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Mastering Generative AI: From Foundations to Live Projects

Course Structure

Module 1: Introduction to Generative AI

  •  Definition and Evolution
  • Applications: Text, Image, Audio, Video, Code
  • Overview of Popular Models
  •  Ethics in GenAI

Mini Assignment: Research a real-world GenAI application.

  • Variational Autoencoders (VAE)
  • Generative Adversarial Networks (GANs)
  • Diffusion Models
  • Transformer-based Generative Models

Hands-on: Implement a simple GAN with MNIST.

  • LLM Fundamentals
  • Transformer Architecture
  • Fine-tuning, Prompt Engineering
  • Hugging Face Ecosystem

Hands-on: Fine-tune GPT-2 on custom text.

  • Diffusion Models (Stable Diffusion, Imagen)
  • Text-to-Image Synthesis
  • Emerging Text-to-Video Models

Hands-on: Generate custom images using Stable Diffusion.

  • TTS Models (ElevenLabs, Bark)
  • Music Generation (MusicGen, Jukebox)

Hands-on: Generate custom voices and music samples.

  • Hugging Face, OpenAI API
  • Vector Databases (Weaviate, FAISS)
  • LangChain, LlamaIndex, Streamlit, Gradio
Mini Assignment: Build a basic chatbot using OpenAI API and Streamlit.
  • Fine-tuning vs LoRA
  • Quantization, Pruning
  • Deployment Strategies
Demo: Quantize a model using bitsandbytes.
  • Evaluation Metrics
  • Bias and Hallucination Mitigation
  • AI Alignment and Safety Frameworks

Live Projects

Project 1: Text-to-Image Generation Web App

  • Stack: Streamlit + Hugging Face + Diffusers
  • Skills: Prompt Engineering, Diffusion Models
  • Stack: LangChain + OpenAI + Weaviate/FAISS
  • Skills: RAG Pipeline, Vector Search
  • Stack: Hugging Face + Gradio
  • Skills: Text-to-Audio, Fine-tuning
  • Stack: Hugging Face + FastAPI
  • Skills: Fine-tuning, Deployment
  • Stack: OpenAI API or Local LLM + Streamlit
  • Skills: Document Processing, LLM Applications
  • Course Duration
  • 12 Weeks (10 Weeks Instruction + 2 Weeks Projects)
  • Target Audience
  • Data Scientists, AI Engineers, Developers, Final Year Students with ML Knowledge
  • Prerequisites
  • Python, Machine Learning Basics, Neural Networks Understanding
Module 1: Introduction to Generative AI
  •  Definition and Evolution
  • Applications: Text, Image, Audio, Video, Code
  • Overview of Popular Models
  •  Ethics in GenAI

Hands-on: Generate custom voices and music samples.

  • Get in touch with an expert
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