Overview

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COURSE OUTLINE : The availability of a huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, but Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course, we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course, students will acquire the knowledge of applying Deep Learning techniques to solve various real-life problems.

Syllabus

NPTEL: Deep Learning.
Lecture 01 : Introduction.
Lecture 02 : Feature Descriptor - I.
Lecture 03 : Feature Descriptor - II.
Lecture 04 : Bayesian Learning - I.
Lecture 05 : Bayesian Learning - II.
Lecture 06 : Discriminant Function - I.
Lecture 07 : Discriminant Function - II.
Lecture 08 : Discriminant Function - III.
Lecture 09 : Linear Classifier.
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.
Lecture 22 : Multilayer Perceptron - II.
Lecture 23 : Backpropagation Learning.
Lecture 24 : Loss Function.
Lecture 25 : Backpropagation Learning - Example.
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.
Lecture 58 : Variational Autoencoder - II.
Lecture 59 : Variational Autoencoder - III.
Lecture 60 : Generative Adversarial Network.