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.

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.

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.

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)?

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.

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!

Let’s say that you’re at the very beginning of developing your app. Equipped with a solid business plan, you decide to go with Python, and you’re betting on the Django framework, ready to make the first decisions that will take this project from concept to reality. One of the most fundamental things to do at this stage is to find a Django-friendly hosting service.

Machine Learning (ML) and Deep Learning (DL) are two data science terms that have become hot buzzwords in tech, business, and marketing. In fact, ML and DL are two different terms often confused for the same thing. Here’s the difference: Deep Learning is a part of Machine Learning, an advanced technique of teaching algorithms to take independent decisions, which is a part of a broader field of Artificial Intelligence.

Many things in the digital world are born out of necessity. That is, we need to find a way to do something in a smoother, more efficient manner in order to create something of higher value.

The power and potential of machine learning (ML) to bring huge benefits and new capabilities to mobile and web applications is remarkable. Today, more and more businesses and app developers are opting to build machine learning into their products to the extent that this once avant-garde technology is now becoming decidedly mainstream.