2017 was a special year for Deep Learning. In addition to the great experimental results obtained thanks to the algorithms developed, the Deep Learning this year has seen its glory in the release of many frameworks. These are very useful tools for developing numerous projects. In the article you will see an overview of many new frameworks that have been proposed as excellent tools for the development of Deep Learning projects.
Before starting with this article, I suggest you read the article Hexagonal binning, in which is explained and shown this method of aggregation. The article compares the scatterplots generated by two different sets of data. It is thus highlighted that for particular sets of data, especially those that are presented as a distribution "sparse" on the XY plane, it can be difficult to detect any clusters or linear trends.