Learn · Deep Learning for Computer Vision
Lecture 15 of 64Week 3
Neural Networks: A Review - Part 1
Prof. Vineeth N Balasubramanian · IIT Hyderabad
Credits and licence
- From
- Deep Learning for Computer Vision, IIT Hyderabad (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. Coordinated by IIT Madras.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Course Introduction
- History
- [Optional] Image Formation
- Image Representation
- Linear Filtering
- Image in Frequency Domain
- [Optional] Image Sampling
- Edge Detection
- From Edges to Blobs and Corners
- Scale Space, Image Pyramids and Filter Banks
- Feature Detectors : SIFT and Variants
- [Optional] Image Segmentation
- Other Feature Spaces
- [Optional] Human Visual System
- Neural Networks: A Review - Part 1
- Neural Networks: A Review - Part 2
- Feedforward Neural Networks and Backpropagation - Part 1
- Feedforward Neural Networks and Backpropagation - Part 2
- Gradient Descent and Variants - Part 1
- Gradient Descent and Variants - Part 2
- Regularization in Neural Networks - Part 1
- Regularization in Neural Networks - Part 2
- Improving Training of Neural Networks - Part 1
- Improving Training of Neural Networks - Part 2
- Convolutional Neural Networks: An Introduction - Part 01
- Convolutional Neural Networks: An Introduction - Part 02
- Backpropagation in CNNs
- CNN Architectures for Image Classification: AlexNet, VGG
- Evolution of CNN Architectures: InceptionNet, ResNet
- Newer and Recent CNN Architectures
- Finetuning CNNs
- Visualizing CNNs
- CNNs for Object Detection: Pre-Deep Learning Era and Initial Steps
- CNNs for Object Detection: Two-Stage Models
- CNNs for Object Detection: Single-stage Models
- CNNs for Segmentation
- Recurrent Neural Networks: Introduction
- Backpropagation in RNNs
- LSTMs and GRUs
- Video Understanding using CNNs and RNNs
- Attention in Vision Models: An Introduction
- Soft and Hard Attention: Image Captioning
- Additional Content: Beyond Captioning: Visual QA and Dialog
- Self-Attention and Transformers
- From Transformers to Vision Transformers
- Transformers for Detection
- Transformers for Segmentation
- Deep Generative Models: An Introduction
- Generative Adversarial Networks - Part 1
- Generative Adversarial Networks - Part 2
- GAN Hacks and Improvements
- Variational Autoencoders
- VAEs and Disentanglement
- Introduction to Diffusion Models and DDPMs - Part 1
- Introduction to Diffusion Models and DDPMs - Part 2
- Classifier and Classifier-Free Diffusion Guidance
- Contrastive Learning and History in Face Understanding - Part 1
- Contrastive Learning and History in Face Understanding - Part 2
- Self-Supervised Learning: SimCLR
- Vision Language Models: Introduction and History
- CLIP: The Anchoring Inflection Point
- Beyond CLIP: BLIP, BLIP-2 and CoCA
- From VLMs to Multimodal LLMs
- Course Conclusion