NOC:Deep Learning For Visual Computing


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