Learn · Generative AI for Computer Vision
Lecture 4 of 46Week 1: Foundations
Mathematical Preliminaries - I (Linear Algebra)
Prof. Arijit Sur · IIT Guwahati
Credits and licence
- From
- Generative AI for Computer Vision, IIT Guwahati (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Introduction
- Generative Modelling
- Generative Vision Models
- Mathematical Preliminaries - I (Linear Algebra)
- Mathematical Preliminaries - II (Basic Probability - I)
- Mathematical Preliminaries - II (Basic Probability - II)
- Mathematical Preliminaries - I (Linear Algebra)
- Neural Network Fundamentals
- Mathematical Preliminaries - II (Basic Probability - I)
- Neural Network Fundamentals (Multi-Layer Perceptron)
- Mathematical Preliminaries - II (Basic Probability - II)
- Neural Network Fundamentals (Backpropagation)
- Neural Network Fundamentals (Overfitting)
- Convolutional Neural Network - I
- Convolutional Neural Network - II
- Convolutional Neural Network - III
- CNN with Residual connections - ResNet
- CNN Model Architectures - II
- Sequential Modelling
- Training of RNN
- LSTM
- Sequence Modelling
- Generative AI for Vision Tasks - I
- Generative AI for Vision Tasks - II
- Generative AI
- Auto - Regressive Generative Models
- Transformers - I
- Transformers - II
- Transformers - III
- Transformers - IV
- Vision Transformers
- Variational Auto - Encoder
- Variational Auto - Encoder - II
- Diffusion Models - I
- Diffusion Models - II
- Generative Adversarial Network - I
- Generative Adversarial Network - II
- Large Language Models - I
- Large Language Models - II
- Vision Language Models
- Multimodal Generative Models - I
- Multimodal Generative Models - II
- Multimodal Generative Models - III
- Agentic AI
- Agentic AI - II
- Current Research Trends and Conclusions