Name: The Tensor Renaissance In Data Science by O'Reilly Radar in ml.deeplearning
Duration: 31 min 14 s
Description: O'Reilly's Ben Lorica chats with Anima Anandkumar about tensor decomposition techniques for machine learning.

ml.deeplearning

On this episode of "Speaking of Bitcoin", join hosts Adam B. Levine, Andreas M. Antonopoulos, Stephanie Murphy and Jonathan Mohan for a discussion about loans in the world of crypto.
HODL. It's crypto-land philosophy for not selling your preferred token even when things look their most bleak. But where philosophy intersects with real-world concerns like shelter, food and the niceties of life, tactics have emerged offering both opportunities and risks. From smart contracts to bitcoin collateralized mortgages, the hosts discuss their experiences with loans in the world of crypto.
Credits
Today’s show featured Andreas M. Antonopoulos, Jonathan Mohan, Stephanie Murphy and Adam B. Levine, with editing by Adam B. Levine and music by Gurtybeats.com. Our episode art is a picture provided by The New York Public Library, modified by "Speaking of Bitcoin."
Any questions or comments? Send us an email at adam@speakingofbitcoin.show

SOB #496 The Ups and Downs of Crypto Loans

<a href="https://www.economist.com/finance-and-economics/2022/05/12/the-crypto-infrastructure-cracks" rel="noopener noreferrer" target="_blank">Stablecoins</a> are essential to the financial plumbing of the cryptocurrency world. They’re pegged to a real-world asset, usually the dollar. But when that peg breaks, things can turn ugly in a hurry. Much of India is suffering through a particularly blistering and costly <a href="https://www.economist.com/the-economist-explains/2022/05/13/the-increasing-frequency-of-fatal-wet-bulb-temperatures" rel="noopener noreferrer" target="_blank">heatwave</a>. And Indonesians’ love of songbirds is threatening wild bird populations within and beyond Indonesia itself. For full access to print, digital and audio editions of The Economist, subscribe here <a href="http://www.economist.com/intelligenceoffer" rel="noopener noreferrer" target="_blank">www.economist.com/intelligenceoffer</a> &#10;&nbsp; <hr>See <a style='color: grey; ' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for privacy and opt-out information.

Luna landing: Crypto chaos

Cette semaine, Livo pensait fuir l'actualité électorale en allant flâner à Marseille, et le voilà pris dans un débat local sur l'usage de la plage des Catalans. Pire, il se sent poursuivi par Macron qui vient vanter son école du futur dans la ville de Jul. De leur côté, Virginie, instite syndiquée, et Zoubir, riverain de l'école en question, n'ont pas pu échanger avec le Président. Dommage, ils avaient des choses à dire...

"Dépêche", le podcast centenaire est à retrouver chaque mercredi sur ARTE Radio. Abonnez-vous sur notre site, Apple Podcasts, Spotify, SoundCloud ou Deezer.

Enregistrements : 31 mai, 2, 3, 6, 7 juin 22 - Texte, voix, réalisation : Olivier Minot - Mix : Charlie Marcelet - Production : ARTE Radio

Dépêche !

Peurs sur Marseille

At Made by Google '22, Google unveiled its latest Pixel lineup with the Pixel 7 and Pixel 7 Pro powered by Google's 2nd-gen mobile chip, the Tensor G2, powering more AI and machine learning features like Super Res Zoom, Photo Unblur, Cinematic Blur. Also announced was Google's first smartwatch, the Google Pixel Watch, with Fitbit's health and fitness expertise, and shared some details on the Pixel Tablet with a charging speaker dock coming in 2023. 
Hosts: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a> and <a href="https://twit.tv/people/ron-richards">Ron Richards</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/twit-news">https://twit.tv/shows/twit-news</a>. 
Get episodes ad-free with Club TWiT at <a href="https://twit.tv/clubtwit" rel="payment">https://twit.tv/clubtwit</a>

TWiT News 386: Made by Google 2022

Il Folle volo

In 1977, a mild-mannered aeronautical engineer sideswiped a parked car in Compton, CA. He stopped his car to see the damage, and all of a sudden a man opened his car door, and jumped in the car. He said, "I'm about to save your life." What happened next would torture him for the rest of his life.

Ep 11: I'm About to Save Your Life

prova

O'Reilly's Ben Lorica chats with Anima Anandkumar about tensor decomposition techniques for machine learning.

The Tensor Renaissance In Data Science

Daredevil Season 2 is almost upon us and that means a new iteration of The Punisher, one of the most mangled and misrepresented characters in Marvel cinematic history! To help us wade through Frank Castle's movie graveyard, Lon Strickland comes on the show to argue that Dolph Lundgren's 1989 version is the best so far! Beyond that, we talk about Spider-Man dropping in on the 'Captain America: Civil War' trailer, the state of animation in the USA and what it was like for Lon to work at Machinima during their heyday!

Geekscape 402: Punishing The Guilty With Lon Strickland!

With the release of Spark version 2.0, streaming starts becoming much more accessible to users. By adopting a continuous processing model (on an infinite table), the developers of Spark have enabled users of its SQL or DataFrame APIs to extend their analytic capabilities to unbounded streams.

Within the Spark community, Databricks Engineer, Michael Armbrust is well-known for having led the long-term project to move Spark’s interactive analytics engine from Shark to Spark SQL. (Full disclosure: I’m an advisor to Databricks.) Most recently he has turned his efforts to helping introduce a much simpler stream processing model to Spark Streaming (“structured streaming”).

Structured Streaming Comes to Apache Spark 2.0

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As data scientists add deep learning to their arsenals, they need tools that integrate with existing platforms and frameworks. This is particularly important for those who work in large enterprises. In this episode of the Data Show, I spoke with Adam Gibson, co-founder and CTO of Skymind, and co-creator of Deeplearning4J (DL4J). Gibson has spent the last few years developing the DL4J library and community, while simultaneously building deep learning solutions and products for large enterprises.

The Key to Building Deep Learning Solutions for Large Enterprises

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.

Deep Learning for Apache Spark

In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning. Most of our conversation centered around MXNet—an open source, efficient, scalable deep learning framework. I’ve been a fan of MXNet dating back to when it was a research project out of CMU and UW, and I wanted to hear Anandkumar’s perspective on its recent progress as a framework for enterprises and practicing data scientists.

Deep Learning That's Easy to Implement and Easy to Scale

Hall Of The Slain

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

The Tensor Renaissance In Data Science

O'Reilly Radar

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