Curriculum
Module 1Introduction to Generative AIRead moreShow less
- What is Generative AI?
- Differences: Discriminative vs Generative models
- History & Evolution of Generative Models
- Key Applications: Text, Image, Audio, Video, Code, Synthetic Data
- Hands-on: Setting up Google Colab / Jupyter environment
- Hands-on: Intro to Hugging Face & PyTorch/TensorFlow ecosystem
Module 2Variational Autoencoders (VAEs)Read moreShow less
- Understanding Autoencoders & Latent Space
- Mathematical Intuition: ELBO, KL Divergence
- Building a VAE in PyTorch/TensorFlow
- Conditional VAEs
- Hands-on:Build a VAE for MNIST image generation
- Hands-on: Visualize latent space and interpolation
Module 3Generative Adversarial Networks (GANs)Read moreShow less
- GAN architecture: Generator & Discriminator
- Loss functions & training instability
- Types of GANs: DCGAN, Conditional GANs, CycleGAN, StyleGAN
- Challenges in GAN training
- Hands-on: Build a DCGAN from scratch
- Hands-on: Train a GAN on custom image dataset
- Hands-on: Style transfer with GANs
Frequently Asked Questions
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