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Behrisch, M. (Michael)
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<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>
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## 🧰 Hardware Requirements <a name = "hardware"></a>
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## 💾 Software Requirements <a name = "software"></a>
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