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.

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. Check out our previous selection of articles.

As machine learning technology continues to advance at a rapid pace, providers are excited by the potential of this type of artificial intelligence to predict which patients are most at risk for clinical events that require early intervention. However, these medical breakthroughs are being hampered by the lack of health data necessary to learn the complex patterns required to positively affect patient care. All Health Data Management content is archived after seven days.

This product-centric overview of machine learning is written by Neal Lathia, Senior Data Scientist at Skyscanner. It's now becoming common for me to hear that product owners, technical managers and designers are turning to popular online courses to learn about machine learning (ML). I always encourage it -- in fact, I did one of those courses myself (and blogged about it). However, it's not always clear how much benefit someone whose goal is to design, support, manage, or plan for products that use machine learning will get from doing an online course in ML. These courses throw you into the deep end, asking you to start programming classifiers, when many non-technical team mates are only looking for sufficient knowledge to be able to work in teams that are creating an ML-driven product.

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

Disclaimer: I am an IT guy and my knowledge on human body is limited to my daughter's high school biology class book and information obtained from search engines. So, excuse me if any of the information below is not represented accurately!! Human body is the most complex machine ever created. With a complex network of interconnected organs, millions of cells and the most advanced processor, human body is the most automated system in this planet. In this article, we will draw comparisons between working of a human body to that of a data center. We will draw parallels between human body automation to data center automation and explain different levels of automation we need to drive in data centers.

It's actually, like, kind of a problem that affects, um, roughly 74 per cent of people. Whether it's long pauses, or the use of'hedging' language (see previous sentence), the way you speak can negatively affect your credibility. Focus all that anxious blinking on one particular, bewildered person? Orai is a mobile public speaking course designed to tackle this specific problem, and in doing so, transforms your smartphone into a speaking coach. Created by engineering students at Drexel University, Orai helps you curate your word choices.

This is an interesting course on applications of linear algebra in data science. The course will first take you through fundamentals of linear algebra. Then, it will introduce you to applications of linear algebra for recognizing handwritten numbers, ranking of sports team along with online codes. The course is open for enrollment.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation. "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Not very clear, is it? This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn.

Tensorflow's meteoric rise to the top of the deep learning world is, while unsurprising, pretty damn impressive. With almost 60k stars on Github (the only reasonable measure of software popularity), Tensorflow is far out in front of nearest competitor Caffe, with its paltry 18k. The framework has a lot going for it: Python, great tools like Tensorboard, Python, Google's knowledge of distributed systems, Python, and popularity that all but guarantees future relevance. But while Tensorflow is a wonderful framework, the decisions (or lack thereof) being made by the Tensorflow product team are making it increasingly difficult for external developers to adopt. In my eyes, Tensorflow's public face has grown without proper direction, and is threatening to alienate developers and allow competing frameworks to take over.