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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

Description:

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.

Deep Learning

NPTEL

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#Computer Science #Deep Learning #Artificial Intelligence #Neural Networks #Autoencoders