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Machine Learning algorithms are being used more often than we can imagine and there is a good reason for that. Let's see what kind of different Machine Learning algorithms exist and how they can help us in solving everyday life problems.
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As organizations strive to make the most out of the data they collect, they turn to different techniques for generating insights from vast data sets. The market for data science and analytics is on the rise – according to IMB, the number of jobs for all US data professionals will increase by 364,000 openings to reach smashing 2,720,000 by 2020!
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One of the major European banks has recently admitted that it has failed to spot money laundering in its books for years. As a result, the bank has agreed to pay $900 million in a settlement with prosecutors. While it was a man (the CFO) who stepped down for this negligence, would a machine (Machine Learning) be a better guardian to prevent such situations in the future?
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Machine learning is undoubtedly one of the hottest topics in software development right now. And for good reason. Machine learning opens up a whole world of new possibilities for developers, exciting app owners and end users alike. From greater personalisation to smarter recommendations, improved search functions, intelligent assistants, and applications that can see, hear, and react – machine learning can improve an app and the experience of using it in all manner of ways.
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For a start, lawyers are nowhere near the top of the list of the jobs most likely to be automated in the nearest future. The popular site willrobotstakemyjob.com estimates the chance to be as low as 4%, which is notably low when compared to the chances of the same happening to other professionals.
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10,923 analysed photos, 10 weeks, and hard work of design and development teams, topped with AR engines and Machine Learning technology. That's our recipe for building a precise algorithm that will detect 5 car models using your smartphone’s camera. Here's more on our journey to create a fun tool with cutting-edge solutions.
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Since many businesses want to make use of AI in order to scale up or take their start-up off the ground, it is crucial to realize one thing: the technology they choose to work with must be paired with an adequate deep learning framework, especially because each framework serves a different purpose. Finding that perfect fit is essential in terms of smooth and fast business development, as well as successful deployment.
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Machine learning is one of the hottest topics in modern software development. Still, it is a fairly young technology, which is undergoing rapid change and development. We wrote this article to aid you in selecting the right flavor of ML for your project, showcasing the top frameworks along with their upsides and shortcomings.
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Python has enjoyed a steady rise to fame over recent years and is now jostling for the position of one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.
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The new surge in Artificial Intelligence development has a strong impact on the InfoSec sector. However, there is much more nuance to it than you may think. Machine learning algorithms are not ready to take over on both sides of the fence. They are just a powerful tool that both security specialists and their adversaries use in their arms race.
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