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What’s in a Name? (aka Data Modeling What?)

Dataversity

What Are You Talking About? The post What’s in a Name? aka Data Modeling What?) Click to learn more about author Thomas Frisendal. This is a summer special, on the lighter side, but addressing a simply overwhelming issue at times. Sometimes the obvious is not that … obvious.

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Action Detection Models— what are they and how can I create one using MVI?

IBM Data Science in Practice

In this article, we will explore Maximo Visual Inspection (MVI), learn about action detection, discuss some real world use cases and do a walkthrough demonstration of how to create your own model in MVI. What is Maximo Visual Inspection? What is Action Detection?

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LEGO, the Future of TikTok, and A Problem for Your Kids?

Towards AI

AI and Control Top highlight ByteDance has announced a new model named LEGO that not only has impressive capabilities, but also gives us a clear idea of the future of its products, including the app your kids are glued to right now: TikTok. And you might not like what this future looks like. From research to projects and ideas.

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NLP, Tools and Technologies and Career Opportunities

Women in Big Data

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. Currently based in Germany, she possesses extensive experience in developing data-intensive applications leveraging NLP, data science, and data analytics.

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Constructing and Visualizing Datagrids in Kangas

Heartbeat

It’s defined as a tool for exploring, analyzing, and visualizing large-scale multimedia data. It’s defined as a tool for exploring, analyzing, and visualizing large-scale multimedia data. Kangas DataGrid, the fundamental class for representing datasets, can easily store millions of rows of data. Any data, any environment.

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phData Toolkit November 2023 Update

phData

We have purposely built this tool based on projects with our customers and feedback from our staff and customers, and it aims to accelerate your data journey. This includes a variety of sources such as Teradata, Oracle, Hive, and Microsoft SQL Server (to name a few). Hello, and welcome to our November update of our Toolkit blog series!

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Feature Selection Techniques in Machine Learning

Mlearning.ai

Photo by Arseny Togulev on Unsplash If you’re working with a dataset and trying to build a machine learning model, you probably don’t need all the data and columns that feed into your model. Using unnecessary data can confuse the model and lead to undesirable results like overfitting or underfitting.