NPTEL : NOC:Deep Learning For Visual Computing (Electrical Engineering)

Co-ordinators : Prof. Debdoot Sheet


Lecture 1 - Introduction to Visual Computing

Lecture 2 - Feature Extraction for Visual Computing

Lecture 3 - Feature Extraction with Python

Lecture 4 - Neural Networks for Visual Computing

Lecture 5 - Classification with Perceptron Model

Lecture 6 - Introduction to Deep Learning with Neural Networks

Lecture 7 - Introduction to Deep Learning with Neural Networks

Lecture 8 - Multilayer Perceptron and Deep Neural Networks

Lecture 9 - Multilayer Perceptron and Deep Neural Networks

Lecture 10 - Classification with Multilayer Perceptron

Lecture 11 - Autoencoder for Representation Learning and MLP Initialization

Lecture 12 - MNIST handwritten digits classification using autoencoders

Lecture 13 - Fashion MNIST classification using autoencoders

Lecture 14 - ALL-IDB Classification using autoencoders

Lecture 15 - Retinal Vessel Detection using autoencoders

Lecture 16 - Stacked Autoencoders

Lecture 17 - MNIST and Fashion MNIST with Stacked Autoencoders

Lecture 18 - Denoising and Sparse Autoencoders

Lecture 19 - Sparse Autoencoders for MNIST classification

Lecture 20 - Denoising Autoencoders for MNIST classification

Lecture 21 - Cost Function

Lecture 22 - Classification cost functions

Lecture 23 - Optimization Techniques and Learning Rules

Lecture 24 - Gradient Descent Learning Rule

Lecture 25 - SGD and ADAM Learning Rules

Lecture 26 - Convolutional Neural Network Building Blocks

Lecture 27 - Simple CNN Model: LeNet

Lecture 28 - LeNet Definition

Lecture 29 - Training a LeNet for MNIST Classification

Lecture 30 - Modifying a LeNet for CIFAR

Lecture 31 - Convolutional Autoencoder and Deep CNN

Lecture 32 - Convolutional Autoencoder for Representation Learning

Lecture 33 - AlexNet

Lecture 34 - VGGNet

Lecture 35 - Revisiting AlexNet and VGGNet for Computational Complexity

Lecture 36 - GoogLeNet - Going very deep with convolutions

Lecture 37 - GoogLeNet

Lecture 38 - ResNet - Residual Connections within Very Deep Networks and DenseNet - Densely connected networks

Lecture 39 - ResNet

Lecture 40 - DenseNet

Lecture 41 - Space and Computational Complexity in DNN

Lecture 42 - Assessing the space and computational complexity of very deep CNNs

Lecture 43 - Domain Adaptation and Transfer Learning in Deep Neural Networks

Lecture 44 - Transfer Learning a GoogLeNet

Lecture 45 - Transfer Learning a ResNet

Lecture 46 - Activation pooling for object localization

Lecture 47 - Region Proposal Networks (rCNN and Faster rCNN)

Lecture 48 - GAP + rCNN

Lecture 49 - Semantic Segmentation with CNN

Lecture 50 - UNet and SegNet for Semantic Segmentation

Lecture 51 - Autoencoders and Latent Spaces

Lecture 52 - Principle of Generative Modeling

Lecture 53 - Adversarial Autoencoders

Lecture 54 - Adversarial Autoencoder for Synthetic Sample Generation

Lecture 55 - Adversarial Autoencoder for Classification

Lecture 56 - Understanding Video Analysis

Lecture 57 - Recurrent Neural Networks and Long Short-Term Memory

Lecture 58 - Spatio-Temporal Deep Learning for Video Analysis

Lecture 59 - Activity recognition using 3D-CNN

Lecture 60 - Activity recognition using CNN-LSTM