Deep Learning for Visual Computing

Deep learning is a genre of machine learning algorithms that attempt to solve tasks by learning abstraction in data following a stratified description paradigm using non-­linear transformation architectures. When put in simple terms, say you want to make the machine recognize Mr. X standing in front of Mt. E on an image; this task is a stratified or hierarchical recognition task. At the base of the recognition, the pyramid would be features that can discriminate flats, lines, curves, sharp angles, color; higher up will be kernels which use this information to discriminate body parts, trees, natural scenery, clouds, etc.; higher up it will use this knowledge to recognize humans, animals, mountains, etc.; and higher up it will learn to recognize Mr. X and Mt. E and finally the apex lexical synthesizer module would say that Mr. X is standing in front of Mt. E. Deep learning is all about how you make machines synthesize this hierarchical logic and also learn these representative features and kernels all by itself. It has been used to solve problems like handwritten character recognition, object and product recognition, and localization, image captioning, generating synthetic images for self-driving cars. This course would provide you insights into the theory and coding practice of deep learning for visual computing through curated exercises with Python and PyTorch on current developments.

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