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Reclaiming the stories that algorithms tell

O'Reilly on Data

Each of the classroom’s library books has a color coded sticker on its spine reflecting its Lexile score—a visual announcement of its official complexity level, and thus of which students might be officially ready to read it. This whole scoring system also changes the story about who librarians and teachers are.

Risk 355
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. ” “Data science” was first used as an independent discipline in 2001. Deep learning algorithms are neural networks modeled after the human brain.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. Chawla et al. Indeed, in the original paper Chawla et al. References. Banko, M., & Brill, E.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for There are models everywhere. In other words, #adulting. It’s a mess.

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Data Science, Past & Future

Domino Data Lab

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He also really informed a lot of the early thinking about data visualization. Key highlights from the session include. Transcript.

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Data Science at The New York Times

Domino Data Lab

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.