Right now Deep Learning is taking over the world. Self-driving cars, composing music, replicating an art — what seemed like magic yesterday is nothing special today. And you don’t have to be a wizard or math Ph.D to create such things. Do you have a graphic card? You’re good.
One of the main reasons why Deep Learning is not a rocket science anymore are all those frameworks. Tensorflow, Caffe, Keras — you can build complex networks as if playing with LEGO pieces.
But not a fun part of it is data, people tend to forget about it. It’s like a fuel for machine learning and without data algorithms mean nothing. There are lots of cool libraries for building neural networks, but not so many tools for data preparation.
Large businesses are able to collect large amounts of data and invest money in annotation on services like Amazon Mechanical Turk. But what small companies and researches can do?
Here at Deep Systems we create AI solutions for various business sectors and face challenges everyday. To apply state-of-the-art models we first need to collect data, combine it with open-source datasets, annotate and prepare it for training. Usually it takes most of our time! 😫
To make things easier we had to come up with a solution. We wanted to create a place where you can go through every step of dataset preparation: from data to neural network model.
So how exactly Supervise.ly can help our fellow data scientists?
To make things even easier we have started a series of tutorials of how to train modern neural networks on open-source datasets like Cityscapes