Learn · Mathematical Foundations of Generative AI
Lecture 6 of 44Week 2 Generative Models : Problem Formulation
Tutorial 4 : CNNs using PyTorch
Prof. Prathosh A. P · IISc Bangalore
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- Mathematical Foundations of Generative AI, IISc Bangalore (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Introduction
- Tutorial 1 : Introduction to Python Basics
- Tutorial 2 : Introduction to Numpy
- Generative Models : Problem Formulation
- Tutorial 3 : PyTorch Basics
- Tutorial 4 : CNNs using PyTorch
- Tutorial 5 : RNNs using PyTorch
- Tutorial 6 : Transfer Learning with PyTorch
- Tutorial 7 : Review of Basic Probability 1
- Tutorial 8 : Review of Basic Probability 2
- Tutorial 9 : Review of Basic Probability 3
- Tutorial 10 : Review of Machine Learning 1
- f-Divergence and Examples
- Tutorial 11 – f-Divergence and Examples
- Variational Divergence Minimization (VDM)
- Generative Adversarial Networks (GANs)
- Tutorial 12 : Implementations of Vanilla GAN, DCGAN and Conditional GAN
- Wasserstein GAN (WGAN)
- Inversion with GANs and FID
- Latent Variable Models and Introduction to Variational Autoencoder (VAE)
- VAEs Part 1
- VAEs Part 2
- Beta- VAE
- Vector Quantised VAE
- Introdution to Diffusion models
- Diffussion Models - Part 1
- Diffussion Models - Part 2
- Tutorial 13 : Wasserstein GAN (WGAN) Implementation using Gradient Clip
- Tutorial 14 : Wasserstein GAN (WGAN) Implementation using Gradient Penalty
- Tutorial 15 : VAE and Beta-VAE Implementation
- Pedagogy in the Times of AI
- Diffussion Models - Part 3
- Diffussion Models - Part 4
- Tutorial 16 : Implementation of VQ-VAE
- Tutorial 17 : Implementation overview of Diffusion Models
- Flow Matching Part 1
- Flow Matching Part 2
- Tutorial 18: Implementation of Diffussion Models Part 1
- Tutorial 19: Implementation of Diffussion Models Part 2
- Flow Matching Part 3
- Flow Matching Part 4
- Auto Regressive models and Transformers
- Reinforcement Learning for Language Model Allignment
- Reward Modelling