Deep Learning

A Mathematical Introduction

Welcome

Dear Faculty, Students and Learners

See the course page for recent information on Lectures, Labs, Resources etc..

Announcements

  • [14-Dec-2024] Notes added to L06 on S4 models.
  • [16-Nov-2024] Notes added to L05 on Transformers.
  • [24-Oct-2024] Notes added to L04 on RNNs.
  • [21-Sep-2024] L02, L03 added.
  • [07-Sep-2024] L01 added.
  • [21-Aug-2024] Course website up

Overview

Part-1: A Mathematical Introduction to Deep Learning Models

  • Feed Forward Neural Networks (FFNs)
  • Convolution Neural Networks (CNNs)
  • Kolmogorov-Arnold Networks (KANs)
  • Recurrent Neural Networks (RNNs)
  • Transformers
  • Graph Neural Networks (GNNs)
  • Selective Structured State Space Models (S4)

Part-2: A Mathematical Introduction to Deep Generative Models

  • Variational Auto Encoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Flow Networks
  • Diffusion Models