Master Class Curriculum on Large Language Models (LLMs)
For anyone looking to understand how LLM works get practical and understand how you or your business leverage it (Use RAG).
1. Introduction to Neural Networks (45 minutes)
Objective:
Build foundational knowledge of neural networks and how they relate to LLMs.
Topics Covered:
- Basics of neural networks: Neurons, layers, and activation functions.
- How neural networks learn: Training, backpropagation, and optimization.
- Overview of deep learning and its significance in NLP.
Tools Used:
- Slides/Presentation Software (PowerPoint, Google Slides) for visual aids.
- Diagrams/Animations for illustrating neural network structures (e.g., Neural Network Playground).
Interactive Activity:
Visual walkthrough of a simple feedforward neural network using diagrams or animations.
2. Understanding Large Language Models (LLMs) (30 minutes)
Objective:
Introduce LLMs and explain how they differ from traditional neural networks.
Topics Covered:
- Definition and characteristics of LLMs.
- How LLMs process and generate language.
- Key terms: Tokenization, embeddings, attention mechanisms, and transformer architecture.
Tools Used:
- Jupyter Notebook or Google Colab for interactive demonstration (if needed).
- Pre-built API Demo Tool like OpenAI’s Playground or Hugging Face's Transformer-based demos to illustrate real-time text generation.
Interactive Activity:
Demo of text generation using an accessible LLM API to illustrate LLM capabilities.
3. Types of LLMs and Their Applications (45 minutes)
Objective:
Familiarize participants with the range of LLMs currently available and their applications.
Topics Covered:
- Overview of popular LLMs: ChatGPT, GPT-3, BERT, T5, etc.
- Differences and applications of each model type.
- Ethical considerations and limitations of LLMs.
Tools Used:
- Comparison Slides showing the differences between various LLMs.
- Video Clips or Case Studies showing real-world applications of each model type.
Q&A Session:
Open floor for questions on LLM types and applications.
4. Hands-On: Building a Simple LLM Chatbot (2 hours)
Objective:
Guide participants in building a basic chatbot using OpenAI’s ChatGPT API.
Topics Covered:
- Setting up API access and environment (basic installation and setup).
- Structuring a conversation flow for the chatbot.
- Making API calls to ChatGPT to generate responses.
Tools Used:
- Python IDE (e.g., Jupyter Notebook, VS Code, or Google Colab) for coding the chatbot.
- OpenAI API: Participants need API access to OpenAI’s GPT model.
- Environment Setup Tools: Python and required libraries (like `openai`, `requests`) for API calls.
- Debugging Tools: Demonstrate using simple logging or debugging techniques in the IDE.
Activity:
Step-by-step coding session, allowing participants to follow along and build their own chatbot.
Debugging and Testing:
Troubleshooting common issues and testing chatbot functionalities.
4B: How Can Businesses gain value from this ? Introduce RAG.
5. Wrap-Up and Next Steps (15 minutes)
Objective:
Conclude the class and provide resources for further learning.
Topics Covered:
- Recap of key concepts covered.
- Suggested resources: books, online courses, and communities for further exploration.
- Career and project ideas for those interested in specializing in LLMs.
Debugging and Testing:
Troubleshooting common issues and testing chatbot functionalities.
4B: How Can Businesses gain value from this ? Introduce RAG.
About Trainer :
Data Scientist with 10 years experience, Leader at Hankali Intel, Certified Trainer, Speaker, Tutor.
Currently Contracts at the Data Analytics at The Credit Pros.
LinkedIN :
CPD : https://thecpdregister.com/view/michael-mgbame-934646
Benefits :
- Learn how your Business can get value from building Custom LLM Powered ChatBots.
- Certificate of Attendance.
- Expert Curriculum developed by CPD Certified Trainer.
- An AI Powered ChatBot to add to your Portfolio.
- Free Resources to Enhance Learning
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