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IBM Watson Analytics – Will it Replace Data Scientists ?
We review IBM Watson Analytics Beta version, the service which aims to provide an automated data scientist and intended for business users who want to move beyond spreadsheets for analysis .
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Will Deep Learning take over Machine Learning, make other algorithms obsolete?
Will deep learning will take over machine learning and make other algorithms obsolete, or is it too complex to use on simpler problems? We look at both sides of this discussion.
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Big Data on the Internet of Things
ParStream unveils the first analytics platform purpose-built for the speed and scale of the Internet of Things (IoT).
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GDELT: Big Data of News, Conflicts, and Society
What is happening in this world today? Obviously, it is impossible for us to read and analyze billions of news reports published every day. GDELT is designed to record, analyze, visualize and even forecast our planet.
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CuDNN – A new library for Deep Learning
Becoming more and more popular, deep learning is proved to be useful in artificial intelligence. Last week, NVIDIA’s new library for deep neural networks, cuDNN, has attracted much attention.
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What is Big Data – answers from thought leaders
What do you think when you hear “big data”? Maybe take a moment and think about this, before you let all the opinions below influence you.
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CoLaboratory: Doing Data Science Collaboratively Like Google Doc
A review of CoLaboratory which enables data scientists to write code like on Google Doc.
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Sibyl: Google’s system for Large Scale Machine Learning
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
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IBM Watson’s Next Step: Partnership with Universities
Students from ten top tech universities now have access to Watson. For students at New York University interested in Watson, Capstone Project Course would be their first choice.
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OpenML: Share, Discover and Do Machine Learning
OpenML is designed to share, organize and reuse data, code and experiments, so that scientists can make discoveries more efficiently. It is an interesting idea to build a network of machine learning.
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Ran Bi received MS in Data Science from New York University, where she worked on machine learning, deep learning and big data analytics. She is currently a Data Scientist at AIG.
