<p align="center"> <a href="" rel="noopener"> <img width=200px height=200px src="https://i.imgur.com/6wj0hh6.jpg" alt="Project logo"></a> </p> <h3 align="center">PROVEE - PROgressiVe Explainable Embeddings</h3> <div align="center"> []() [](https://github.com/kylelobo/The-Documentation-Compendium/issues)    [](https://github.com/kylelobo/The-Documentation-Compendium/issues) [](https://github.com/kylelobo/The-Documentation-Compendium/pulls) [](/LICENSE) </div> --- <p align="center"> Deep Neural Networks (DNNs), and their resulting **latent or embedding data spaces, are key to analyzing big data** in various domains such as vision, speech recognition, and natural language processing (NLP). However, embedding spaces are high-dimensional and abstract, thus not directly understandable. We aim to develop a software framework to visually explore and explain how embeddings relate to the actual data fed to the DNN. This enables both DNN developers and end-users to understand the currently black-box working of DNNs, leading to better-engineered networks, and explainable, transparent DNN systems whose behavior can be trusted by their end-users. Our central aim is to open DNN black-boxes, making complex data understandable for data science novices, and raising trust/transparency are core topics in VA and NLP research. PROVEE will advertise and apply VA in a wider scope with impact across sciences (medicine, engineering, biology, physics) where researchers use big data and deep learning. </p> ## 📝 Table of Contents - [Deployment Guide](#guide) - [Hardware](#hardware) - [Software](#software) ## 🧐 Deployment Guide <a name = "guide"></a> empty for now ## 🧰 Hardware Requirements <a name = "hardware"></a> empty for now ## 💾 Software Requirements <a name = "software"></a> empty for now