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?

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 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?

This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least $\sqrt{\log(T)}$ better, where $T$ is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.

In my 25 years of working with large datasets, from developing early machine learning algorithms for multimedia systems in the 1990s to optimizing the email marketing infrastructure at GSI Commerce in the 2000s and now applying machine learning to big data to find actionable insights in real time, I've seen the convergence of machine learning and marketing firsthand. This year, I'm excited to see how machine learning (ML), an artificial intelligence (AI) discipline geared toward the technological development of human knowledge, has impacted the marketing big data ecosystem. I'm also intrigued by how much room I see for growth in the future. Machine learning techniques are being used to solve many diverse problems, and we stand to benefit as we move towards a world of hyper-converged data, channels, content, and context -- having the right conversation at the right time with the right person in the right way. For us marketers, ML is about finding nuggets of "predictive" knowledge in the waves of structured and unstructured data.

Five years from now, company leaders will be looking back and wishing they developed an artificial intelligence strategy sooner, according to one of the veterans of the field. Andrew Ng, the cofounder of Coursera and the former machine learning chief at Chinese tech powerhouse Baidu, said that he thinks Fortune 500 businesses will find the rise of AI similar to the rise of the internet. Some top CEOs bemoan how their businesses were late to the party when it came to competing on the internet, and Ng said that the same thing will be true when it comes to AI. In his view, businesses are best off hiring a leader with deep knowledge of the field who can help build up an organization's knowledge and capabilities in a centralized way. That chief AI officer, as he described it, would be charged with helping to bring expertise in the field to the rest of the a company.

Editor's note: This tutorial series was started in September of 2014, with the 8 installments coming over the course of 2 years. I only mention this to put John's first paragraph into context, and to assure readers that this informative series of tutorials, including all of its code, is as relevant and up-to-date today as it was at the time it was written. This is great material, both for anyone taking Andrew Ng's MOOC and as a standalone resource. One of the pivotal moments in my professional development this year came when I discovered Coursera. I'd heard of the "MOOC" phenomenon but had not had the time to dive in and take a class.

Last week, Elon Musk dashed off 125 characters announcing a remarkably ambitious plan to send Amtrak to an early grave. "Just received verbal govt approval for The Boring Company to build an underground NY-Phil-Balt-DC Hyperloop. NY-DC in 29 mins," he proclaimed in a tweet. Sign up to get Backchannel's weekly newsletter. Yet something about this particular moonshot seemed off.