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Lec20 Denoising Autoencoders for MNIST classification (Hands on)
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Classroom Contents
Deep Learning for Visual Computing
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- 1 Deep Learning for Visual Computing (NPTEL Online Course) - Dr. Debdoot Sheet (IIT Kharagpur)
- 2 Lec01 Introduction to Visual Computing
- 3 Lec02 Feature Extraction for Visual Computing
- 4 Lec03 Feature Extraction with Python (Hands on)
- 5 Lec04 Neural Networks for Visual Computing
- 6 Lec05 Classification with Perceptron Model (Hands on)
- 7 Lec06 Introduction to Deep Learning with Neural Networks (Part 1)
- 8 Lec07 Introduction to Deep Learning with Neural Networks (Part 2)
- 9 Lec08 Multilayer Perceptron and Deep Neural Networks (Part 1)
- 10 Lec09 Multilayer Perceptron and Deep Neural Networks (Part 2)
- 11 Lec10 Classification with Multilayer Perceptron (Hands on)
- 12 Lecture 11: Autoencoder for Representation Learning and MLP Initialization
- 13 Lec12 MNIST handwritten digits classification using auto encoders (Hands on)
- 14 Lec13 Fashion MNIST classification using auto encoders
- 15 Lec14 ALL-IDB Classification using auto encoders
- 16 Lec15 Retinal Vessel Detection using auto encoders (Hands on)
- 17 Lec16 Stacked Autoencoders
- 18 Lec17 MNIST and Fashion MNIST Classification with Stacked Autoencoders (Hands on)
- 19 Lec18 Sparse and Denoising Autoencoders
- 20 Lec19 Sparse Autoencoders for MNIST classification (Hands on)
- 21 Lec20 Denoising Autoencoders for MNIST classification (Hands on)
- 22 Lecture 21 : Cost Function
- 23 Lecture 22 : Classification cost functions
- 24 Lecture 24 : Gradient Descent Learning Rule
- 25 Lecture 25 : SGD and ADAM Learning Rules
- 26 Lecture 42 : Assessing the space and computational complexity of very deep CNNs
