Syllabus  |   Lectures  |   Downloads  |   FAQ  |   Ask a question  |  
Course Co-ordinated by IIT Kharagpur
Coordinators
 
Prof. Debdoot Sheet
IIT Kharagpur

 

Download Syllabus in PDF format



Untitled Document
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.

Week

Topics

1.

Introduction to Visual Computing and Neural Networks

2.

Multilayer Perceptron to Deep Neural Networks with Autoencoders

3.

Autoencoders for Representation Learning and MLP Initialization

4.

Stacked, Sparse, Denoising Autoencoders and Ladder Training

5.

Cost functions, Learning Rate Dynamics and Optimization

6.

Introduction to Convolutional Neural Networks (CNN) and LeNet

7.

Convolutional Autoencoders and Deep CNN (AlexNet, VGGNet)

8.

Very Deep CNN for Classification (GoogLeNet, ResNet, DenseNet)

9.

Computational Complexity and Transfer Learning of a Network

10.

Object Localization (RCNN) and Semantic Segmentation

11.

Generative Models with Adversarial Learning

12.

Recurrent Neural Networks (RNN) for Video Classification
  • Digital Image Processing
  • Machine Learning

Goodfellow, Y, Bengio, A. Courville, “Deep Learning”, MIT Press, 2016. S. Haykin, “Neural Networks and Learning Machines”,3e,Pearson, 2008. 


NIL


NIL



Important: Please enable javascript in your browser and download Adobe Flash player to view this site
Site Maintained by Web Studio, IIT Madras. Contact Webmaster: nptel@iitm.ac.in