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.
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