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Lecture 32: Autoencoder Variants I, by P.K. Biswas

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Lecture 36: Histogram Implementation-I, by P.K. Biswas

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Lecture 02: Application of Digital Image Processing, by P.K. Biswas

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Lecture 01: Introduction, by P.K. Biswas

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Lecture 27: Rotation Invariance Property, by P.K. Biswas

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Lecture 27: Backpropagation Learning-Example III, by P.K. Biswas

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CPU Design-II, by P.K. Biswas

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Lecture 45: Estimation of Degradation Model and Restoration Techniques-II, by P.K. Biswas

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Lecture 33: Autoencoder Variants II, by P.K. Biswas

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Lecture 48 : Image Registration-I, by P.K. Biswas

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Basic Transformations, by P.K. Biswas

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Lecture 17: Optimization Techniques in Machine Learning, by P.K. Biswas

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Neural Networks for Pattern Recognition (Contd.), by P.K. Biswas

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Image Segmentation-III, by P.K. Biswas

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Lecture 12: Image Formation-II, by P.K. Biswas

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Image Digitization I, by P.K. Biswas

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K-L Transform, by P.K. Biswas

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Image Restoration-III, by P.K. Biswas

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Lecture 40: Vanishing and Exploding Gradient, by P.K. Biswas

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Lecture 03: Feature Descriptor-II, by P.K. Biswas

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Lecture 11: Image Formation-I, by P.K. Biswas

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Pixels Relationships II, by P.K. Biswas

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Object Representation and Description-III, by P.K. Biswas

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Lecture 47 : Other Restoration Techniques-II, by P.K. Biswas

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Lecture 53 : Conversion of one Color Model to another-II, by P.K. Biswas

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Cache Memory Architecture, by P.K. Biswas

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Lecture 10: Linear Classifier-II, by P.K. Biswas

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Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser, by P.K. Biswas

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Perceptron Criterion, by P.K. Biswas

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Introduction to Digital Computer Organization, by P.K. Biswas

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