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
