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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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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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Modern workplaces need character and challenge to attract the best talent. It’s a deceptively simple truth. In the tech sector in particular, employees have high expectations. They aren’t just looking for a job. They’re searching for work that will let them pour their passion into a common goal and put real effort into overcoming challenges. They’re searching for something greater than the sum of its parts, something they will be able to build together with other, similarly ambitious folks. Not so simple, providing that kind of environment. In this article, I’ll list the 9 most important features of a workplace that attracts and motivates people. If you see these 9 characteristics in a potential employer, you can be sure they put real effort into making their team happy.
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Top 3 Machine Learning Trends in 2018

Machine Learning has been growing at an incredible pace in 2017. We’ve witnessed many breakthroughs such as AlphaGo beating the best world’s Go players, the comeback of evolution algorithms meta learning, or WaveNet generating speech that mimics any human voice. Deep Learning research has been skyrocketing since 2012, and it’s not going to stop. Carlos Perez - Co-Founder of Intuition Machine - predicts that the number of research papers related to Deep Learning will triple or even quadruple in 2018!
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