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A Developer’s Guide to Launching a Machine Learning Startup

Frameworks, applications, libraries and toolkits—journeying through the world of deep learning can be daunting. If you’re trying to decide whether or not to begin a machine or deep learning project, there are several points that should first be considered.

Launch a Machine Learning Startup

Launch a Machine Learning Startup – In this report, we’ll address everything from how to choose a framework and pick the tools you need to get started, to the questions you’ll be asking yourself, and the benefits of immersing yourself in the machine and deep learning communities. This report also untangles the jargon and explores what these terms actually mean. Download this special report now.

Virtualitics: Caltech & NASA Scientists Build VR/AR Analytics Platform using AI & Machine Learning

Virtualitics is a transformative start-up company that merges artificial intelligence (AI), big data and virtual reality (VR), and augmented reality (AR) to gain insights from big and complex data sets. Furthermore, Virtualitics leverages AI and easy-to-use machine learning tools so even non-expert users can uncover multidimensional relationships present in complex data sets with the click of a button.

Machine Learning: the Power and Promise of Computers that Learn by Example

25 April 2017

The many potential social and economic benefits from advances in AI-based technologies depend entirely on the environment in which these technologies evolve, says the Royal Society. According to a new report from the UK’s science academy, urgent consideration needs to be given to the “careful stewardship” needed over the next ten years to ensure that the dividends from machine learning – the form of artificial intelligence that allows machines to learn from data – benefit all in UK society.

New DataRobot Release Extends Enterprise Readiness Capabilities and Automates Machine Learning in Insurance Industry Pricing Models

DataRobot, the leader in machine learning automation, unveiled significant new features in the DataRobot machine learning automation platform, including new model deployment options, SAS integration, and features that make it easier than ever for analysts of any skill level to quickly build and deploy accurate predictive models.

Machine Learning: Better Means of Consumption Required

In this contributed article, Darren Peirce, CTO of Magnitude Software, discusses how companies that will win using machine learning will be those that figure out how to take the innovation, apply it in simple ways, and make it consumable for the end user.

Interview: Daphne Koller, Chief Computing Officer, Calico; Adjunct Professor of Computer Science, Stanford University

The following is a discussion with Daphne Koller, Chief Computing Officer, Calico Labs; Adjunct Professor of Computer Science, Stanford University; ACM-Infosys 2007 Foundation Award. The Association of Computing Machinery (ACM) just concluded a celebration of 50 years of the ACM A.M. Turing Award (commonly known as the “Nobel Prize of computing”) with a two-day conference in San Francisco. The conference brought together some of the brightest minds in computing to explore how computing has evolved and where the field is headed.

FogHorn Systems Brings Advanced Machine Learning Capabilities to Industrial IoT Edge Computing

FogHorn Systems announced the availability of Lightning ML, the newest version of its Lightning™ edge intelligence software platform for the Industrial Internet of Things (IIoT). Lightning ML is now the industry’s first IIoT software platform with integrated machine learning capabilities and universal compatibility across all major IIoT edge systems.

Using Python to Drive New Insights and Innovation from Big Data

In a recent white paper “Management’s Guide – Unlocking the Power of Data Science & Machine Learning with Python,” ActiveState – the Open Source Language Company – provides a summary of Python’s attributes in a number of important areas, as well as considerations for implementing Python to drive new insights and innovation from big data.

Book Review: Statistical Learning with Sparsity – The Lasso and Generalizations

As a data scientist, I have a handful of books that serve as important resources for my work in the field – “Statistical Learning with Sparsity – The Lasso and Generalizations” by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is one of them. This book earned a prominent position on my desk for a number of reasons.