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about 3 hours
This episode is a conversation with Iain Bethune from the Edinburgh Parallel Computing Center about high-performance computing: the topic has played an implicit role in many previous omega tau episodes, and this episode treats it explicitly. We discuss different architectures (supercomputers, commodity clusters, grid computing), programming languages and software design as well as application areas.
7 minutes
In the second part of the Docker Swarm series, Dongluo Chen continues his demonstration and shows us how to provision containers on both Windows and Linux using Swarm. Find a topic: [00:19] How to add Windows node to a swarm cluster? [01:31] Demo - How to add Windows Server 2016 as worker node behind swarm cluster? [03:50] How to create a windows server container instance through the swarm cluster? [04:58] Demo - How to create a new container instance on Windows Server 2016 through the swarm cluster? [06:19] How to get started? Get started: Watch the Running Docker Swarm on Microsoft Azure - Part 1 video Learn more about Docker Swarm  Take the next steps: Create clusters using the Azure Swarm template Deploy Azure ACS + Swarm template What do you think? Send us an email, comment below in the comments area on Channel 9, or tweet us at @TheOpsTeam   Follow @TheOpsTeam Follow @DTzar Follow @dongluochen
about 1 hour
On episode 11 of SOMEWHERE IN THE SKIES, Ryan speaks with Erica Lukes, a UFO researcher out of Utah. They discuss the many mysterious happenings at Dugway Proving Ground, a U.S. Army facility known for testing biological and chemical weapons. But recently, many exotic aircraft and UFO activity have been reported in and around the base. Erica also speaks to us about the strange plethora of activity surrounding the Skinwalker Ranch. The conversation then moves to strictly UFOs with Erica's research into a 2014 mass UFO sighting over Salt Lake City, the orange orb phenomenon, and the stunning UFO incident of American Airlines Flight 434. Guest Bio: Erica Lukes is the head of Unexplained Utah, an organization which is focused upon scientifically researching Unidentified Aerial Phenomena. She is currently developing research protocols that can be utilized by other organizations and to streamline the research process. She now serves as the Communications Director for the International Association of UAP Researchers. She has collected decades’ worth of case reports from Skinwalker Ranch regarding mass sightings of UFOs, mysterious mutilations of animals, and alien abductions. Erica was the team-leader on a research program called “Project Orange.” This project is specifically dedicated to studying sightings and clusters of orange/red orbs. Erica was also the lead investigator on the American Airlines 434 case over Nephi, Utah. Her weekly radio show, UFO Classified, and all her work can be found at: ​Guest & Topic Suggestions: Twitter: @SomewhereSkies Facebook Group: Click Here Order Ryan's Book by Clicking Here  
31 minutes
The O’Reilly Data Show Podcast: Jason Dai on BigDL, a library for deep learning on existing data frameworks. In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications. When I predicted that 2017 would be the year when the big data and data science communities start exploring techniques like deep learning in earnest, I was relying on conversations with many members of those communities. I also knew that Dai and his team were at work on a distributed deep learning library for Apache Spark. This evolution from basic infrastructure, to machine learning applications, and now applications backed by deep learning models is to be expected. Once you have a platform and a team that can deploy machine learning models, it’s natural to begin exploring deep learning. As I’ve highlighted in recent episodes of this podcast (here and here), companies are beginning to apply deep learning to time-series data, event data, text, and images. Many of these same companies have already invested in big data technologies (many of which are open source) and employ data scientists and data engineers who are comfortable with these tools. While there are many libraries, cloud services, and packaged solutions available for deep learning, deploying it usually involves big (labeled) data, big models, and big compute, so a typical project involves data acquisition, preprocessing and preparation on a Spark cluster, and model training on a server with multiple GPUs. A new project called BigDL, offers another option: it brings deep learning directly into the big data ecosystem. BigDL is an open source, distributed deep learning library for Apache Spark that has feature parity with existing popular deep learning frameworks like Torch and Caffe (BigDL is modeled after Torch). For the many companies that already have data in Hadoop/Spark clusters, BigDL lets them use those same clusters for deep learning. Source: Jason Dai, used with permission. The typical deep learning pipeline that involves data preprocessing and preparation on a Spark cluster and model training on a server with multiple GPUs, now involves a simple Spark library that runs on the same cluster used for data preparation and storage. BigDL takes advantage of MKL software and also lets you efficiently train larger models across a cluster (using distributed synchronous, mini-batch SGD), and an AMI is available to those who want to run it on Amazon Web Services. While GPUs still provide much faster training times for deep learning, and thus remain the option for bleeding-edge researchers, BigDL should appeal to companies that have invested in big data clusters and software (convenience versus performance). This is true even for companies using cloud computing resources, or even public cloud providers that have invested in more CPU than GPU compute resources. Many data products involve complex data pipelines, and machine learning models