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LLMs Workshop

Practical Exercises of Large Language Models

Omar Elgendy

Dive into the revolutionary world of Large Language Models (LLMs) with our comprehensive 4-hour workshop, designed to bridge the gap between theoretical knowledge and practical skills. Whether you're a budding data scientist, an AI enthusiast, or a seasoned professional looking to expand your toolkit, this course is tailored to empower you with hands-on experience in leveraging LLMs for a variety of real-world applications.

Read more

Dive into the revolutionary world of Large Language Models (LLMs) with our comprehensive 4-hour workshop, designed to bridge the gap between theoretical knowledge and practical skills. Whether you're a budding data scientist, an AI enthusiast, or a seasoned professional looking to expand your toolkit, this course is tailored to empower you with hands-on experience in leveraging LLMs for a variety of real-world applications.

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.

Intended Learners:

This course is perfect for individuals looking to deepen their understanding of LLMs and apply these models in innovative ways. Ideal for AI professionals, data scientists, and researchers eager to expand their skills and apply LLMs to solve complex problems.

Enroll now

What's inside

Learning objectives

  • Deep learning
  • Transformers
  • Langchain
  • Large language models

Syllabus

Introduction
How to use any dataset on Kaggle in Colab
LLMs Training Optimization Methods - Quantization & LoRA
What is RAG ?
Read more
Evaluation Methods for LLMs

How many models we can use in RAGs problems?

Full Fine-tuning for Question Answering
Introduction to the problem & the dataset
Data preparation
Model and tokenizer
Training
Evaluation and Testing
Fine-tuning for News-Text Summarization (QLoRA)
Tokenizer
Model Preparation
Evaluation
Extra: Publish your model on Huggingface
Find your movie - Semantic Search
Introduction to the problem & dataset
Data Preparation
Vector Database creation
Testing
Chat with your paper - Retrieval Augmented Generation (RAG)
Data Preparation & Preprocessing
LLM Prompt Integration & Model

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches advanced techniques in Deep Learning for working with Large Language Models (LLMs) like Transformers and Langchain, making this a strong fit for learners with some prior experience in Deep Learning
Provides a practical foundation for building real-world applications using LLMs, such as semantic search engines and chat interfaces
Engages learners with hands-on exercises using real-world datasets, making learning more interactive and applicable
May require learners to have basic programming skills to fully engage with exercises and projects
Taught by Omar Elgendy, an experienced data scientist and AI enthusiast
Provides learners with the opportunity to publish their fine-tuned models on Huggingface, enhancing their visibility and credibility in the AI community

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in LLMs Workshop: Practical Exercises of Large Language Models with these activities:
Review foundation of Large Language Models (LLMs)
Review the fundamental concepts of Large Language Models (LLMs) and their architecture to reinforce your understanding.
Browse courses on Large Language Models (LLMs)
Show steps
  • Read introductory articles or blog posts on LLMs to refresh your knowledge.
  • Review lecture notes or textbooks on the topic to solidify your understanding.
  • Take practice quizzes or exercises to test your comprehension.
Read 'Deep Learning for NLP with PyTorch'
Expand your knowledge of deep learning techniques for natural language processing, which are essential for building and fine-tuning LLMs.
View Natural Language Processing with PyTorch: Build... on Amazon
Show steps
  • Acquire a copy of the book 'Deep Learning for NLP with PyTorch'.
  • Read the book thoroughly, focusing on chapters related to transformer models and LLMs.
  • Take notes and highlight important concepts to enhance your retention.
Follow tutorials on transformer models
Deepen your understanding of transformer models, which are the backbone of LLMs, by following structured tutorials.
Browse courses on Transformers
Show steps
  • Search for online tutorials or courses on transformer models.
  • Choose a tutorial that aligns with your skill level and interests.
  • Follow the tutorial steps and complete the exercises.
  • Experiment with different transformer model parameters and observe their impact.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice fine-tuning LLMs for specific tasks
Gain hands-on experience in fine-tuning LLMs for specific tasks, such as question answering or text summarization.
Show steps
  • Choose a dataset and task for your fine-tuning project.
  • Select an LLM and fine-tuning technique.
  • Implement the fine-tuning process using a library like Transformers or Hugging Face.
  • Evaluate the performance of your fine-tuned LLM on the task.
Mentor a peer or assist in online forums
Enhance your understanding of LLMs by mentoring a peer or assisting in online forums, solidifying your knowledge while supporting others.
Browse courses on Large Language Models (LLMs)
Show steps
  • Identify opportunities to mentor a peer or assist in online forums related to LLMs.
  • Provide guidance and support to others based on your knowledge and experience.
  • Engage in discussions and answer questions to help others deepen their understanding.
Contribute to open-source projects related to LLMs
Deepen your understanding of LLMs and contribute to the community by participating in open-source projects related to LLMs.
Browse courses on Large Language Models (LLMs)
Show steps
  • Identify open-source projects focused on LLMs that align with your interests.
  • Review the project's documentation and codebase to understand its goals and implementation.
  • Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
  • Submit your contributions to the project and engage with the community to discuss your ideas.

Career center

Learners who complete LLMs Workshop: Practical Exercises of Large Language Models will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
4 total hours
Via
Udemy
Instructor
Omar Elgendy
Language
English
Good to know
Teaches advanced techniques in Deep Learning for working with Large Language Models (LLMs) like Transformers and Langchain, making this a strong fit for learners with some prior experience in Deep Learning
Provides a practical foundation for building real-world applications using LLMs, such as semantic search engines and chat interfaces
Engages learners with hands-on exercises using real-world datasets, making learning more interactive and applicable
May require learners to have basic programming skills to fully engage with exercises and projects
Taught by Omar Elgendy, an experienced data scientist and AI enthusiast
Provides learners with the opportunity to publish their fine-tuned models on Huggingface, enhancing their visibility and credibility in the AI community
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