customize Language Models

3h 18m19 lectures1 sections

What you'll learn

  • Understand the evolution of language models
  • Learn the tokenization process
  • Grasp the architectures of Transformers
  • Explore models like BERT
  • Master the principles of Prompt Engineering
  • Apply models in developing smart applications

About this course

The Large Language Models course (LLMs) is your comprehensive guide to understanding and applying the latest techniques in linguistic artificial intelligence. You will learn how the massive models operate behind the scenes, the role of Transformers, and how to customize models using techniques like Fine-Tuning and LoRA to develop smart applications that understand and interact with language in ways that exceed expectations.

Expected outcomes

  • Comprehensive understanding of training and customizing large language models.
  • Ability to use the Hugging Face platform to work with LLMs.
  • Mastery of PEFT and LoRA techniques for efficient model customization.
  • Capability to build smart applications for text analysis and content summarization.

Course content

1

Large Language Models LLMs

19 lectures
  1. Course Introduction3:51
  2. Before the Era of Transformers7:02
  3. Basics of Tokenization4:34
  4. Transformers Architecture - Part One8:02
  5. Transformers Architecture - Part Two5:07
  6. Transformers Architecture - Part Three: Multi-Head Attention Mechanism4:54
  7. Transformers Architecture - Part Four9:43
  8. Different Architectural Patterns of Transformers10:02
  9. Prompts and Prompt Engineering6:45
  10. BERT Model and Its Applications17:37
  11. Configuration Parameters in LLMs7:52
  12. Hugging Face Platform - Part One15:17
  13. Hugging Face Platform - Part Two13:41
  14. Hugging Face Platform - Part Three15:15
  15. Practical Project: Summarizing YouTube Content Using Transformers - Part One20:16
  16. Practical Project: Summarizing YouTube Content Using Transformers - Part Two15:41
  17. Introduction to Fine-Tuning15:17
  18. PEFT Techniques (Parameter-Efficient Fine-Tuning)7:42
  19. LoRA Technique (Low-Rank Adaptation)9:30

Instructor

Eng. Ziad Ahmed

Eng. Ziad Ahmed

Data scientist and engineer specializing in large language models. Expert in natural language processing, automation, and data analysis.
5,621 students3 courses