MindsEye

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Neural Networks in Java 8 with CuDNN and Aparapi

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# MindsEye - Java 8 Neural Networks

[![Sample Art created by MindsEye](https://raw.githubusercontent.com/SimiaCryptus/MindsEye/master/Mindseye_Textart.png)](https://photos.app.goo.gl/K8VWG5BpGV8Ufxn3A)
 
[![Sample Painting created by MindsEye](https://raw.githubusercontent.com/SimiaCryptus/MindsEye/master/0.gif)](https://photos.app.goo.gl/5SeREKdmYuIzMSv12)
 
Welcome! This is MindsEye, a neural network library written in Java 8. Low-level computation is provided by CuDNN and other native libraries.

## Links

**Developer Guide** - [docs.google.com](https://docs.google.com/document/d/15SzONTa9D5sR-3fehFPm_GWCQxhht0A7IjR_1_vIlk4/edit?usp=sharing) - Detailed Technical Documentation

**Art Gallery** - [photos.google](https://photos.app.goo.gl/5SeREKdmYuIzMSv12) - Curated artwork generated by MindsEye

**Component Documentation** - [simiacryptus.github.io/NetworkZoo/](https://simiacryptus.github.io/NetworkZoo/layers/index.html) - Test-generated documentation reports detailing each neural network component type.

**JavaDocs** - [simiacryptus.github.io/MindsEye/apidocs/](https://simiacryptus.github.io/MindsEye/apidocs/index.html) - API documentation for the current release

**Project Site** - [simiacryptus.github.io/MindsEye/](https://simiacryptus.github.io/MindsEye/) - The current maven-published site

**Maven** - [mvnrepository.com/artifact/com.simiacryptus/mindseye](http://mvnrepository.com/artifact/com.simiacryptus/mindseye) - Dependency import information

**Blog Articles** - [http://simiacryptus.com](http://blog.simiacryptus.com/search/label/MindsEye) - Related articles from the author

## Features

* Java - Use Java 8+, Scala, or any other JVM language.

* Support for multiple GPUs and high performance math using native libraries including CuDNN, CUDA, and OpenCL.

* A highly modular optimization engine - It can be configured for many popular algorithms, or customized for optimization research.

* A large and extensible component library with a focus on automated testing and documentation.

* Utilize server clusters with Apache Spark for data-parallel training.

* Built-in JSON serialization for all networks.

* Output Markdown or HTML Notebook-style reports for documentation and research.

* Directly regress input datasets - learning is not limited to network weights.

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