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AD-601Deep Learning
Unit-I: Introduction to Deep Learning
Introduction to Deep Learning: Basics: Biological Neuron, Idea of computational units, McCulloch–Pitts Neural Model, Linear Perceptron, Perceptron Learning, Feed Forward and Back Propagation Networks.
Unit-II: Feedforward Networks
Feedforward Networks: Multilayer Perceptron, Gradient Descent, Backpropagation, Empirical Risk Minimization, regularization, auto encoders.
Unit-III: Convolutional Networks
Convolutional Networks: The Convolution Operation, Variants of the Basic Convolution Function, Structured Outputs, Efficient Convolution Algorithms, Random or Unsupervised Features, LeNet, AlexNet
Unit-IV: Recurrent Neural Networks
Recurrent Neural Networks: Bidirectional RNNs, Deep Recurrent Networks Recursive Neural Networks, The Long Short-Term Memory and Other Gated RNNs
Unit-V: Deep Generative Models
Deep Generative Models: Boltzmann Machines, Restricted Boltzmann Machines, Introduction to MCMC and Gibbs Sampling, Gradient computations in RBMs, Deep Belief Networks, Deep Boltzmann Machines
APPLICATIONS
Image Processing, Speech Recognition, Natural Language Processing
REFERENCES
1. Ian Goodfellow, YoshuaBengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.
2. Francois Chollet, “Deep Learning with Python”, Manning; Second Edition, 2021.
Notes
Assignment
AD602_Deep_Learning_Assignment_Unit 1
AD602_Deep_Learning_Assignment_Unit 2
AD602_Deep_Learning_Assignment_Unit 3
AD602_Deep_Learning_Assignment_Unit 4
AD602_Deep_Learning_Assignment_Unit 5
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