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AD-601 · Deep Learning/Official Syllabus

Deep Learning (AD-601) - Official Syllabus

1
Unit 1

Introduction to Deep Learning

  • Biological Neuron
  • Idea of computational units
  • McCullochPitts Neural Model
  • Linear Perceptron
  • Perceptron Learning
  • Feed Forward Networks
  • Back Propagation Networks
2
Unit 2

Feedforward Networks

  • Multilayer Perceptron
  • Gradient Descent
  • Backpropagation
  • Empirical Risk Minimization
  • regularization
  • auto encoders
3
Unit 3

Convolutional Networks

  • The Convolution Operation
  • Variants of the Basic Convolution Function
  • Structured Outputs
  • Efficient Convolution Algorithms
  • Random or Unsupervised Features
  • LeNet
  • AlexNet
4
Unit 4

Recurrent Neural Networks

  • Bidirectional RNNs
  • Deep Recurrent Networks
  • Recursive Neural Networks
  • The Long Short-Term Memory
  • Other Gated RNNs
5
Unit 5

Deep Generative Models

  • Boltzmann Machines
  • Restricted Boltzmann Machines
  • Introduction to MCMC and Gibbs Sampling
  • Gradient computations in RBMs
  • Deep Belief Networks
  • Deep Boltzmann Machines
  • Image Processing
  • Speech Recognition
  • Natural Language Processing

Laboratory Experiments

  • Write a Program to implement Linear Perceptron.
  • Write a Program to implement Multi-Layer Perceptron.
  • Write a Program to implement Autoencoders.
  • Write a Program to implement basic Convolutional Neural Network for Image Classification.
  • Write a Program to implement LeNet for image classification
  • Write a Program to implement AlexNet for image classification
  • Write a Program to implement RNN for text classification
  • Write a Program to implement LSTM for text prediction.
  • Write a Program to implement Boltzmann Machines for any real world classification problem.
  • Write a Program to implement restricted Boltzmann Machines for any real world classification problem.

Reference Books

  • Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville — MIT Press
  • Deep Learning with Python — Francois Chollet — Manning
  • Deep Learning with Applications Using Python — Navin Kumar Manaswi — Apress
  • Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms — Nikhil Buduma — O'Reilly publications
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