NOC:Deep Learning (Prof. P.K. Biswas)


Lecture 1 - Introduction


Lecture 2 - Feature Descriptor - I


Lecture 3 - Feature Descriptor - II


Lecture 4 - Bayesian Learning - I


Lecture 5 - Bayesian Learning - II


Lecture 6 - Discriminant Function - I


Lecture 7 - Discriminant Function - II


Lecture 8 - Discriminant Function - III


Lecture 9 - Linear Classifier - I


Lecture 10 - Linear Classifier - II


Lecture 11 - Support Vector Machine - I


Lecture 12 - Support Vector Machine - II


Lecture 13 - Linear Machine


Lecture 14 - Multiclass Support Vector Machine - I


Lecture 15 - Multiclass Support Vector Machine - II


Lecture 16 - Optimization


Lecture 17 - Optimization Techniques in Machine Learning


Lecture 18 - Nonlinear Functions


Lecture 19 - Introduction to Neural Network


Lecture 20 - Neural Network - II


Lecture 21 - Multilayer Perceptron - I


Lecture 22 - Multilayer Perceptron - II


Lecture 23 - Backpropagation Learning


Lecture 24 - Loss Function


Lecture 25 - Backpropagation Learning- Example - I


Lecture 26 - Backpropagation Learning- Example - II


Lecture 27 - Backpropagation Learning- Example - III


Lecture 28 - Autoencoder


Lecture 29 - Autoencoder Vs PCA - I


Lecture 30 - Autoencoder Vs PCA - II


Lecture 31 - Autoencoder Training


Lecture 32 - Autoencoder Variants - I


Lecture 33 - Autoencoder Variants - II


Lecture 34 - Convolution


Lecture 35 - Cross Correlation


Lecture 36 - CNN Architecture


Lecture 37 - MLP versus CNN, Popular CNN Architecture: LeNet


Lecture 38 - Popular CNN Architecture: AlexNet


Lecture 39 - Popular CNN Architecture: VGG16, Transfer Learning


Lecture 40 - Vanishing and Exploding Gradient


Lecture 41 - GoogleNet


Lecture 42 - ResNet, Optimisers: Momentum Optimiser


Lecture 43 - Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser


Lecture 44 - Optimisers: Adagrad Optimiser


Lecture 45 - Optimisers: RMSProp, AdaDelta and Adam Optimiser


Lecture 46 - Normalization


Lecture 47 - Batch Normalization - I


Lecture 48 - Batch Normalization - II


Lecture 49 - Layer, Instance, Group Normalization


Lecture 50 - Training Trick, Regularization,Early Stopping


Lecture 51 - Face Recognition


Lecture 52 - Deconvolution Layer


Lecture 53 - Semantic Segmentation - I


Lecture 54 - Semantic Segmentation - II


Lecture 55 - Semantic Segmentation - III


Lecture 56 - Image Denoising


Lecture 57 - Variational Autoencoder - I


Lecture 58 - Variational Autoencoder - II


Lecture 59 - Variational Autoencoder - III


Lecture 60 - Generative Adversarial Network