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Bringing an AI Product to Market

O'Reilly on Data

Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process. Some of the best lessons are captured in Ron Kohavi, Diane Tang, and Ya Xu’s book: Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing.

Marketing 362
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Artificial Intelligence: Implications On Marketing, Analytics, And You

Occam's Razor

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works. All sans human-control.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.

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Top 10 IT & Technology Buzzwords You Won’t Be Able To Avoid In 2020

datapine

AI refers to the autonomous intelligent behavior of software or machines that have a human-like ability to make decisions and to improve over time by learning from experience. Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. Voice-as-User Interface (VUI).

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

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Data Science at The New York Times

Domino Data Lab

Here is a picture of The New York Times on its birthday in 1851, and for the vast majority of its lifespan this is pretty much what the user experience of interacting with The New York Times looks like. Editors can interact with this bot. You may wonder where would you put a data science group in The New York Times. We crowdsourced it.