TIOBE April 2019 to be Available

Top three are Java, C and C++, Python was pushed on the 4th place
11 April 2019   776

In April, C ++ pressed Python out of the top three and sent it on the fourth line. Experts say the reason is not a drop in interest in Python. On the contrary, from month to month interest in it is growing. Also, the popularity of C ++ is growing.

TIOBE Programming Community Index April 2019
TIOBE Programming Community Index April 2019

TIOBE experts recalled that once C ++ market share exceeded 15%. Difficulties with the release of new versions of the standard language provoked a drop in interest in C ++ and a reduction in this share. With the release of C ++ 11, C ++ 14 and C ++ 17, and most importantly, with their support by the main compilers, the popularity of the language began to revive.

TIOBE April 2019
TIOBE April 2019

The TIOBE ranking is compiled monthly based on the analysis of search queries in Google, Bing, Yahoo !, Wikipedia, Amazon, YouTube and Baidu. It reflects the popularity of programming languages, but not their quality.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   170

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.