Click to learn more about author Paolo Tamagnini. Welcome to the seventh episode of our Guided Labeling Blog Series. In the last six episodes, we have covered active learning and weak supervision theory. Today, we would like to present a practical example of implementing weak supervision via guided analytics based on a Workflow. The other […]
A Beginner’s Guide to Using SQL for Data Science
With the increasing hype of trends like AI and digital transformation, companies have become data-driven. They have started relying on data and analytics instead of gut feelings, and Data Science has emerged as a lucrative profession. There are 2.72 million job openings for data scientists at present, and this demand will only go higher. If […]
Data Science and Social Visibility: An Emerging Tech Revolution for 2020
Click to learn more about author Pratip Biswas. Costumes, trick-or-treating, “Fire burn and cauldron bubble,” the “Ghostbusters” movie — this year’s Halloween was eerie, as usual! While the origin of the day is rooted elsewhere, these days, it’s all about getting socially visualized! So, on this holiday, when you sipped your pumpkin latte or vampire […]
Guided Labeling Episode 6: Comparing Active Learning with Weak Supervision
Click to learn more about author Paolo Tamagnini. Welcome to the sixth episode of our Guided Labeling Blog Series. In the last episode, we made an analogy with a number of “friends” labeling “movies” with three different outcomes: “good movie” (?), “not seen movie” ( – ), “bad movie” (?). We have seen how we can train a […]
How Storytelling Makes for More Effective Data Comprehension
Click to learn more about author Bernard Brode. Once upon a time, humans communicated meaning through stories. Epic tales of adventure and intrigue, memorable myths of heroes and their trials and travails — these were the parameters around which people framed their individual lives. Today, it’s all about the numbers. From business to advertising and […]
Guided Labeling Episode 5: Blending Knowledge with Weak Supervision
Click to learn more about author Paolo Tamagnini. Welcome to the fifth episode of our Guided Labeling Blog Series.In the last four episodes, we introduced Active Learning and a practical example with body mass index data, which shows how to perform active learning sampling via the technique “exploration vs exploitation”. This technique employs label density and model uncertainty […]
From Modeling to Scoring: Correcting Predicted Class Probabilities in Imbalanced Datasets
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Alfredo Roccato. This is the second part of a the From Modeling to Scoring Series, see Part One here. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model? Model evaluation is an important part […]
How AI Is Revolutionizing Social Visibility
Click to learn more about author Ashok Sharma. Artificial intelligence has the power to revolutionize the social visibility of brands, making way for a very inclusive approach towards online marketing. Today, the power of digital marketing and artificial intelligence go hand in hand. Artifical intelligence (AI) in digital marketing is useful in gathering data from all […]
Guided Visualization and Guided Exploration
Click to learn more about co-author Scott Fincher. Click to learn more about co-author Paolo Tamagnini. Click to learn more about co-author Maarit Widmann. No matter if we are experienced data scientists or business analysts, one of our daily routines is the easy and smooth extraction of the relevant information from our data regardless of […]
What Are GPUs and Why Do Data Scientists Love Them?
Click to learn more about author Eva Murray. Move over, CPUs. The GPUs have arrived in modern enterprises, and data scientists are eager to use them for their modeling and deep learning applications. Why is this happening, and what are the advantages GPUs bring for Data Science applications? Read on and find out. What Are GPUs? GPUs, or graphics […]