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Subject Syllabus

AD-601 - Deep Learning

AIDS · Semester 6

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Basics: Biological Neuron Idea of computational units McCulloch-Pitts Neural Model Linear Perceptron Perceptron Learning Feed Forward and Back Propagation Networks.

Multilayer Perceptron Gradient Descent Backpropagation Empirical Risk Minimization, regularization, auto encoders.

The Convolution Operation Variants of the Basic Convolution Function Structured Outputs Efficient Convolution Algorithms Random or Unsupervised Features LeNet AlexNet

Bidirectional RNNs Deep Recurrent Networks Recursive Neural Networks The Long Short-Term Memory and Other Gated RNNs

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