Production-grade Python packages for reproducible ML research, edge deployment, and computer vision. All available on PyPI with MIT licensing.
MIT License · Python ≥3.9
Ensemble Deep Random Vector Functional Link with skip connections. Delivers deep-ML–level accuracy without GPU training using closed-form layer solves and feature reuse. Targets ultra-fast training and inference on CPU and embedded boards (Jetson, Raspberry Pi).
pip install ed-rvfl-sc
PyTorch · Temporal CNNs / LSTM / Transformer
Preprocessing and dataset tooling for time-series forecasting experiments. Provides sliding windows, normalization flows, reproducible train/val/test slicing, and ready-to-train tensors for LSTM, TCN, Transformer, and iTransformer-style models. Designed for fast ablation studies and reproducible research.
pip install timemesh
TensorFlow · Vision Transformer · Focal Loss
Transformer-based Swin-UNet segmentation stack for earth observation, medical imaging, and industrial perception. Includes attention backbones, skip connections, and robust Focal Loss settings for rare-structure segmentation. Clean Keras API compatible with TensorFlow.
pip install keras-swin-unet