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Introduction to the course: Overview
About the Course
Know your Instructor
Fundamentals of Generative AI: Module 1
1.0 Introduction to Generative AI: The bigger Picture
1.1 What is Generative AI?
1.2 Types of Generative AI models: Most relevant
1.3(a) What makes Transformers so powerful in Generative content?
1.3(b) Tokenization in Transformers
1.3(c) Self Attention mechanism in Transformers
Mathematical Understanding of Transformers Architecture: Simple learning
1.4 Applications of Generative AI
Bonus lecture 1
Reference to Bonus lecture
Implementing Self-Attention mechanism
Bonus reference: Getting started with Llama models
Hands-on with HuggingFace: Module 2
2.0 Getting started with Module 2: Building LLM powered solutions
2.1 Introduction to HuggingFace: Open Source AI community
2.2(a) Getting started with Transformers pipeline
2.2(b) Various Pipeline parameters
2.3 End to End QA: LLM powered tools
2.4 End 2 End LLM Project: Chatbot development with interface
Bonus 2(a) Introduction to HuggingChat: Open Source conversational agent (ChatGPT alternative)
Bonus 2(b) Building chatbot interface using Gradio
Implement the Sentiment Analysis pipeline, similar to Question Answering pipeline presented in the lectures
(Bonus) Working with OpenAI API
Customizing LLMs: Approach to fine-tuning and Prompt Engineering (Module 3)
3.0 Introduction to customizing LLMs
3.1 Understanding customization technique: Prompt Engineering
Prompt Engineering 101 -- Master ChatGPT using 6 secret ingredients 🤫
3.2 Fine-tuning LLMs
3.3(a) Loading and Downloading datasets from Hugging Face
3.3(b) Operations on datasets: Preprocessing
3.4(a) Preparing data and Model (LLM) for fine-tuning
3.4(b) Introduction to Trainers module for fine-tuning LLMs
3.4(c) How to choose the base model for finetuning?
3.4(d) Training pipeline using trainers module
Fine Tuning Microsoft DialoGPT for building custom Chatbot || Step-by-step guide