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TLDR — Raven Stayed Heads Down Building in Q3 2019

Raven’s Q3 2019 Update

Our new website launched as we move a bit out of stealth. We’ve announced the first alpha customer, Mate Labs, who trains thousands of models per day.


— First alpha customer adopting the RAVEN token and our protocol
— Built a repository for development with engineers from various parts of the globe. Taking measures to safeguard our technology and distribution approaches.
— Development of functions such as Back-Propagation (Back-prop) and several activation functions that are used to train vanilla Neural Network models have been completed.

Diving into the execution of all development goals

As stated in our roadmap, the Q3 theme was development. We’ve come a long way since the prototype. Because we’re building a framework from the ground up, development takes a very long time. Today marks a huge milestone and shows we have taken major strides in achieving adoption for the RAVEN token and our deep learning training technology. From a development standpoint, we have completed the Q3 development goals and are on track.

We are currently developing the framework right now and have built a repository for development with engineers from various parts of the globe. It’s just not public. We have collectively taken the decision to not open source it to safeguard our technology and approaches. Raven has a competitive advantage and a unique approach to distribution. We want to maintain this for some time and will open source select parts of our technology.

While having said, we are excited to announce that machine learning company, Mate Labs, has agreed to adopt RAVEN tokens into their platform. They have volunteered to become our first Alpha customer who will actually test the robustness and efficacy of our technology. Mate Labs is a horizontal AI startup which trains thousands of models on a daily basis. This is a huge win because it is the best test bed for us.

Testing the framework will be an exciting time as we have completed a first cut of various functions. The development of functions such as Back-Propagation (Back-prop) and several activation functions that are used to train vanilla Neural Network models have been completed. In our alpha test with Mate Labs, they will be using these functions to train on structured data to develop models for classification and regression.

Community Updates

Interview with Roy Blackstone on the Fill Before Shill show:

Interview on The Digital Frontier Corp:

Our Knights Themed Contest:

Raven joining Binance’s V Label Transparency Initiative:

Proof of Steak: Raven Staking Program:

AMA on the Atomic Wallet TG:

AMA on the Crypto Calibur TG:

AMA on Crypto Daku TG:

Ama on Gagarin Talk Russian TG:

Raven in the #2 Spot of Top Altcoin Picks by @MartiniGuyYT:

Binance Sharing Raven’s Reasons for Choosing Binance Chain:

Community Going Strong

Image Credit: Christian Ott

Closing Remarks as we Look Ahead to Q4 2019

We are entering a period of builders again, just like 2018. To build a company of lasting value, the tech will need to have real adoption and real use cases. This only comes with building strong tech that solves a real problem. That is exactly why we are so well positioned. The Raven team comes from the traditional startup world where product-market fit matters as much as speed and execution.

Thank you to everyone who has supported us on this journey. Now let’s take the next steps towards massive adoption.


Rahul and Sherman

Rahul and Sherman coding away at CoWrks in Bangalore, India. Really great space for working. Endless coffee and cucumber water :)

Want to talk to the founders of Raven?
Drop us an email: founders@ravenprotocol.com

Join us on Telegram: t.me/ravenprotocol
Join Official Announcements: t.me/raven_announcements
Follow us on Twitter: twitter.com/raven_protocol
Get RAVEN on Binance DEX: binance.org/en/trade/RAVEN-F66_BNB




Decentralized and Distributed Deep Learning

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Raven Protocol

Raven Protocol

www.RavenProtocol.com is a decentralized and distributed deep-learning training protocol. Providing cost-efficient and faster training of deep neural networks.

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