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Minor dl4j website updates #197

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7 changes: 2 additions & 5 deletions about.md
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## About Eclipse Deeplearning4j

Eclipse Deeplearning4j is an open-source, distributed deep-learning project in Java and Scala spearheaded by the people at [Konduit](https://konduit.ai/), a San Francisco-based business intelligence and enterprise software firm. We're a team of data scientists, deep-learning specialists, Java systems engineers and semi-sentient robots.
Eclipse Deeplearning4j is an open-source, is a suite of open source tools for training and deploying deep learning models supported by the team at [Konduit](https://konduit.ai/), a remote team distributed all over the world. For up to date statistics about who is involved with the project, please see the [eclipse page](https://projects.eclipse.org/projects/technology.deeplearning4j/who).

There are a lot of knobs to turn when you're training a distributed deep-learning network. We've done our best to explain them, so that Eclipse Deeplearning4j can serve as a DIY tool for Java, Scala and Clojure programmers working on Hadoop and other file systems.
With first commits in 2013 and donated to eclipse in 2017, dl4j has grown to a widely used tool for various use cases ranging from distributed training on spark to deployment in JVM micro service environments.

## Media

Deeplearning4j has been featured in [Wired](http://www.wired.com/2014/06/skymind-deep-learning/), [GigaOM](http://gigaom.com/2014/06/02/a-startup-called-skymind-launches-pushing-open-source-deep-learning/), [Businessweek](http://www.businessweek.com/articles/2014-06-03/teaching-smaller-companies-how-to-probe-deep-learning-on-their-own), [Venturebeat](http://venturebeat.com/2014/06/02/skymind-launches-with-open-source-plug-and-play-deep-learning-features-for-your-app/), [The Wall Street Journal](http://blogs.wsj.com/cio/2014/06/03/the-morning-download-apple-relies-on-ecosystem-for-innovation/), [Fusion](http://fusion.net/story/177825/privacy-conscious-siris-that-dont-give-up-your-secrets-are-coming/) and [Java Magazine](http://oraclejavamagazine-digital.com/javamagazine/may_june_2015?sub_id=DJ9kzXBnuXELe#pg58).

## Cite Eclipse Deeplearning4j

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With a versatile n-dimensional array class for Java and Scala, DL4J is Scalable on Hadoop, utlizes GPU support for scaling on AWS, includes a general vectorization tool for machine-learning libs, and most of all relies on ND4J: A matrix library much faster than Numpy and largely written in C++. We also built RL4J: Reinforcement Learning for Java with Deep Q learning and A3C.
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What's the use case for AI and machine learning?
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AI tools like Deeplearning4j can be applied to robotic process automation (RPA), Fraud detection, network intrusion detection, Recommender Systems (CRM, adtech, churn prevention), Regression and predictive analytics, Face/image recognition, Voice search, Speech-to-text (transcription), and preventative hardware monitoring (anomaly detection).
DL4j is a set of tools for running deep learning workloads. It includes a pytorch/tensorflow 2 like library called samediff. It also contains interop with main stream python tools via import from tensorflow and keras. This allows easy deployment in to enterprise environments. Our core c++ codebase libnd4j is also very small and easy to extend set of math operations compilable on a wide variety of architectures. Lastly, dl4j contains a very easy to use spark integration allowing for distributed training and batch inference over a cluster on both cpus and gpus.
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Is DL4J parallelized and multi-threaded?
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Deeplearning4j includes both a distributed, multi-threaded deep-learning framework and a normal single-threaded deep-learning framework. Training takes place in the cluster, which means it can process massive amounts of data quickly. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala, Clojure and Kotlin. Deeplearning4j's role as a modular component in an open stack makes it the first deep-learning framework adapted for a micro-service architecture.
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