Lecture Notes || SNS Courseware

Subject Details

Dept : CSE

Sem : 6

Regul : 2023

Faculty : vasuki S

phone : NIL

E-mail : vasuki.s.cse@snsct.org

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Lecture Notes

UNIT 1:

Basic Concept of Neurons

Perceptron Algorithm

Single Layer Neural Network and Multilayer Neural Network

Feed Forward Neural Network and Back propagation Networks

Create and deploy neural networks using Tensor Flow for Image data

UNIT 2:

Deep Neural Networks

Rectified Linear Unit (ReLU)

Heuristics for Avoiding Bad Local Minima

Heuristics for Faster Training

Nestors Accelerated Gradient Descent

UNIT 3:

CNN Architectures

Convolution Layer

Pooling Layers

Hyper parameter–Activation Function

Recurrent and Recursive Nets

Recurrent Neural Networks

Deep Recurrent Networks

Recursive Neural Networks

Create and deploy Convolutional Neural networks using Keras for Image data.

UNIT 4:

Long Short Term Memory (LSTM) Networks

Sequence Prediction

Gated Recurrent

Encoder/Decoder Architectures

Autoencoders – Standard

Sparse – Denoising – Contractive

Variational Autoencoders

Applications of Autoencoders

Case Study: Representation Learning

UNIT 5:

Images segmentation – Object Detection

Automatic Image Captioning

Image generation with Generative adversarial networks

Video to Text with LSTM models

Attention models for Computer Vision

Case Study: Named Entity Recognition

Opinion Mining using Recurrent Neural Networks

Parsing and Sentiment Analysis using Recursive Neural Networks

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