Foundations of Generative AI

Introduction to AI

Mathematical Foundations for AI

Probability, Statistics, and Linear Algebra

Basics of Neural Networks

Gradient Descent and Optimization Basics

Architectures: Feedforward, RNN, and CNN

Mini Project 1 - Build a Simple Neural Network Using TensorFlow

Mini Project 2 - Train an Autoencoder on the MNIST Dataset

Deep Generative Models

Discriminative and Generative models

Generative Adversarial Networks (GANs)

DCGAN and StyleGAN

Variational Autoencoders (VAEs)

Probabilistic Data Generation Using VAEs

Introduction to Diffusion Models

Basics of DDPMs and Applications

Mini Project 3 - Implement a GAN to Generate Handwritten Digits

Mini Project 4 - Train a VAE to Generate Faces Using the CelebA Dataset

Transformers and Large Language Models

Transformers Architecture

Attention Mechanism: Self-Attention and Positional Encoding

Encoder-Decoder Framework

Pretraining and Fine-Tuning

Pretraining objectives: MLM, CLM

Fine-tuning for Tasks Like Summarization, QA

GPT, BERT

Mini Project 5 - Sentiment Analysis Using BERT

LangChain and Multimodal Models

LangChain Installation and Basic Setup

Overview of LangChain: Prompts, Memory, Chains, Agents

Vision + text models - CLIP, DALL-E

Mini Project 6 - Train a Multimodal Model to Generate Image Captions

Mini Project 7 - Simple Q&A Application Using LangChain

RAG and Hybrid Architectures

RAG Pipeline

Retrieval Systems: BM25, FAISS, and Elasticsearch

Integrating retrieval with LLMs

Applications of RAG

Vector Databases

Building RAG Pipelines with LangChain

Projects

Project 8 - Build a Knowledge-Aware Chatbot by Integrating an External Database with LLMs

Project 9 - Fine-Tune an Open-Source LLM for the Financial Domain

Project, Extra Doubt Support

Project 10 - Text-to-Image Generation

Prompt Engineering

AI Agents