Deep learning is a computational technique that allows you to extract and transform data from sources such as human speech or image classification, using multiple layers of neural networks. Each of these layers takes its inputs from the previous layers and refines them, so progressively. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way the networks learn to perform specific tasks.
The progress of this last year regarding Deep Learning is truly exceptional. Many steps forward have been made in many fields of technology thanks to neural networks and among these there is the synthetic voice, or rather the Text-To-Speech (TTS) that is, that series of technologies able to simulate the human way of speaking by reading a text. Among the models realized, therefore, there is WaveNet, a highly innovative model that has revolutionized the way of doing Text-To-Speech making them jump really forward
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.
In recent years many new professions are emerging, some of which you probably barely know. These new professional activities will play an important role in the years to come. One of these figures is precisely the Data Scientist. In this article you will see in more detail what is the work of the Data Scientist, what should be his skills and what activities he must perform.
This article is linked to the article Hexagonal binning in which this method of aggregation of data is shown starting from a scatterplots representation and then ending with a representation that uses Hexagonal bins.
Scatterplots are a simple way to visualize the distribution of data on an XY plane, especially when we want to highlight the presence of clusters or particular trends.
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.