COURSE LAYOUT
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
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