pandyaHomeLab
A personal learning platform showcasing Machine Learning and Deep Learning Models
— From Theory to Production: Learn & Develop on Synology NAS & Deploy to AWS.
Each module is a self-contained learning track with its own cloud backend, serving structured lessons, notebooks, and model demos.
Classical ML algorithms, feature engineering, model evaluation, and scikit-learn workflows. Foundational concepts through hands-on projects.
PyTorch · scikit-learn · Flask pandyahomelab.com/mlNeural networks, CNNs, RNNs, Transformers, and beyond. TensorFlow-powered experiments deployed to private cloud backends.
TensorFlow · FastAPI · Docker pandyahomelab.com/dlEach project is a containerized microservice — built on NAS, deployed to AWS. Click any live project to interact with the model API directly.
End-to-end regression pipeline with feature engineering, cross-validation, and a Flask API for real-time predictions.
Ensemble learning with decision trees — feature importance visualisation and model explainability via SHAP.
Convolutional neural network that classifies images into categories. Upload any image — get a prediction with confidence score instantly.
Long Short-Term Memory network for sequential data forecasting. Predicts trends from historical time-series inputs.
Fine-tuned transformer model for text classification and sentiment analysis using HuggingFace and FastAPI.
Real-time object detection API. Upload an image — get bounding boxes and class labels back as JSON or annotated image.
Each module covers core theory, practical implementation, and real deployment patterns.
Production-grade tools used across both learning modules.
