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Machine Learning Is Cybersecurity’s Answer to Detecting Advanced Breaches

In this special guest feature, Mike Paquette, VP of Products at Prelert, highlights how forward-thinking organizations see machine learning as an absolutely essential component to detecting today’s advanced cybersecurity threats early.

insideBIGDATA Guide to Scientific Research

In this new insideBIGDATA Guide to Scientific Research, the goal is to provide a road map for scientific researchers wishing to capitalize on the rapid growth of big data technology for collecting, transforming, analyzing, and visualizing large scientific data sets.

Advanced Data Science for Healthcare Scheduling Optimization

LeanTaaS iQueue is the flagship product of LeanTaaS. It applies advanced data science and machine learning to overcome healthcare scheduling complexity by optimizing the utilization of scarce resources in order to improve patient flow.

Machine Learning: Hottest Tech Trend in the Next 3-5 Years?

The featured talk focused on – by leveraging big data to allow computers to develop evolving behaviors, machine learning is vastly improving pattern recognition, allowing for broad application such as improved facial and speech recognition for application in many industries, especially national security.

Loop AI Labs Cognitive Computing Platform

The talk below by CTO Bart Peintner of Loop AI Labs was presented at the Deep Learning Summit in Boston on May 26, 2015 and coincides with the launch of the Loop Cognitive Computing Platform.

IBM’s Machine Learning Technology Accepted as Apache Open Source Project

IBM (NYSE: IBM) today announced that its machine learning technology –SystemML –has been accepted as a project by the Apache Incubator open source project. Originally developed by IBM Research, and now used in IBM’s BigInsights data analytics platform, SystemML is a machine learning algorithm translator.

Spark MLlib: Making Practical Machine Learning Easy and Scalable

In this talk, Xiangrui Meng of Databricks shares his experience in developing MLlib. The talk covers both higher-level APIs, ML pipelines, that make MLlib easy to use, as well as lower-level optimizations that make MLlib scale to massive data sets.

Book Review: Doing Math with Python

When one of my favorite independent tech book publishers, No Starch Press, notified me about their new title “Doing Math with Python,” I was energized to review what potentially could be a good new resource for budding data scientists.

Argyle Data Outlines Five Key 2016 Predictions for Native Hadoop Applications

Vikash Varma, President and CEO of Argyle Data, a leader in native Hadoop applications for threat analytics in mobile communications, has provided a perspective on the company’s momentum and industry outlook for 2016. Varma’s predictions for substantially accelerated growth are fueled by the growing need for data-driven applications and fraud analytics for the mobile communications industry that run natively on Hadoop.

Andrew Ng Talks Deep Learning at Bay Learn 2015

In this short presentation, Dr. Andrew Ng (world renowned deep learning luminary, Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty) talks about what’s going on with deep learning and how it is rapidly changing the problem domains that can be addressed with machine learning. In particular, Ng announces a deep learning […]