LLMs Workshop: Practical Exercises of Large Language Models
Practice Generative AI and Large Language Models with Real-world Exercises!
IT and Software ,Other IT and Software,Python
Lectures -26
Duration -4 hours
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Course Description
Our exhaustive 4-hour workshop dives ground dwellers learners into revolutionary Large Language Modelling spaces. It serves the need of bridging theory with the practical side of learning outfit. Thus, whether budding data scientist, amateur AI enthusiast, or indeed the professional with a well-honed toolkit considering augmenting it, this course is meant to empower you with experience in putting LLMs to good use in performing varied real-world applications.
What You Would Learn:
- A Primer I on the Advanced Techniques: Start off with basics regarding the Large Language Models architecture and capabilities of the progression to advanced optimization methods like Quantization and LoRA.
- Applications in the Real World: Get spirited as projects like building a semantic search engine to find movies or developing a chat interface with scholarly articles come into your mind, or hold the kind of knowledge you apply in solid, moving ways.
- Model Publication Learning: Bonus content on which you'll also learn how to float your fine-tuned models into the world, through Huggingface, giving your presence in the AI community further visibility.
Target Learners:
This course is very much suited for those individuals seeking to further their insights into LLMs and use the models in innovative applications. It is a program for AI professionals, data scientists and researchers who want to expand their talents and apply these models to solve complicated problems.
Goals
What You'll Learn:
Fundamentals and Advanced Techniques: Start with the basics of Large Language Models, including their architecture and capabilities, before progressing to advanced optimization methods such as Quantization and LoRA.
Practical Exercises: Engage in structured exercises using Kaggle datasets in Colab, fine-tuning models for tasks like question answering and text summarization with QLoRA and exploring cutting-edge concepts such as Retrieval Augmented Generation (RAG).
Real-World Applications: Tackle engaging projects like building a semantic search engine to find movies and developing a chat interface with scholarly articles, applying your knowledge in tangible, impactful ways.
Model Publication: As a bonus, learn how to share your fine-tuned models with the world through Huggingface, enhancing your visibility in the AI community.
Prerequisites
- Basic Python Knowledge
- Some knowledge of Machine Learning and Deep Learning
- Transformer knowledge is a plus

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
4 Lectures
-
How to use any dataset on Kaggle in Colab 04:55 04:55
-
LLMs Training Optimization Methods - Quantization & LoRA 11:51 11:51
-
What is RAG 18:50 18:50
-
Evaluation Methods for LLMs 10:10 10:10
Full Fine-tuning for Question Answering
5 Lectures

Fine-tuning for News-Text Summarization (QLoRA)
7 Lectures

Find your movie - Semantic Search
5 Lectures

Chat with your paper - Retrieval Augmented Generation (RAG)
5 Lectures

Instructor Details

Omar Elgendy
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