ajayarunachalam
AWS Certified Solution Architect; AWS Certified Machine Learning Specialist; Microsoft Certified Power BI Associate; Certified Scrum Master
United Kingdom
Pinned Repositories
Deep-Learning-Cryptocurrency
Predicting Cryptocurrency prices (Bitcoin & ZCOIN)
Deep_XF
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
EDA
Exploratory Data Analysis
gui-pandas-ai
GUIPandasAI - Integrating Generative AI capabilities into Pandas as Web Interface along with key-words based data analysis services
msda
Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector
Neighbor-Discovery
P2P Network Resource Discovery Simulation in MANET
pychatgpt_gui
A simple, ease-to-use python APP built for unleashing the power of GPT with custom-data and pre-trained inferences.
pynmsnn
NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch
RegressorMetricGraphPlot
Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (mape), etc.
vision-transformer-demo
Designing, Implementing & Deploying Transformer Deep Learning Network Architecture for computer vision tasks
ajayarunachalam's Repositories
ajayarunachalam/msda
Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector
ajayarunachalam/RegressorMetricGraphPlot
Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (mape), etc.
ajayarunachalam/pyss3
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
ajayarunachalam/retail-demo-store
AWS Retail Demo Store is a sample retail web application and workshop platform demonstrating how AWS infrastructure and services can be used to build compelling customer experiences for eCommerce, retail, and digital marketing use-cases
ajayarunachalam/xai
XAI - An eXplainability toolbox for machine learning
ajayarunachalam/amazon-forecast-samples
Notebooks and examples on how to onboard and use various features of Amazon Forecast.
ajayarunachalam/amazon-sagemaker-operator-for-k8s
Amazon SageMaker operator for Kubernetes
ajayarunachalam/amazon-sagemaker-secure-mlops
ajayarunachalam/appv1_docker
ajayarunachalam/awesome-mlops
:sunglasses: A curated list of awesome MLOps tools
ajayarunachalam/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
ajayarunachalam/aws-cdk
The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
ajayarunachalam/biobert_embedding
Token and Sentence level embeddings from BioBERT model
ajayarunachalam/GazeTracking
đź‘€ Eye Tracking library easily implementable to your projects
ajayarunachalam/great_expectations
Always know what to expect from your data.
ajayarunachalam/Hierarchical-Localization
Visual localization made easy with hloc
ajayarunachalam/improving-forecast-accuracy-with-machine-learning
The Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks—providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output.
ajayarunachalam/intermdiate_layer_matter_ssl
The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper.
ajayarunachalam/Latest-News-Classifier
Master in Data Science Final Project
ajayarunachalam/Natural-Language-Processing-with-AWS-AI-Services
Natural Language Processing with AWS AI Services, published by Packt
ajayarunachalam/oreilly_book
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker
ajayarunachalam/pylabel
Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.
ajayarunachalam/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
ajayarunachalam/sagemaker-video-examples
Repository for code examples for 2021 sagemaker launches
ajayarunachalam/serverless-application-model
AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications
ajayarunachalam/starrynight
An application to train, experiment with, and deploy real-time style transfer models
ajayarunachalam/terraform-aws-s3-bucket
Terraform module which creates S3 bucket resources on AWS
ajayarunachalam/text_classification
all kinds of text classification models and more with deep learning
ajayarunachalam/Text_Classification-1
Text Classification Algorithms: A Survey
ajayarunachalam/Transformer4Vision
Summarize Transformer-based papers for Computer Vision tasks. Mainly focus on object detection, segmentation, and few-shot learning. Keep update frequently.