Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis. For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We've done the same in the past for Machine Learning and NLP terms, which you might also find interesting. In the human brain, a neuron is a cell that processes and transmits information.

Below is the latest post (and the first post in these 10 months...) of my blog. What kind of decision boundaries does Deep Learning (Deep Belief Net) draw? Once I wrote a post about a relationship between features of machine learning classifiers and their decision boundaries on the same dataset. The result was much interesting and many people looked to enjoy and even argued about it. Actually I've been looking for similar attempts about Deep Learning but I couldn't find anything so far.

Watch DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a 1M 5-game challenge match in Seoul. This is the livestream for Match 3 to be played on: 12th March 13:00 KST (local), 04:00 GMT; note for US viewers this is the day before on: 11th March 20:00 PT, 23:00 ET. In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.

Artificial intelligence* is developing much faster than we thought. Just last month, Google's DeepMind AI beat Lee Sedol, a legendary Go player, at his own game in a defining moment for the industry. What enabled this win is a relatively new AI technique called deep learning, which is transforming AI. Until deep learning was introduced, even the best AI systems were always highly tuned for specific problems and required many rules to operate successfully. But deep learning has changed that, causing many researchers to abandon classical AI approaches.

In the very same week that Artificial Intelligence lost one of its greatest pioneers, Marvin Minsky, it saw major progress on a decades-old challenge of playing human-level Go. There is much to shout about, but also a lot of hype and confusion about what we just saw. With so much at stake as people try to handicap the future of AI, and what it means for the future of employment and possibly even the human race, it's important to understand what was and was not yet accomplished. Fact: The paper published yesterday in Nature by DeepMind represents major progress in getting AI to play Go, a game that has been notoriously difficult for machines. Confusion: The European champion of Go is not the world champion, or even close.

Next time you ask Google for directions or run an image search, thank Le Viet Quoc. The 34-year-old Vietnamese engineer is part of the team behind Google Brain, an artificial intelligence (AI) research project whose technology is responsible for such features, reports VnExpress. Part of Google's not-so-secret research outfit X, which pioneers cutting-edge technology like self-driving cars and delivery drones, Quoc works in a field known as "deep learning" which uses the human brain as a model to create "neural networks" for computers. Though deep learning's development has been slow, engineers like Quoc are making progress: in 2012, Google Brain made headlines when its network of 16,000 computer processors successfully learned how to search for cat videos on YouTube, despite being given no information prior to the test on how to identify such animals. The Stanford grad, who holds a doctorate in computer science and was named one of MIT's Innovators Under 35, is still working toward the creation of better, more intelligent machines.

What are you trying to do with your loop?

Welcome to the second lesson in our Practicing Deep Learning Series. Thoughtly is writing a multi-part tutorial series focused on understanding the foundations of Deep Learning, specifically as they apply to Natural Language Processing. If you want to jump to another post check the post listing here. Last time we focused on the elements of a simple single neuron network. We specifically discussed those that feed into the neuron – the inputs and weights – and their interaction via the dot product.

Microsoft is making the tools behind applications like Cortana available on code repository GitHub, opening the doors to new open source machine learning projects based on its software programmes. Redmond originally created the toolkit, known as Computational Network Toolkit (CNTK), out of necessity to help its developers make faster improvements to how well computers can understand speech. But in a blog post, Microsoft explained that CNTK has proved to be "more efficient" than the four notable computational toolkits – namely Theano, TensorFlow, Torch7, and Caffe – that developers were using to create deep learning applications. "The CNTK toolkit is just insanely more efficient than anything we have ever seen," said Xuedong Huang, chief speech scientist at Microsoft. This has allowed Microsoft's researchers to create systems that can accurately recognise and translate conversations, as well as ones that can recognise images and even answer questions about them.

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave (@317070), 3 PhD students, and professor Joni Dambre. It's also a follow-up of last year's team Deep Sea, which finished in first place for the First National Data Science Bowl. This blog post is going to be long, here is a clickable overview of different sections. The goal of this year's Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat.