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Lecturer Steffen Pfiffner of University of Weingarten in Germany is teaching ROS to 26 students at the same time at a very fast pace. They connect to a web page containing the lessons, a ROS development environment and several ROS based simulated robots. Using the browser, Pfiffner and his colleague Benjamin Stähle, are able to teach how to program with ROS quickly and to many students. This is what Robot Ignite Academy is made for. "With Ignite Academy our students can jump right into ROS without all the hardware and software setup problems. And the best: they can do this from everywhere," says Pfiffner.

Thanks to video games, the idea of talking to a virtual character is commonplace. Whether it's the onscreen text of early Super Nintendo titles or the fully rendered characters of the Mass Effect series, people have long been able to understand how these features work. But what if onscreen characters could be used for other applications besides gaming? Kognito is a New York company focused on designing simulations and avatars. These avatars and conversational scenarios are used for applications ranging from medical professional training, helping parents learn how to teach their kids about health and substance issues and veteran care.

You can download and modify the code from this tutorial on GitHub here. According to Yann LeCun, "adversarial training is the coolest thing since sliced bread." Sliced bread certainly never created this much excitement within the deep learning community. Generative adversarial networks--or GANs, for short--have dramatically sharpened the possibility of AI-generated content, and have drawn active research efforts since they were first described by Ian Goodfellow et al. in 2014. GANs are neural networks that learn to create synthetic data similar to some known input data.

Did you have a good, relaxing break over the summer? Are you refreshed and re-energised, looking forward to a new start, a new you and brushing up on your data analysis skills? If so, I've thrown together a collection of a few excellent (and free!) statistics eBooks for your Kindle to sharpen up your stats while you're on the long commute to work. Just try not to read them while driving! These books require different levels of existing knowledge, and while some are for early-stage data scientists others are for more hard-core physicists and mathematicians.

Each lesson page includes links to course notes, forum discussion, and (most importantly) a wiki page. Nearly all the participants in the original in-person course said that they found these resources very important for successfully completing the course. So be sure to make the most of them! And be sure to carefully read the Getting Started page to find out how this course is designed and how to get the most out of it.

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).

Andrew Ng is one of the foremost thinkers on the topic of artificial intelligence. He founded and led the "Google Brain" project which developed massive-scale deep learning algorithms. In 2011, he led the development of Stanford University's main Massive Open Online Course (MOOC) platform. His course on Machine Learning would eventually reach an "enrollment" of over 100,000 students. That experience led Ng to co-found Coursera, a MOOC that partners with some of the top universities in the world to offer high quality online courses. Today, Coursera is the largest MOOC platform in the world.

Here we bring you the most popular recent Machine Learning videos worth watching. This is the first video (Lecture 1 published 8 years ago) in the great series of Stanford machine learning lectures given by Andrew Ng. Originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.

In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways.