CSPC-615 Deep Learning | |||||||
|---|---|---|---|---|---|---|---|
Teaching Scheme | Credit | Marks Distribution | Duration of End Semester Examination | ||||
| L | T | P | Internal Assessment | End Semester Examination | Total | ||
| 3 | 1 | 0 | 4 | Maximum Marks: 40 | Maximum Marks: 60 | 100 | 3 Hours |
| Minimum Marks: 16 | Minimum Marks: 24 | 40 | |||||
Unit-I
Introduction: History of Deep Learning, McCulloch Pitts Neuron, Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Feed Forward Neural Networks, Back propagation.
Deep Learning Applications: Image Processing, Natural Language Processing, Speech recognition, Video Analytics.
Unit-II
Activation functions and parameters: Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, Principal Component Analysis and its interpretations, Singular Value Decomposition, Parameters v/s Hyper-parameters.
Unit-III
Auto-encoders & Regularization: Auto encoders and relation to PCA, Regularization in auto encoders, Denoising auto encoders, Sparse auto encoders, Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Encoder Decoder Models, Attention Mechanism, Attention over images, Batch Normalization.
Unit-IV
Deep Learning Models: Introduction to CNNs, Architecture, Convolution/pooling layers, CNN Applications, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet. Introduction to RNNs, Back propagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs.