Name: Tools for Machine Learning Development by O'Reilly Radar in The O'Reilly Data Show Podcast
Duration: 39 min 27 s
Description: In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).

The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

Raplh Dogg,Profile,Just Real,& D-Mac went in on this track tell me what u think

Everythang

Jane Foster was introduced as the love interest of the superhero Thor, but now in the MCU Jane has picked up mjolnir and become a superhero herself. Learn the Marvel Comics history of Jane Foster before Thor Love and Thunder hits!     &nbsp;  Join #MarvelClub for the full THOR experience & more: &nbsp; &nbsp;      GHL RECOMMENDED READING from this episode:&nbsp; Follow the show on TWITTER -&nbsp; Visit our Facebook fan page:&nbsp; You can find Ashley at&nbsp;&nbsp;and Jason at&nbsp;. Thanks for showing up to class today. Class is dismissed!

419: Jane Foster (Thor)

- Tell us something we don't know!
- Stranger Things ROCKS "Master of Puppets"
- An Iowan on Jeopardy? (Waukee!)
- What's next for the Big 12?

Stranger Things ROCKS "Master of Puppets"!

Log4j, license plate reader dangers, AI graphing with Katana Graph, and more.<ul> 
<li>Log4j is far from over</li> 
<li>Is cryptocurrency's crash causing geadaches for ransomware gangs?</li> 
<li>Starlink unveils $5,000-a-month internet for oil rigs and premium yachts </li> 
<li>FCC chair proposes new US broadband standard of 100Mbps down, 20Mbps up</li> 
<li>The danger of license plate readers in post-Roe America</li> 
<li>Katana Graph CEO and co-founder Keshav Pingali talks </li> 
<li>about their AI-powered graph intelligence platform 
 </li></ul> 
Hosts: <a href="https://twit.tv/people/louis-maresca">Louis Maresca</a>, <a href="https://twit.tv/people/brian-chee">Brian Chee</a>, and <a href="https://twit.tv/people/curt-franklin">Curt Franklin</a> 
Guest: <a href="https://katanagraph.com/" target="_blank">Keshav Pingali</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/this-week-in-enterprise-tech">https://twit.tv/shows/this-week-in-enterprise-tech</a>. 
Get episodes ad-free with Club TWiT at <a href="https://twit.tv/clubtwit" rel="payment">https://twit.tv/clubtwit</a>
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<li><a href="http://meraki.cisco.com/twit" target="_blank" rel="sponsored">meraki.cisco.com/twit</a></li>
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</ul>

This Week in Enterprise Tech 502: A Graph Is Worth a Thousand Words

Il Folle volo

Carl Frampton v Kiko Martinez II dominates the podcast with the Northern Irishman, Frampton, bidding to win his 19th bout in a row.
Mike Costello and Steve Bunce discuss the fight as well as who Carl Froch's next opponent should be and the future of Anthony Joshua. Sep 04, 2014

5lspecials: 5live Boxing: Frampton v Martinez 04 Sep 14

this is a slow song isn't it..

like it.

Midnight

Firewatch stand auf unserer Liste der meisterwarteten Spiele 2016. Jetzt ist die Zeit der Abrechnung gekommen: Ist das Indie-Abentuer wirklich unsere Vorfreude wert gewesen, oder verdammen wir es wie eifersüchtige Ex-Freundinen?

Runde #46: Firewatch (Some people just want to watch the world burn)

Why don’t we understand how the female orgasm works? After years of scientific research, the male body is understood but when it comes to how women work, we are a long way behind. Why is there this gap in knowledge? 



It appears research has been hindered by the assumption that the female body works in the same way as the male body and that for women, arousal is all in the mind. There’s also a general attitude that studying sexual pleasure isn’t important and that female orgasms aren’t important to study as they serve no purpose for reproduction.



Researchers are slowly correcting these assumptions and making surprising discoveries.



We’ll take you behind the scenes to two orgasm labs to bring you the latest research on how orgasms work for women. We’ll also hear from Callista, who struggled with excruciating pain during sex for many years but was told the problem was all in her mind. Her journey to diagnosis shows how little is known, even amongst gynaecologists and doctors, about female sexual pleasure.  





Presenter: Aasmah Mir

Producer: Phoebe Keane



(Image: Woman's hand grasping a bed sheet. Credit: Shutterstock) Published on December 2, 2016.

The Female Orgasm

Parce que certains sextoys ont des voix intégrées, ils nous racontent, en creux, ce que nous aimerions entendre. Attention, ce podcast contient des voix d'hommes gutturales chaudes comme la braise. Et pas mal de vibrations.
Sexe & sounds, le podcast de Maïa Mazaurette : des bruits du porno aux sextoys qui parlent, des langues de l'amour aux mystères de l'ASMR et des musiques qui donnent envie... 
Réalisation : Samuel Hirsch - Texte & voix : - Maïa Mazaurette

Sex and sounds (n°2) : La voix des sextoys

In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).

Tools for Machine Learning Development

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

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.

Tools for Machine Learning Development

O'Reilly Radar

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