comprise a small component of such systems. I imagine some companies will be drawn to BigDL because it opens up the possibility of having a unified platform for data processing, storage, feature engineering, analytics, machine learning, and now deep learning. This means not having to transfer data between clusters or frameworks (BigDL is just a Spark library), lower total time for end-to-end learning, and simpler resource and workflow management. This is, in fact, the origin of BigDL: the team decided to work on it after several companies in China expressed interest in using their existing hardware and compute resources for deep learning projects and workloads. BigDL was publicly released as open source at the end of 2016. In the months leading up to its public release, Dai and his team helped companies use it in production on Spark clusters comprised of several tens of Xeon servers. Some early use cases include fraud detection systems at a large payment company and a large commercial bank, and image classification and object detection applications at large manufacturing companies. We’re still in the very early stages of companies adding deep learning to their list of machine learning models. I expect that we will continue to experiment with a variety of managed services, and proprietary and open source tools for deep learning. BigDL brings another option for companies that want to leverage their existing big data infrastructure and ease the adoption of deep learning by teams who are familiar with existing frameworks. There’s an economic benefit as well: besides the convenience that comes from using existing tools, reducing complexity and increasing utilization can often mean much lower TCO. The Call For Proposals for Strata + Hadoop World Beijing closes on Feb 24, 2017. Related resources: Web-scale machine learning on Apache Spark: Jason Dai’s talk at Strata Singapore 2016 Code for BigDL (deep learning), SparseML (machine learning on sparse data), and topic modeling 2017 will be the year the data science and big data community engage with AI technologies The key to building deep learning solutions for large enterprises Use deep learning on data you already have How big compute is powering the deep learning rocket ship
19 minutes
  We’ve been seeing a lot of recipes pop up around the internet for cauliflower. It’s kind of weird…but it prompted us to do this topic. In this episode of the Vegetarian Zen podcast, we talk about cauliflower, the other meat substitute.   Thanks for tuning in to this episode of the Vegetarian Zen Podcast! […] The post VZ 212: Cauliflower: The Other Meat Substitute appeared first on Vegetarian Zen.
Results from audio
00:00:29for Colly Florida reception notice that she said that this looks kind of like that's a new
00:00:34trend is kind of Weird but it's prompted US to do this topic in this episode of our
00:00:39podcast we're going to be talking about cauliflower the other meat. Substitute
00:02:22that we asked area is really awful lot more about Larry David.
00:02:27Our guides are ready to jump into the main topic already have under Nice Yeah some Awesome Yeah
00:02:32I was Thundering these kind of dark out there will simply happen is a cauliflower
00:02:37OK so called flower really isn't a vegetable that you'd necessarily think of as a
00:02:42substitute for Me right now and not at all when you told me about this topic is you're the one that usually does the.
00:02:46Research babies I was I was like was it Yeah right I mean You Know
00:08:03creamy white compact. I'm not like really loose head You Want
00:08:07something that's really tight. Where the clusters or not separated
00:08:12so you just don't really compact. So the EU also
00:09:04You Want to keep you want to keep that with stem side down to prevent the moisture from developing
00:09:09in the floor clusters Yeah so some ways to use cauliflower in
00:09:14place of meat and there are lots of them and You know there are also ways like we mentioned
00:12:09way for gluten free people to enjoy a healthier Pizza. Are and I
00:12:14think that does it for our main topic This is Vera been very educational for Me this
00:12:18particular episode because it was I just cannot of thought of it
20 minutes
In this 57th episode, we explain how to use unsupervised machine learning algorithms to catch internet criminals who try to steal your money electronically!  Check it out at:  
Results from audio
00:07:34clustering in the proceedings of the actuator National Conference on communications
00:07:39recently thousand thirteen a acts by the paper on this topic is available.
00:07:44If you visit the website. Dot learning machines one and one dotcom and
00:11:34custer's a sizable number for each possible way of grieving and
00:11:39harvesters indicate distinct clusters it's never signed a particular with
00:11:43Western and harvesters indicates think Westerners small this is a case of a good
00:12:40Harvesters and it cost are more similar to one another while
00:12:44making harvesters in different clusters more different from one
00:12:49another. This basic principle clustering is well knows cystic
00:15:03we're going to show the Cape which minimizes Harvester clustering
00:15:08measure. In any case a Super now another clusters K has been
00:15:13chosen using one at the bottom at this or some other method so now that
30 minutes
Come join the cosmic café at Neutrons, protons, and electrons—these are the basic building blocks of matter. But this kind of matter is only a tiny fraction of the entire universe. The rest, about 95 percent, in fact, is divided between dark matter and dark energy. Understanding what makes up dark matter and dark energy could help answer some of the biggest questions in physics. Physicists Jodi Cooley, Dan Hooper, and Nobel Prize winner Steven Weinberg join Ira Flatow to discuss what we do and don’t know about this “darker” side of physics, and what we hope to learn.
