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AL-503 (B) · Deep Learning/Official Syllabus

Deep Learning (AL-503 (B)) - Official Syllabus

1
Unit 1

Introduction

  • History of Deep Learning
  • McCulloch Pitts Neuron
  • Multilayer Perceptions (MLPs)
  • Representation Power of MLPs
  • Sigmoid Neurons
  • Feed Forward Neural Networks
  • Back propagation
  • weight initialization methods
  • Batch Normalization
  • Representation Learning
  • GPU implementation
  • Decomposition – PCA and SVD
2
Unit 2

Deep Feedforward Neural Networks

  • Deep Feedforward Neural Networks
  • Gradient Descent (GD)
  • Momentum Based GD
  • Nesterov Accelerated GD
  • Stochastic GD
  • AdaGrad
  • Adam
  • RMSProp
  • Auto-encoder
  • Regularization in auto-encoders
  • Denoising auto-encoders
  • Sparse auto-encoders
  • Contractive auto-encoders
  • Variational auto-encoder
  • Auto-encoders relationship with PCA and SVD
  • Dataset augmentation
3
Unit 3

Introduction to Convolutional neural Networks (CNN) and its architectures

  • Introduction to Convolutional neural Networks (CNN) and its architectures
  • CCN terminologies: ReLu activation function
  • Stride
  • padding
  • pooling
  • convolutions operations
  • Convolutional kernels
  • types of layers: Convolutional, pooling, fully connected
  • Visualizing CNN
  • CNN examples: LeNet
  • AlexNet
  • ZF-Net
  • VGGNet
  • GoogLeNet
  • ResNet
  • RCNN etc.
  • Deep Dream
  • Deep Art
  • Regularization: Dropout
  • drop Connect
  • unit pruning
  • stochastic pooling
  • artificial data
  • injecting noise in input
  • early stopping
  • Limit Number of parameters
  • Weight decay etc.
4
Unit 4

Introduction to Deep Recurrent Neural Networks and its architectures

  • Introduction to Deep Recurrent Neural Networks and its architectures
  • Backpropagation Through Time (BPTT)
  • Vanishing and Exploding Gradients
  • Truncated BPTT
  • Gated Recurrent Units (GRUs)
  • Long Short Term Memory (LSTM)
  • Solving the vanishing gradient problem with LSTMs
  • Encoding and decoding in RNN network
  • Attention Mechanism
  • Attention over images
  • Hierarchical Attention
  • Directed Graphical Models
  • Applications of Deep RNN in Image Processing, Natural Language Processing, Speech recognition, Video Analytics
5
Unit 5

Introduction to Deep Generative Models

  • Introduction to Deep Generative Models
  • Restricted Boltzmann Machines (RBMs)
  • Gibbs Sampling for training RBMs
  • Deep belief networks
  • Markov Networks
  • Markov Chains
  • Auto-regressive Models: NADE
  • MADE
  • PixelRNN
  • Generative Adversarial Networks (GANs)
  • Applications of Deep Learning in Object detection, speech/ image recognition, video analysis, NLP, medical science etc.

Reference Books

  • Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems — Aurelien Geon — O'Reilly
  • Introduction to Machine Learning with Python: A Guide for Data Scientists — Andreas Muller — O'Reilly
  • Deep Learning: A Practitioner's Approach — Adam Gibson, Josh Patterson — O'Reilly
  • Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville — MIT Press
  • Neural Networks and Deep Learning: A Textbook — Charu C. Aggarwal — Springer
  • Deep Learning with Python — Francois Chollet — Manning Publications
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