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Why Machine Learning Algorithms Fall Short

The video presentation below is “Why Machine Learning Algorithms Fall Short (And What You Can Do About It)” by Jean-François Puget, speaking at MLconf SF 2016. The talk examines how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time.

ROOT Data Center – Wholesale Provider to Implement AI and Machine Learning for Reduced Downtime Risk

ROOT Data Center, announced that it is the first wholesale data center in the world to use Artificial Intelligence (AI) and machine learning to reduce the risk of data center downtime. ROOT Data Center has partnered with state-of-the-art AI and machine learning technology firm Litbit, within ROOT’s Montréal-based facility.

Machine Learning: A Guide for Non-Technical Readers

Machine learning has become a water-cooler topic across industries. And the chatter about the possibilities of AI and deep learning certainly isn’t slowing down anytime soon. Download the new report from Dataiku that offers a guide to machine learning basics for non-technical readers.

Trifacta Expands Data Wrangling on the Cloud with Additional Support of Amazon Web Services and Availability on AWS Marketplace

Trifacta, a leader in data wrangling, announced expanded support for Amazon Web Services (AWS) and the availability of Wrangler Edge and Wrangler Enterprise on AWS Marketplace, allowing organizations to deploy Trifacta in less than an hour. Trifacta has also earned AWS Machine Learning (ML) Competency status. This achievement recognizes that Trifacta has demonstrated success in helping customers deploy their ML workloads on AWS.

Cazena Extends Big Data as a Service Platform with New “App Cloud” to Accelerate Adoption of Partners’ Machine Learning and Analytical Solutions on AWS and Azure

Big Data as a Service leader Cazena announced its new “App Cloud,” which makes it easier for enterprises to deploy a wide range of machine learning (ML) and analytics applications on AWS and Microsoft Azure with instant access to their data on the Cazena platform.

10 Tips for Building Effective Machine Learning Models

In this contributed article, Wayne Thompson, Chief Data Scientist at SAS, provides 10 tips for organizations who want to use machine learning more effectively. Machine learning continues to gain headway, with more organizations and industries adopting the technology to do things like optimize operations, improve inventory forecasting and anticipate customer demand.

Python: Unlocking the Power of Data Science & Machine Learning

Python stands out as the language best suited for all areas of the data science and machine learning framework. Designed as a flexible general purpose language, Python is widely used by programmers and easily learnt by statisticians. Download the new guide from ActiveState that provides a summary of Python’s attributes, as well as considerations for implementing the programming language to drive new insights and innovation from big data.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – November 2017

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Intermedix Data Scientists Published for Health Care Machine Learning Research

Two of Intermedix’s leading data scientists, Danielle Baghernejad and Lihong Li, have been published in Biomedical Journal of Scientific & Technical Research and Advances in Biotechnology & Microbiology respectively for their pioneering research in machine learning.

Machines: How Do They Learn and Where Are They Headed?

In this public lecture held at the Institute for Advanced Study on October 27, 2017, Sanjeev Arora, Visiting Professor in the School of Mathematics, and Richard Zemel, Visitor in the School of Mathematics, gave brief talks about the field of machine learning and its major technical challenges. A panel discussion moderated by Robbert Dijkgraaf, Director and Leon Levy Professor, followed.