Deep Learning Analytics

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Artificial intelligence has entered everything – from autonomous cars to automatic emails to smart homes. You seem to get any merchandise (such as medical health, flight, travel, etc.) and make it smarter through the special application of artificial intelligence.

Machine learning is a multi-disciplinary subject that has emerged in the past 20 years and involves many disciplines such as probability theory, statistics, approximation theory, convex analysis, and computational complexity theory.

Machine learning engineers are part of the team that develops products and builds algorithms and ensures that they work reliably, quickly, and on a scale.

Machine learning is almost ubiquitous, and even if we don't call them, they often appear in large data applications. I used to describe some typical big data use cases in my blog. In other words, these applications can provide the best results in "extreme situations". 　　At the end, I also mentioned the combination of byte-level data capacity, real-time data speed, and/or diversity of multiple structured data. I also listed a list of applications that deliberately avoided "machine learning analysis" during the collection process. The main reason is that while in these use cases machine learning is not primarily ...

Ai technology, known for its machine learning, is now at a white-hot stage, as we have mentioned many times before. The technology is driving the development of computer vision, language recognition, and text analysis technologies for companies such as Google, Facebook, Microsoft and Baidu, and has become the technology base for many start-ups (some of which have been acquired before the product is released). With the development of machine learning, these successes have received a lot of media attention. But what you're seeing is probably just a superficial phenomenon. Many studies are taking place in those non-large networks ...

Today, big data technologies, especially big data analytics, have evolved into an important part of most corporate strategies, and companies are under intense pressure to keep up with the rapid growth of big data.

The term “big data” has been on fire for several years. In the last one or two years, the limelight seems to have been taken away by the concepts of artificial intelligence and deep learning, and has gradually become a “out of gas” technical vocabulary.

The big data field has boomed in recent years. As a segment of big data, education big data has great potential to promote educational change. Big data technology is rapidly evolving, which opens up new possibilities for big data applications.