• Using Multilevel Modeling for Secure and Effective Dam Operation and Floods Prediction

    Multilevel modeling

    Floods are among Earth’s most common and most destructive natural hazards. Floods occur when water overflows the land that is normally dry. Excessive rains can also lead to dams rapture or simple water overflow, which causes unwanted consequences for downstream settlements and unoptimized electricity production. Most floods take hours and even days to develop, giving…

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  • 6 Data Collection Rules for Your Future Perfect Machine Learning Dataset

    data collection process

    Modern companies produce gigantic amounts of data. Later it becomes a part of their machine learning datasets. Those are further used to build models that aim to solve various problems business may face, and make it more profitable, customer-oriented and, of course, data-driven. Machine Learning depends heavily on data, that makes algorithm training possible.  Regardless…

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  • AI at the Forefront of Digital Transformation Process in 2018

    Digital transformation definition Digital transformation has been a big topic for a few years now, and it has many definitions. From a business perspective, digital transformation is about leveraging digital technologies to improve processes, competencies, and business models. It is also about changing the culture of the company because it requires letting go of old…

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  • 5 Things you Must Consider to Maximize the Value of your Company’s Predictive Analytics and Machine Learning Initiatives

    predictive analytics and machine learning

    Investigating company data for insights is a well known and widely adopted practice. However, using predictive analytics and machine learning is the next frontier in data analysis. The ability to predict future outcomes is what sets predictive analytics apart from other analytics used today, such as descriptive analytics, which gives answer to the question “what…

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  • How to Design Better Machine Learning Systems with Machine Learning Canvas

    Design better machine learning systems wirh machine learning canvas

    Machine Learning Canvas is a template for designing and documenting machine learning systems. It has an advantage over a simple text document because the canvas addresses the key components of a machine learning system with simple blocks that are arranged based on their relevance to each other. This tool has become popular because it simplifies the visualization of a complex project and helps to start a structured conversation about it.

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  • 3 Criteria for a Successful Machine Learning Project

    A checklist for a successful machine learning project

    Many companies are taking advantage of the latest AI and machine learning technologies to create better products and services. If you consider joining their ranks, you are wise to do so. However, before diving into a new machine learning project you need to make sure that you have identified the best opportunity for your company….

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  • The Most Exciting Applications of Computer Vision across Industries

    Computer Vision Applications across Industries

    Computer vision has come a long way in terms of what it can do for different industries. Now that the technology has finally caught up the original ideas of computer vision pioneers from the 70s, we are seeing more exciting computer vision applications across different industries.

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  • How Visual Search Is Disrupting The Retail Industry

    Visual search is disrupting the retail industry

    E-commerce and retail industry have changed drastically in recent years. Young, tech-savvy shoppers are demanding smarter shopping experience where the journey from discovery of a product to check-out is as short as possible. To deliver such experience, companies are turning to AI for help.

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  • Using Data Science to Grow Your Business: 3 Key Areas to Consider

    Using data science to grow your business

    Data science can have incredible benefits for your business but it’s important to understand that it’s a solution to a problem, not a way to find the problem. It means that if your company has a lot of data that you don’t quite know what to do with, you need to figure out what you…

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