The entire session will be covered in two parts

Theory Session – Basic & Advanced.

Practical Sessions – Basic & Advanced.

Course Content

Introduction to Generative AI:

Get to know about what is GenAI

Difference between AI, Machine Learning & Deep Learning Difference between Discriminative AI vs Generative AI Tools involved in GenAI

Know about privileged GenAI tools and Models.

Effective Road Map to explore the GenAI Model.

Generative Architecture

Introduction to Generative Adversarial Network (GAN) Architecture

Know about the Generator & Discriminator Models

How GAN architecture uses for Deep Fake Model

Difference between Variational Auto Encoders (VAEs) vs GAN architecture Hands-on: Development of Generative Images using GAN Complex Architecture Hands-on: Create your own “Deep Fake Model” using low-level GAN Structure. Deep insights of GAN applications & drawbacks of it.

Attention Is All You Need – Transformers & LLMs

What is Transformer? How the architecture looks like? Text Generation before transformers

Know about Attention Model vs Multi-Head Attention Model vs Scaled Dot- Product Attention Difference between Encoder & Decoder Architecture.

Difference between Batch vs Layer Normalization

Introduction to Large Language Models (LLMs)

Popular LLMs: BERT, BART, DistilBert

Hands-on: Conversation AI using pre-trained LLM Model

Hands-on: Image Caption Generator using pre-trained LLM Model Hands-on: Customized & Fine-tuning Question & Answering BERT Model Hands-on: Customized & Fine-tuning DistilBert Model

Mastering Generative AI (Exploring the Frontiers of Generative Models)

Learn about GPT (Generative Pre-trained Model )

Fine-Tuning the LLM Model on a specific task & Model Evaluation Parameter Efficient Fine-Tuning (PEFT)

Vision Transformer – ViT Model with API interface

Hands-on: Pre-trained Google ViT Model for Image Classification

Hands-on: Customized ViT architecture (RAW Structure for ViT Base)

Hands-on: Create your own ViT Multi-class classification and deploy the model into HuggingFace Hug using API Interface.

Git workflows

Git cheat sheet

Reinforcement Learning From Human Feedback:

Deep Insight of RLHF

RLHF: Obtaining feedback from humans RLHF: Reward model

RLHF: Fine-tuning with reinforcement learning

Relative Sync of ChatGPT with RLHF

GenAI – LangChain Framework:

What is LangChain?

Usage of LangChain Framework? How it works?

Integrating the LangChain Framework with LLM Model.

Understand about the Langchain pipeline end points, Chains & Prompt Template.

Hands-on: Create the simple LLM Langchain using the HuggingFace API Interface

Hands-on: Create the Langchain framework including with promptTemplate, AutoTokenizer & LLM Chain interfacing with HuggingFace API Interface

GenAI – Retrieval Augmented Generation (RAG)

Explore the different tools in LangChain and initialise an agent that uses the tools to read different types of files or data present in the company database

Build the backend for the system using Vectorstore options present in LangChain Divide the documents into chunks and apply the LLM to create the embeddings. Extract entity for the chunks of document and store them in the Vectorstore

Construct the Search Index and Entity Store and create a functionality to update it with every question that the user asks

Use the Chain functionality of LangChain to connect all the components

Hands-on: Create your simple RAG pipeline with FAISS Vector Store similarity search using OpenAI API or HuggingFace End-point API Interface.

Hands-on: Create your own Retrieval QA End-to-End RAG pipeline using OpenAI/HuggingFace API.

GenAI Advanced- LangGraph & VectorDB/ChromaDB

Build an agent from scratch, and understand the division of tasks between the LLM and the code around the LLM.

Integrate agentic search capabilities to enhance agent knowledge and performance.

Learn how agentic search retrieves multiple answers in a predictable format, unlike traditional search engines that return links.

Incorporate human-in-the-loop into agent systems

Hands-on: Create your own Retrieval pipeline using ChromaDB to store the pdf embedding chunks.

Add-on: Hugging Spaces- Knowledge to deploy

How to use the Llama & other popular LLM models in HuggingFace Spaces. How to create the new workspace and initializing your repository?

Hands-on: Build and deploy your own GenAI Application in Hugging Face Spaces using Streamlit.