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June 3 · Issue #17 · View online
A weekly digest of all the best Data Science related news and blog posts.
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Welcome to this week’s installment of my curated list of all the best data science related news and blog posts. If you appreciate the curated content I send out, please consider buying me a coffee here☕. I’m sure this week content will bowl you over 🥣🏏🎯. – Luis de Sousa
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Sunday vibes!
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Data Points: Visualization That Means Something - Nathan Yau
“Whether it’s statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own.” Using examples from art, design, business, statistics, cartography, and online media, this book explores both standard-and not so standard-concepts and ideas about illustrating data.
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Resonate: Present Visual Stories that Transform Audiences - Nancy Duarte
“Presentations are meant to inform, inspire, and persuade audiences. So why then do so many audiences leave feeling like they’ve wasted their time?” “Resonate helps you make a strong connection with your audience and lead them to purposeful action. The author’s approach is simple: building a presentation today is a bit like writing a documentary. Using this approach, you’ll convey your content with passion, persuasion, and impact.”
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Announcing the R Consortium ISC Funded Project grant recipients for Spring 2018
The R Consortium has just announced its latest round of project grants. The link above contains details of each project selected.
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How to Use FPGAs for Deep Learning Inference to Perform Land Cover Mapping on Terabytes of Aerial Images
Easily deploy models to FPGAs for ultra-low latency with Azure Machine Learning powered by Project Brainwave Images were scored with FPGAs at a rate of 305 000 images/s.
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Build Your Own Image Similarity Model – Microsoft Machine Learning Server
“The Image Similarity with SQL Server solution on the Azure AI Gallery demonstrates how to apply transfer learning, incorporating a pre-trained deep neural network (DNN) model (trained on ImageNet) to the problem of finding images that are similar to a target image.”
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How to operationalize Keras models in Microsoft Machine Learning Server
“Microsoft Machine Learning Server’s operationalization feature enables data scientists to operationalize their R and Python analytics. In this blog, we will see how to operationalize Keras models as web services in R and Python.”
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StatCheck the Game
StatCheck the board game where the object is to publish two papers before any of your opponents.
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Reflections on the ROpenSci Unconference
It’s amazing what you can achieve when you get 65 inspired people together. Some thoughts by David Smith the rOpenSci unconference
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Recap: Microsoft at PyCon US 2018
A great recap of Microsoft at PyCon2018
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PyCon 2018
Recordings of PyCon Cleveland 2018 have been released.
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We Taught an AI To Synthesize Materials
The paper “Gaussian Material Synthesis” and its source code is available here.
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AI-Based Large-Scale Texture Synthesis
The paper “Non-stationary Texture Synthesis by Adversarial Expansion” and its source code is available here.
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Optimizing Barnes-Hut t-SNE
Dive into the research to see how Microsoft’s Tavian Barnes is speeding up the Barnes-Hut algorithm used by the t-SNE dimensionality reduction technique.
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Thanks for reading. As always, let us know if you have any questions or suggestions, just hit reply, click the thumbs up or thumbs down icons below or send us an email at luisd@syeop.co.za. xoxo
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Johannesburg, South Africa, 2020
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