The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students will acquire the knowledge of applying Deep Learning techniques to solve various real life problems.
INTENDED AUDIENCE : Electronics and Communication Engineering, Computer Science, Electrical Engineering
PRE-REQUISITES : Knowledge of Linear Algebra, DSP, PDE will be helpful.
INDUSTRY SUPPORT : Google, Adobe, TCS, DRDO etc.
Week 1: Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
Week 2: Linear Classifiers, Linear Machines with Hinge Loss
Week 3: Optimization Techniques, Gradient Descent, Batch Optimization
Week 4: Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
Week 5:Unsupervised Learning with Deep Network, Autoencoders
Week 6: Convolutional Neural Network, Building blocks of CNN, Transfer Learning
Week 7: Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
Week 8: Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
Week 9: Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.
Week 10: Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.
Week 11:LSTM Networks
Week 12:Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
Prof. Prabir Kumar Biswas