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Deep Learning For Visual Computing

Indian Institute of Technology, Kharagpur via NPTEL

1 Review 20 students interested

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Overview

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 pyramid would be features which 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 to self driving cars. This course would provide you insights to theory and coding practice of deep learning for visual computing through curated exercises with Python and PyTorch on current developments.

Syllabus

Week 1: Introduction to Visual Computing and Neural Networks

Week 2: Multilayer Perceptron to Deep Neural Networks with Autoencoders

Week 3: Autoencoders for Representation Learning and MLP Initialization

Week 4: Stacked, Sparse, Denoising Autoencoders and Ladder Training

Week 5: Cost functions, Learning Rate Dynamics and Optimization

Week 6: Introduction to Convolutional Neural Networks (CNN) and LeNet

Week 7: Convolutional Autoencoders and Deep CNN (AlexNet, VGGNet)

Week 8: Very Deep CNN for Classification (GoogLeNet, ResNet, DenseNet)

Week 9: Computational Complexity and Transfer Learning of a Network

Week 10:Object Localization (RCNN) and Semantic Segmentation

Week 11:Generative Models with Adversarial Learning

Week 12: Recurrent Neural Networks (RNN) for Video Classification

Taught by

Debdoot Sheeet

Review for NPTEL's Deep Learning For Visual Computing
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Pdubey P
4.0 3 months ago
by Pdubey completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
Really nice course, covers all the fundamentals of Deep learning and has lab exercises to facilitate the lectures. It also covers some advanced topics like RNNs, LSTMs, and generative modelling.
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