Deep Learning - IIT Ropar
Indian Institute of Technology, Ropar and NPTEL via Swayam
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Overview
INTENDED AUDIENCE: Any Interested LearnersPREREQUISITES: Working knowledge of Linear Algebra, Probability Theory. It would be beneficial if the participants have
done a course on Machine Learning.
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.
Syllabus
COURSE LAYOUT
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
Taught by
Prof. Sudarshan Iyengar
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