Lecture 14 of 62Week 3 :
Lecture 14: Multiclass Support Vector Machine - I
Prof. Prabir Kumar Biswas · IIT Kharagpur
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All the lectures
- 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
- Live Session 06-03-2020
- Live Session 15-02-2022