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Android has a whopping 85.9-percent share in the mobile OS market. Even if you’re an iPhone enthusiast yourself, these numbers can’t be argued with – Google’s operating system is an extremely important part of the modern technology landscape.
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React Native lets you build native mobile apps using only JavaScript. It uses the same design as React, letting you compose a rich mobile UI from declarative components. It basically allows you to reuse your code for writing apps for Android and iOS. “Write once, use everywhere” was one of the most popular slogans for this technology when it came out.
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Python is a programming language that has been around for quite a while now and has become very popular. However, it seemed like it wasn’t as much in the spotlight as some of the other languages, despite being used by Google. In reality, Python developers weren’t a hot commodity because the language itself wasn’t used widely as some of the others, such as JavaScript, Java, C, or C++.
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Two programming languages probably couldn’t be more different from each other than Python and PHP. The former is hailed for its simplicity and straightforwardness, while the latter is often criticized for its quirks and tendency to generate spaghetti-like codebases. Python brings to mind modern applications such as machine learning and data science, while PHP is mostly associated with old-timey web development and old-school discussion forums. Still, both remain popular and have their benefits and disadvantages. In this blogpost, we’ll try to present a fair comparison of Python and PHP, taking into account a number of factors which you should consider when choosing the language for your new software project.
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Is it possible to achieve the pole position on the market while having precisely zero funding from investors? Surely it is possible, as evidenced by the story of Patrick Campbell, founder and CEO of PriceIntelligently (now ProfitWell). Back in 2012, Campbell has founded a pricing analysis startup that helps SaaS companies figure out their pricing strategies.
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Fed up with the low level of banking services in France, two entrepreneurs decided to have a crack at business banking. Since the launch in 2016, the Qonto team has grown from the founding duo to 80 people. They have attracted over 20,000 small businesses to their bank and closed two funding rounds, pocketing about €12m.
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The age of generic services is dropping off. Today's customers expect more, and they want you to produce software tailored to their needs. This can be done with machine learning.
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7 Challenges for Machine Learning Projects

Although scientists, engineers, and business mavens agree we might have finally entered the golden age of artificial intelligence when planning a machine learning project you have to be ready to face much more obstacles than you think.
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It’s 2018, and we’ve gained a level of advancement in machine learning technology that no one expected this soon. Images made using algorithms don’t look artificial, retailers’ smart recommendations raise sales, and the stock market has forever been changed by predictions based on machine learning. It’s a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage as their business competitors leverage ML and leave everyone else in the dust.
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If you’ve been following the latest trends in technology, you’ve probably noticed that machine learning (ML) and deep learning (DL) are not just buzzwords anymore (see what is the difference between ML and DL). In fact, they’re responsible for some of the most impressive achievements in artificial intelligence so far, like the spotting of a distant eight-planet solar system or beating real humans at master-level chess. But are you also familiar with the concept of convolutional neural networks (CNNs or ConvNets), and how they contribute to AI development (see how does AI differ from ML)?
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