Significant growth of Python

According to Stack Overflow, Python is fastest-growing major programming language
12 September 2017   928

Python is one of the most popular programming language in the world. And recently new proof of this statement appeared. Stack Overflow, one of the most popular resource for all kind of coders, published the metrics, demonstrating the giant growth of Python's popularity.

We can see on Stack Overflow Trends that Python has been growing rapidly in the last few years. But let's focus on high-income countries, and consider visits to questions rather than questions asked (this tends to give similar results, but has less month-by-month noise, especially for smaller tags).

Stack Overflow Trends
Stack Overflow Trends

Data on Stack Overflow question views going back to late 2011, and in this time period Stack team considered the growth of Python relative to five other major programming languages. These are currently six of the ten most-visited Stack Overflow tags in high-income countries; CSS, HTML, Android, and JQuery aren't included.

Growth of major programming languages
Growth of major programming languages

In June, Python became the most visited tag on the developer community site for the first time among high income countries, including hitting top spot in the US and UK and being in the top two almost everywhere else, behind either Java or JavaScript.

Students to Beat Google’s Machine-Learning Code

Student programmers' image classification algorithm successfully identifies the object in 93% of cases
13 August 2018   401

Developers-students from Fast.ai which organize free online computer training courses have created an image classification algorithm that successfully identifies the object in 93% of cases and copes with it faster than a similar Google algorithm with a similar configuration. The authors argue that "the creation of breakthrough technologies is not just for big companies". This is reported by MIT Technology Review.

When evaluating performance, the DAWNBench test was used, which calculates the speed and cost of teaching the neural network. During the Fast.ai experiment, the neural network was launched on 16 virtual AWS nodes, each contained 8 NVIDIA V100 graphics cards. This configuration achieved accuracy of 93% in 18 minutes, and the cost of machine time was estimated at $ 40. The result of Fast.ai is faster than the development of Google engineers by 40%, but the corporation uses its own clusters TPU Pod, so the comparison is not entirely objective.

The developers used the PyTorch Python library, as well as their own development - fastai. They were able to achieve this learning speed with the new method of cropping images from the ImageNet dataset: instead of square pictures, they began to use rectangular:

Fast AI
Fast AI

State-of-the-art results are not the exclusive domain of big companies. These are the obvious, dumb things that many researchers wouldn’t even think to do.
 

Jeremy Howard

Founder, Fast.AI

The authors tried to make the project accessible to everyone, so they simplified its infrastructure, refusing to use distributed computing systems and containers. To implement it, developers teamed up with engineers from the innovative division of the Pentagon (DIU) to release software to quickly create and support distributed models on AWS.