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Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam Optimiser
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Classroom Contents
Deep Learning
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- 1 NPTEL: Deep Learning
- 2 Lecture 01 : Introduction
- 3 Lecture 02 : Feature Descriptor - I
- 4 Lecture 03 : Feature Descriptor - II
- 5 Lecture 04 : Bayesian Learning - I
- 6 Lecture 05 : Bayesian Learning - II
- 7 Lecture 06 : Discriminant Function - I
- 8 Lecture 07 : Discriminant Function - II
- 9 Lecture 08 : Discriminant Function - III
- 10 Lecture 09 : Linear Classifier
- 11 Lecture 10 : Linear Classifier - II
- 12 Lecture 11 : Support Vector Machine - I
- 13 Lecture 12 : Support Vector Machine - II
- 14 Lecture 13 : Linear Machine
- 15 Lecture 14 : Multiclass Support Vector Machine - I
- 16 Lecture 15 : Multiclass Support Vector Machine -II
- 17 Lecture 16 : Optimization
- 18 Lecture 17 : Optimization Techniques in Machine Learning
- 19 Lecture 18 : Nonlinear Functions
- 20 Lecture 19 : Introduction to Neural Network
- 21 Lecture 20 : Neural Network -II
- 22 Lecture 21 : Multilayer Perceptron
- 23 Lecture 22 : Multilayer Perceptron - II
- 24 Lecture 23 : Backpropagation Learning
- 25 Lecture 24 : Loss Function
- 26 Lecture 25 : Backpropagation Learning - Example
- 27 Lecture 26 : Backpropagation Learning- Example II
- 28 Lecture 27 : Backpropagation Learning- Example III
- 29 Lecture 28 : Autoencoder
- 30 Lecture 29 : Autoencoder Vs. PCA I
- 31 Lecture 30 : Autoencoder Vs. PCA II
- 32 Lecture 31 : Autoencoder Training
- 33 Lecture 32 : Autoencoder Variants I
- 34 Lecture 33 : Autoencoder Variants II
- 35 Lecture 34 : Convolution
- 36 Lecture 35 : Cross Correlation
- 37 Lecture 36 : CNN Architecture
- 38 Lecture 37 : MLP versus CNN, Popular CNN Architecture: LeNet
- 39 Lecture 38 : Popular CNN Architecture: AlexNet
- 40 Lecture 39 : Popular CNN Architecture: VGG16, Transfer Learning
- 41 Lecture 40 : Vanishing and Exploding Gradient
- 42 Lecture 41 : GoogleNet
- 43 Lecture 42 : ResNet, Optimisers: Momentum Optimiser
- 44 Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser
- 45 Lecture 44 : Optimisers: Adagrad Optimiser
- 46 Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam Optimiser
- 47 Lecture 46 : Normalization
- 48 Lecture 47 : Batch Normalization-I
- 49 Lecture 48 : Batch Normalization-II
- 50 Lecture 49 : Layer, Instance, Group Normalization
- 51 Lecture 50 : Training Trick, Regularization,Early Stopping
- 52 Lecture 51 : Face Recognition
- 53 Lecture 52 : Deconvolution Layer
- 54 Lecture 53 : Semantic Segmentation - I
- 55 Lecture 54 : Semantic Segmentation - II
- 56 Lecture 55 : Semantic Segmentation - III
- 57 Lecture 56: Image Denoising
- 58 Lecture 57 : Variational Autoencoder
- 59 Lecture 58 : Variational Autoencoder - II
- 60 Lecture 59 : Variational Autoencoder - III
- 61 Lecture 60 : Generative Adversarial Network
