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anomalydetection

Anomaly Detection with R

anonymous-cyber-security-club-of-bmsce-presentations

This contains all the presentations that I had made as a core team member in Cyber Security Club of BMSCE - "Anonymous"

ansible

Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy. Avoid writing scripts or custom code to deploy and update your applications — automate in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com/ansible/

assistive-technology-android-application-for-autism-spectrum-disorder-therapy

We, as a team of 4, collectively developed an Assistive Technology android application for autism children of the ASHA Foundation, Bangalore to help them develop the necessary motor skills like finger movements and language understanding under the guidance of Jayashree Ramesh, Director, ASHA Foundation and Dr. Indiramma M, Professor, CSE, BMSCE. We also attended the World Autism Day 2017, at Bal Bhavan, Cubbon Park to spread awareness about autism.

autoencoders

Autoencoders - a deep neural network used for feature extraction followed by clustering

deep-learning-programs

All the Deep Learning Python Programs that I tried out using tflearn

deep-neural-network-for-clustering

Autoencoders - a deep neural network was used for feature extraction followed by clustering of the "Cancer" dataset using k-means technique

diabetic-retinopathy-image-classification

Automated Diabetic retinopathy Image Classification using Deep Learning

drmlapp

github-readme-stats

:zap: Dynamically generated stats for your github readmes

machine-learning-presentations

This repository contains all the presentations made while doing my project with Dr. Senthilnath J, PhD(IISc), currently a Research Fellow at NTU, Singapore in Machine Learning particularly Deep Learning which includes techniques like Self Organizing Maps (SOM), Unsupervised Extreme Learning Machine (US-ELM), Restricted Boltzmann Machine(RBM) and Deep Autoencoders

minisom

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

multiple-bus-architecture-simulation-using-java-applets-

Multiple Bus Architecture Simulation System

presumm

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

python-elm

Extreme Learning Machine implementation in Python

self-organizing-maps

A Self Organizing Maps (SOM) or Kohonen Network is a type of Artificial Neural Network that is trained using clustering of datasets. This repo implements SOM using MiniSOM library applied on Iris Dataset and outputs the confusion matrix and clustering accuracy

sumanth-bmsce

Config files for my GitHub profile.

supervisedelm

Implementation of Supervised Extreme Learning Machine for binary classification

unsupervised_extreme_learning_machine

Unsupervised Extreme Learning Machine(ELM) is a non-iterative algorithm used for feature extraction. This method is applied on the IRIS Dataset for non-linear feature extraction and clustering using k-means, Self Organizing Maps(Kohonen Network) and EM Algorithm