XplainNN
Demystifying Neural Networks with Explainable AI Libraries for the Internet of Things.
Our Mission
XplainNN is dedicated to demystifying the black box of artificial intelligence. We create open-source, user-friendly libraries that bring transparency and interpretability to complex neural networks.
Our core focus is developing robust libraries for Explainable AI (XAI), making model decisions understandable to humans.
Seamlessly integrate with Weights & Biases to track, visualize, and compare your model explanations and experiments.
We provide lightweight, efficient tools applicable to AI on the Internet of Things (AIoT), similar in scope to TensorFlow.
Featured Projects
Xplainnn Academy
A distraction-free EdTech platform with in-browser compilers (Python, MySQL) and Gemini AI-powered doubt solving — delivering a Coursera-like experience for free.
Visit ProjectXplainNN
A PyTorch-like deep learning library with custom layers, activations, and optimizers, built from scratch and open-sourced.
Visit ProjectXplainDB
A unified multi-model database engine supporting SQL, NoSQL, GraphQL, and Vector Search with custom storage and indexing in FastAPI/Python.
Visit ProjectAmnesiaDB
A modern alternative to Redis with web-native APIs, seamless Python integration, and advanced SQLite-powered querying.
Visit ProjectWe are actively researching new methods for generating more intuitive and accurate explanations for deep learning models, especially in computer vision.
Our team has published several papers in top-tier AI conferences.
We aim to tackle challenges in explaining reinforcement learning agents and time-series models for IoT applications.
