Machine Learning Video Analytics

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 ...

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 and artificial intelligence are transforming businesses, brands and the industry as a whole. They have the ability to dramatically reduce labor costs, generate unexpected new ideas, and discover new models and create predictive models from raw data types.

Introduction: It is well known that R is unparalleled in solving statistical problems. But R is slow at data speeds up to 2G, creating a solution that runs distributed algorithms in conjunction with Hadoop, but is there a team that uses solutions like python + Hadoop? R Such origins in the statistical computer package and Hadoop combination will not be a problem? The answer from the king of Frank: Because they do not understand the characteristics of R and Hadoop application scenarios, just ...

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 ...

The author describes how to use OpenCV and similar tools for digital video analysis and methods to extend such analysis using cluster and distributed system design. In previous installments, a coprocessor designed specifically for video analysis and new OPENVX hardware acceleration was discussed, which can be applied to the computer Vision (CV) sample provided in this article. This new data-centric CV and video analysis technology requires system designers to rethink application software and system design to meet demanding requirements such as large, public facilities and infrastructure, and a more entertaining ...

As we all know, predictive analysis has always been the "prerogative" of statisticians and data scientists in the "Ivory Tower", and they are far from day-to-day business decision makers. Big data will change that. As more and more data streams are put online and integrated into existing BI, CRM, ERP, and other critical business systems, predictive analytics will eventually become a focus of attention. Although most customer service representatives and field sales representatives have not yet felt the impact, companies such as IBM and MicroStrategy have begun to move. Big Data: Predictive analysis is no longer the prerogative of statisticians to think ...

Humans have always been very curious about the concept of robotics and artificial intelligence (AI). Hollywood films and science fiction may have inspired some scientists to start working in this direction.

This article describes ways to perform large data analysis using the R language and similar tools, and to extend large data services in the cloud. In this paper, a kind of digital photo management which is a simple and large data service is analyzed in detail, and the key elements of searching, analyzing and machine learning are applied to the unstructured data. This article focuses on applications that use large data, explains the basic concepts behind large data analysis, and how to combine these concepts with business intelligence (BI) applications and parallel technologies, such as the computer Vision (CV) and ... as described in part 3rd of the Cloud Extensions series.