Results from audio
00:01:25the so-called dark matter and see that the dark matter when two
00:01:30clouds of it from the two clusters of galaxies collide with each other
00:01:35nothing happens it just goes whizzing right through narrow they have
00:01:35nothing happens it just goes whizzing right through narrow they have
00:01:40seventy two of these colliding clusters of galaxies
00:01:45and it's all pinned down much more accurately with better
00:04:01you'd like to get in on the conversation because we're going dark matter will get into I don't want to Steve's
00:04:06favorite topic dark energy.
00:04:07We'll talk about that and anything else that has to do with the cosmos.
00:13:40then I think earlier you talked about the this new paper about a
00:13:44colliding galaxy clusters and that provides some of the strongest evidence to date as
00:13:49well. Not only that there seems to be missing mass but that
00:25:03saw this this new concrete evidence that dark matter exists from these colliding
00:25:08clusters. So a lot of stuff is advancing and I'm
00:25:12optimistic that with the next round of the Large Hadron Collider in the next round of dark matter
about 1 hour
Missing 411. North America. One man's research shows that for more than a century, people have been disappearing under mysterious circumstances across North America. Now, author David Paulides brings some of these cases, previously recounted in his series of books, to living rooms in the form of his Missing 411 documentary. Made with the help of his son, Benjamin, they profile five separate, mysterious incidents involving children. When a person goes missing there is a search effort to locate them. But national parks and even forests can make such searches difficult. Adding to these difficulties are the questions that remain long after a person's remains are located. How did a child disappear when surrounded by family? How and why did they end up 12 miles away and a half a day later with no explanation of how they could have traversed such difficult, mountainous terrain on their own? We interview David about why he got involved in researching these disappearances as well as what can be done to understand why they are happening and possibly prevent them.
Results from audio
00:08:25totally different career path such as investigative journalism and the missing for one
00:08:31well I was doing some Pro. For research on another topic at a National Park and
00:08:36Ari had a couple books that were published in laws at this park in August a couple
00:25:56this out and we come up with a stack of Records and then we start to realize.
00:26:01Well these there's these geographical clusters of missing people that
00:26:06matches profile points and we threw them on the ground could run room then we put up a
00:26:06matches profile points and we threw them on the ground could run room then we put up a
00:26:10map of North American we come up with fifty nine geographical clusters eighty
00:26:15percent of those are within a hundred and fifty to two hundred miles of major body of water
00:28:49y. Stamps dot com never go to the post office again are right back to
00:28:54the show when you're talking about these clusters are
00:28:59these places more frequented by tourists are these high
00:33:33points about that also don't You. Well I think that's an easier way for
00:33:38people to dismiss it and for their mind to go under comfort Zone on the on the topic
00:33:42but the reality is at many cases where the clothing was found within eyesight
00:49:26a lot of times there's an adult who disappears or young teenager.
00:49:32And what I talk about there's a lot of cases in Colorado there's the clusters there's multiple clusters of
00:49:37color on My map and in Colorado. Today that
31 minutes
This week, the earliest Americans, 2D magnets, and the legacy of the Universe’s first ‘baby picture’.
Results from audio
00:00:41. Immigration is a Hot topic in America right now and no not just
00:00:45because of Donald Trump the archaeological community is interested in when the very
00:03:03percent over a five month period we found interesting concentrations of bones and
00:03:07rock. Concentrated into distinct clusters the two clusters each other heavy
00:03:12cobblestone at their center with pieces of broken bone and rock scattered around each one.
about 1 hour
The Night Sky Guy, Andrew Fazekas, talks about his beautiful new, Star Trek-inspired guide to the real wonders of astronomy.
Results from audio
00:22:59Voyagers there's no shortage of books that introduced the night sky but
00:23:04this is the first that approaches the topic aboard the starship Enterprise anybody who
00:23:09takes on writing about the universe has to come to grips with the fact that by the time the
00:25:11eyepiece I can't believe. Even what i'm saying and then in terms of deep Sky targets like
00:25:16things that are beyond the Solar System star clusters
00:25:20particularly globular star clusters are just absolute edge people are not
00:25:25familiar with with what they are It can think of it as sort of if you had a a
00:26:09illustrations in the book has a two page spread. Of a particular cluster
00:26:14Omega Centauri Yeah I mean You know all of the globular star clusters are
00:26:19one of those staples of backyard Sky watchers with You have a
00:26:29based on traditional astronomical types of. Objects and we went
00:26:33into the globular clusters and looking at that I knew right away I
00:26:38mean You Know You You Got such an amazing capabilities from like the Hubble Space
00:26:43Telescope and its cousins we can actually go right into the
00:26:48center of the question you can see that in the Omega globular clusters it's a staple
00:26:52for. For those in the southern hemisphere in the Centaurus constellation and it's