Deep Learning

A Mathematical Introduction

Welcome

Dear Faculty, Students and Learners

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

Announcements

• [22-Sep-2024] Notes added to L03.
• [21-Sep-2024] L02, L03 added.
• [07-Sep-2024] L01 added.
• [21-Aug-2024] Course website up

Overview

Prereqs

• Undergraduate level exposure to Linear Algebra, Calculus
• Ability to read Python code
• Basic exposure to ML/DL

Part-1: A Mathematical Introduction to Deep Learning Models

• Topics
  • 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

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

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