Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Click here to register and download your free 30-day trial of Intel Parallel Studio XE. We live in a world where humans rely more and more on computers to solve a variety of engineering problems―ranging from weather prediction to the discovery of lifesaving drugs. We are on the verge of another dramatic change where machines are capable of reaching and even exceeding humans in their ability to make decisions and solve complex problems. Computers have already beaten the best human players in Jeopardy* and Go*, and autonomous cars drive on the roads of California. This is all possible due to petaflop levels of compute power (thanks to Moore's Law) and the vast amounts of data available for training machine learning algorithms. At Intel, we work in close collaboration with our leading academic and industry fellow travelers to solve the hardware and software architectural challenges for Intel's upcoming multicore/manycore compute platforms. To help innovators tackle the complexities of machine learning, we are making performance optimizations available to developers through familiar Intel software tools, specifically through the Intel Data Analytics Acceleration Library (Intel DAAL) and enhancements to the Intel Math Kernel Library (Intel MKL).

Spending on cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. In its Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide IDC said the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1 per cent over the 2016-2020 forecast period. According to David Schubmehl, research director, Cognitive Systems and Content Analytics at IDC, "Software developers and end user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process" "Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organizations to be planning and undertaking strategies that incorporate these wide-ranging technologies," he said. Schubmehl said identifying, understanding, and acting on the use cases, technologies, and growth opportunities for cognitive/AI systems will be a differentiating factor for most enterprises and the digital disruption caused by these technologies will be significant. The ability to recognize and respond to data flows using algorithms and rule-based logic enables cognitive/AI systems to automate a broad range of functions across many industries.

Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver the main talk of the Spark & Machine Learning Meetup in Brussels, "Creating an end-to-end Recommender System with Apache Spark and Elasticsearch."

The recent explosion of interest in data science, data mining, and related disciplines has been mirrored by an explosion in book titles on these same topics. One of the best ways to decide which books could be useful for your career is to look at which books others are reading. This post details the 10 most popular titles in Amazon's Data Mining Books category as of Nov 10, 2016, skipping over repeated titles as well as titles which have been obviously miscategorized and are of no use to our readers. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in the chart below to see its documentation.

Pylearn2 is a library designed to make machine learning research easy. Its a library based on Theano NuPIC, 4392 commits, 60 contributors, www.github.com/numenta/nupic The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns.

Humans have been storing, retrieving, manipulating, and communicating information since the Sumerians in Mesopotamia developed writing in 3000 BCE. Since then, we have continuously developed more and more sophisticated means to communicate and push information. Whether unconsciously or consciously, we seem to always need more data, faster than ever. And with every technological breakthrough that comes along, we also have a set of new concepts that reshape our world. We can think back, for example, to Gutenberg's printing press.

This session will be an introductory dive into sklearn & theano. What each one is used for and how to build a basic model with each. We will do a walkthrough of the developer portal, testing the production system as well as security. Here's Why Cloud Adoption Is About To Shift Into High Gear Friday Spotlight: automated installation of Oracle VM Server x86 Here's Why Cloud Adoption Is About To Shift Into High Gear

Machine Learning can be used to derive a variety of insights as well as predict the future using data. This talk will go through a variety of advanced techniques in machine learning to produce better results. We will take a dive at various algorithms, problem resolutions and what to do if you don't have enough data. These techniques will work for you in SQL Server, Azure Machine Learning and more. Here's Why Cloud Adoption Is About To Shift Into High Gear Friday Spotlight: automated installation of Oracle VM Server x86 Here's Why Cloud Adoption Is About To Shift Into High Gear