Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

It would have been great to actually experience the online training by showing it's improvement on autopilot after training on each lap individually. Unfortunately it just cuts out and then shows it on autopilot so I guess we just have to take their word for it?

Andrew Ng is a soft-spoken AI researcher whose online postings talk loudly. A March blog post in which the Stanford professor announced he was leaving Chinese search engine Baidu temporarily wiped more than a billion dollars off the company's value. A June tweet about a new Ng website, Deeplearning.ai, Today that speculation is over. Deeplearning.ai is home to a series of online courses Ng says will help spread the benefits of recent advances in machine learning far beyond big tech companies such as Google and Baidu.

Even though I did not follow his older courses, they seem really appreciated, at least on this subreddit. I hope these new ones will set an even higher standard. That way, newcomers may share an identical set of notations, principles and methodologies so we can all focus on other tasks, such as visualization. You will practice all these ideas in Python and in TensorFlow. What do you guys think of this choice?

Deep Learning-powered image recognition is now performing better than human vision on many tasks. We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.

Here I discuss four interesting mathematical problems (mostly involving famous unsolved conjectures) of considerable interest, and that even high school kids can understand. The field itself has been a source of constant innovation -- especially to develop distributed architectures, as well as HPC (high performance computing) and quantum computing to try to solve (to non avail so far) these very difficult yet basic problems. And for those interested in mathematical logic and measure theory, here is an interesting paradox, which somehow allows you to duplicate a ball made out of gold, into two balls, each having the same size as the original ball (though it does not double the mass.) The Banach–Tarski paradox is a theorem which states the following: Given a solid ball in 3‑dimensional space, there exists a decomposition of the ball into a finite number of disjoint subsets, which can then be put back together in a different way to yield two identical copies of the original ball.

Machine learning is, as you would likely imagine, extremely complicated, and not something your run-of-the mill computer engineer is going to be capable of without proper training. It requires someone with a background in computer science, likely with a doctorate in the sciences, as well as a significant amount of practical experience working with data at scale. Given that there is already a dearth of qualified data scientists, there is little to suggest that the situation is going to be any different when it comes to machine learning. And this is already hampering the technology. Just 15% of organizations manage to bring their big data projects to production, according to Gartner analyst Nick Heudecker, and he believes this number is likely to be far lower when it comes to machine learning.

Here is a list of the best courses in Data Science and Machine Learning from Udemy. With the back-to-school sale, you can get these and other Udemy courses for $12 ($10 if it is your first purchase), 90-95% off original price. Offer expires on Aug 10, 2017. Udemy.com is an online marketplace for learning, their data science content is updated regularly by the instructors who created good courses (filled with actionable tools) and bite-size lessons that help you cover defined topics at your own pace. Ready to be thrown into the deep end and learn the real problems a data scientist faces on a daily basis?

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.