And we want your help! At Udacity, we believe in democratizing education. How can we provide opportunity to everyone on the planet? We also believe in teaching really amazing and useful subject matter. When we decided to build the Self-Driving Car Nanodegree program, to teach the world to build autonomous vehicles, we instantly knew we had to tackle our own self-driving car too.

We've added some blogs that were missing in the original list, and eliminated some that aren't worth mentioning, hoping to make this list less biased. AnalyticBridge, about advanced analytics, books, salary surveys, training, challenges. Anil Batra's Web Analysis (Analytics), Online Advertising and Behavioral Targeting blog BigDataNews General articles about big data, as well as news (selected press releases) Business. CoolData By Kevin MacDonell on Analytics, predictive modeling and related cool data stuff for fund-raising in higher education. Cloud of data blog By Paul Miller, aims to help clients understand the implications of taking data and more to the Cloud.

Some foundations of statistical science have been questioned recently, especially the use and abuse of p-values. See also this article published in FiveThirtyEight.com. Statistical tests of hypotheses rely on p-values and other mysterious parameters and concepts that only the initiated can understand: power, type I error, type II error, or UMP tests, just to name a few. Pretty much all of us have had to learn this old stuff (pre-dating the existence of computers) in some college classes. Sometimes results from a statistical test will be published in a mainstream journal - for instance about whether or not global warming is accelerating - using the same jargon that few understand, and accompanied by misinterpretations and flaws in the use of the test itself. Especially when tests are repeated over and over (or data adulterated or wrongly collected to start with) until they deliver the answer that we want.

Last week, IBM released a public beta of Watson Analytics, a platform for data exploration, visualization and predictive analytics. This product follows on Microsoft's Azure Machine Learning service, which provides cloud-based machine learning solutions. Interested to see how the offerings compare, I set up accounts with both services and set out to explore several datasets. For fairness, I should note that IBM's Watson analytics is in a public beta, while Microsoft's product is a significantly more mature offering. Besides relative maturity, the more striking difference between the products is the fundamentally different use cases they address.

The company showcases HCM Suite innovations to help drive inclusion for better business results. SAP has unveiled plans to use machine learning to help eliminate bias in hiring and employee performance reviews. At the SuccessConnect event in Las Vegas, the company exhibited planned upcoming capabilities within its cloud-based SAP SuccessFactors HCM Suite to help global organisations detect and avoid unconscious bias. As part of its commitment, SAP intends to optimise existing solutions and roll out new functionality in important decision areas that have prevented organisations from using total talent. The German company is designing the new features with a customer advisory group particularly focused on diversity and inclusion issues.

StartupSource's Liam Tracey-Raymont recently spoke with Andrew Arruda, CEO and co-founder of ROSS Intelligence Inc. ("ROSS"), to discuss ROSS's growth and to shed some light on the developmental timeline and hurdles associated with launching a tech startup. ROSS, initially developed in Canada and founded by University of Toronto ("U of T") students, is a cloud-based software program that assists users in answering legal questions efficiently and without relying on complicated boolean queries and keywords. While ROSS is still a relatively new product, it has quickly attracted significant media attention and immense interest from U.S. clients, which are predominantly law firms. Liam caught up with the ROSS CEO after Andrew returned to his new home in San Francisco, California, following a week on the road. Andrew, it's been a while. What have you been up to recently?

Every student who has panicked while reading the same page of a textbook over and over again may suspect this. But stress cramming for an exam does not work, because the facts are likely to be lost from your memory. Instead it is best to learn through practice tests to protect your brain from the effects of stress, with a study showing that we remember more this way. Every student who has panicked while reading the same page of a textbook over and over again may suspect this. Some 120 students were asked to learn a set of 30 words and 30 images. Each item was displayed for a few seconds on a computer screen.

Machine learning may once have been a topic of discussion only for computer scientists and researchers. Now, however, it is a technology businesses are eager to use. The need for machine learning and Artificial Intelligence (AI) is being driven by the massive amount of data being generated today. Statisticians can get insight from this data. But the volume is so large and growing at such a rate, the best way to tackle it is using the very same machines that are in part responsible for creating the data.

SAN FRANCISCO, Nov. 17, 2016 – Intel Corporation today announced a range of new products, technologies and investments from the edge to the data center to help expand and accelerate the growth of artificial intelligence (AI). Intel sees AI transforming the way businesses operate and how people engage with the world. Intel is assembling the broadest set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars. At an industry gathering led by Intel CEO Brian Krzanich, Intel shared how both the promise and complexities of AI require an extensive set of leading technologies to choose from and an ecosystem that can scale beyond early adopters. As algorithms become complex and required data sets grow, Krzanich said Intel has the assets and know-how required to drive this computing transformation.

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available resources (e.g. online course, tutorial and book information) and toolboxes are provided. Finally, we conclude this paper with remarks and some interesting research directions on data representation learning.