I am a machine learning enthusiast. I like to research more in Machine Learning related topics like Optimization, Deep Learning and CNN.
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autoencoders
May 18, 2020 – September 22, 2021
Autoencoders - a deep neural network used for feature extraction followed by clustering
View ProjectSelf-Organizing-Maps
March 25, 2020 – March 25, 2020
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
View ProjectSupervisedELM
August 15, 2019 – August 15, 2019
Implementation of Supervised Extreme Learning Machine for binary classification
View ProjectUnsupervised_Extreme_Learning_Machine
May 5, 2018 – July 8, 2018
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
View ProjectDeep-Learning-Programs
March 16, 2018 – March 16, 2018
All the Deep Learning Python Programs that I tried out using tflearn
View ProjectAssistive-Technology-Android-Application-for-Autism-Spectrum-Disorder-Therapy
February 25, 2018 – February 25, 2018
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.
View ProjectMachine-Learning-Presentations
February 25, 2018 – February 25, 2018
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
View ProjectDeep-Neural-Network-for-Clustering
February 25, 2018 – February 25, 2018
Autoencoders - a deep neural network was used for feature extraction followed by clustering of the "Cancer" dataset using k-means technique
View ProjectCultural Fit Analysis
The candidate's projects show a strong interest in academic and research-oriented machine learning topics. The 'Assistive Technology' project demonstrates a social impact orientation. However, the projects are heavily skewed towards Machine Learning/Deep Learning and Python, with only one project in Java (Android). The target role is 'Frontend Developer', which is not well-aligned with the current project portfolio. This indicates a potential mismatch in technical focus for a frontend role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work in teams (Assistive Technology project) and a focus on problem-solving through machine learning. However, without psychometric test results or interview data, a comprehensive assessment of soft skills and operational fit is not possible.