AI with Pyramids of Self Programmable Gates
Guest blog post by David Enríquez Arriano. For more information or to get higher pictures resolution, contact the author (see contact information at the bottom of this a...
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An ML Framework
A machine learning solution can be broadly divided into 3 parts. A typical ML exercise would involve experimentation and iteration of all the 3 parts together and/or 1 of...
Data Translators: Those Who Ensure Analysis Is Not Lost In Rendition
A few days ago, while discussing with colleagues why many organizations were still fumbling at what’s typically called the “last mile” in data science, the conversa...
15 Amazing Infographics and Other Visual Tutorials
They come in various shapes: infographics, cheat sheets, periodic tables, one-picture articles, and even maps. They cover everything: IoT, AI, machine learning, data scie...
Technology has to follow your data strategy
Knowledge is power With IDC estimating that the data mountain has now reached five zettabytes, it is not a case of a business not having enough data to make business deci...
DSC Webinar Series: Data Viz: Painting a Truthful Point of View
We all know that Data does not lie. However, when we create visualizations we may at times “stretch the truth.” This is often done to help others realize the true sto...
DSC Webinar Series: Matei Zaharia’s Predictions for 2018: Big Data and AI Highlights
Over the past few years, AI and big data have powered numerous technologies that have changed the way we live, from autonomous cars to conversational systems to personali...
The one critical skill many data scientists are missing
This article was written by Emma Walker. Emma is a data scientist at Qriously. When I started to learn about data science and consider it as a career choice, there was a...
Paradox Regarding Random (Normal) Numbers
I am investigating if some numbers like Pi or SQRT(2) are normal in base 2 or 10, that is, whether any sequence of digits appear with the expected frequency in their deci...
Optimization techniques: Finding maxima and minima
In the last post, we talked about how to estimate the coefficients or weights of linear regression. We estimated weights which give the minimum error. Essentially it is a...