NOTE: This course videos are created and offered earlier by Prof. Mitesh Khapra, it will be offfered by Prof. Sudarshan Iyengar for the subsequent runs.
- Week 1 : (Partial) History of Deep Learning, Deep Learning Success Stories, McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm
- Week 2 : Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Representation Power of Feedforward Neural Networks
- Week 3 : FeedForward Neural Networks, Backpropagation
- Week 4 : Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis
- Week 5 : Principal Component Analysis and its interpretations, Singular Value Decomposition
- Week 6 : Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders
- Week 7 : Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout
- Week 8 : Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization
- Week 9 : Learning Vectorial Representations Of Words
- Week 10: Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks
- Week 11: Recurrent Neural Networks, Backpropagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs
- Week 12: Encoder Decoder Models, Attention Mechanism, Attention over images