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/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.
ajayarunachalam/pynmsnn
NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch
ajayarunachalam/AJAY-sagemaker-examples
Amazon SageMaker Examples
ajayarunachalam/amazon-personalize-pinpoint-contextual-targeting
ajayarunachalam/awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
ajayarunachalam/aws-ai-intelligent-document-processing
A set of Jupyter notebooks that describes different phases of an Intelligent Document Processing with AWS AI Services
ajayarunachalam/causalml
Uplift modeling and causal inference with machine learning algorithms
ajayarunachalam/ChatGPT
🔮 ChatGPT Desktop Application (Mac, Windows and Linux)
ajayarunachalam/Customer-Survival-Analysis-and-Churn-Prediction
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
ajayarunachalam/customer_segmentation
An end-to-end project on customer segmentation
ajayarunachalam/data-science-template
Template for a data science project
ajayarunachalam/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
ajayarunachalam/EconML
This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
ajayarunachalam/Failed-ML
Compilation of high-profile real-world examples of failed machine learning projects
ajayarunachalam/forward-forward-algorithm
Plug and play modules to boost the performances of your AI systems 🚀
ajayarunachalam/huggingface-notebooks
Notebooks using the Hugging Face libraries 🤗
ajayarunachalam/human-learn
Natural Intelligence is still a pretty good idea.
ajayarunachalam/hyperlib
Library that contains implementations of machine learning components in the hyperbolic space
ajayarunachalam/influenza_transformer
PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"
ajayarunachalam/langchain
⚡ Building applications with LLMs through composability ⚡
ajayarunachalam/load-testing-sagemaker-endpoints
ajayarunachalam/Mathematics-for-ML
🧮 A collection of resources to learn mathematics for machine learning
ajayarunachalam/mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
ajayarunachalam/openai-cookbook
Examples and guides for using the OpenAI API
ajayarunachalam/PaLM-rlhf-pytorch
Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
ajayarunachalam/pythoncode-tutorials
The Python Code Tutorials
ajayarunachalam/SageMaker-Deployment
Compilation of examples of SageMaker inference options and other features.
ajayarunachalam/streamlit-apps
Sample streamlit applications
ajayarunachalam/streamlit-demo-ML-app
ajayarunachalam/Streamlit_Multipage_AWSCognito_User_Authentication_Authorization