Name: Building Intelligent Applications with Deep Learning and TensorFlow by O'Reilly Radar
Duration: 38 min 49 s
Description: In this episode of the O’Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies.

This brand-new unabridged audio book of The Fellowship of the Ring, the first part of J. R. R. Tolkien’s epic adventure, The Lord of the Rings, is read by the BAFTA award-winning actor, director and author, Andy Serkis. 

 In a sleepy village in the Shire, a young hobbit is entrusted with an immense task. He must make a perilous journey across Middle-earth to the Cracks of Doom, there to destroy the Ruling Ring of Power – the only thing that prevents the Dark Lord Sauron’s evil dominion.

Thus begins J. R. R. Tolkien’s classic tale of adventure, which continues in The Two Towers and The Return of the King.

The Fellowship of the Ring, By J. R. R. Tolkien, Read by Andy Serkis

Host Meg Wolitzer presents three works about women who defy the status quo and might therefore be perceived as “dangerous.”  In Margaret Atwood’s “Unpopular Gals,” fairy-tale archetypes reclaim their power. The reader is Ann Harada.  A boisterous and brilliant student threatens to upend the order of her high school in Shanteka Sigers’ “A Way with Bea,” performed by Pascale Armand.  And a Victorian-era wife fights for her sanity in Charlotte Perkins Gilman’s classic “The Yellow Wallpaper,” performed by Carrie Coon.  The show also includes commentary by the Egyptian-American journalist and activist Mona Eltahawy.

Dangerous Women

Daniel Nieto est activement recherché. Les enquêteurs ont identifié le Marseillais en cavale. Dans un geste désespéré, il donne un dernier rendez-vous à Giovanna Amati le 24 mai 1978...

Enlèvement à l’italienne
Rome. Fin des années 1970. Daniel Nieto, un braqueur marseillais, est en cavale dans l’Italie des années de plomb où le rapt est une véritable industrie. Il devient kidnappeur et enlève la fille d’un riche industriel du cinéma. Une relation contre-nature se noue entre l’otage et son geôlier. Elle sera fatale à ce dernier.

Une série documentaire de Marine Vlahovic
Enregistrements: Août 2020 à mai 2022 - Entretiens: Marine Vlahovic - Montage : Marine Vlahovic et Sara Monimart - Réalisation : Marine Vlahovic et Samuel Hirsch - Musique originale & mixage: Samuel Hirsch - Illustration : Zaven Najjar - Production : Arte Radio

Enlèvement à l'italienne (7/7): Nous nous sommes tant aimés

Obedear (Outro) [Prod. By ROME]

The diary of a girl in the 1980s who-- forbidden to go out on dates-- resorts to finding love via the phone. To learn more about Mortified visit getmortified.com.  The Mortified Podcast is a proud member of Radiotopia from PRX. 

02 Sakena: Mr. Telephone Man

Wenn es euch gefällt bitte abonnieren. ;-)

Please subscribe if you like it. ;-)

Thank you for listening!

Neodym - New

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)

In this episode of the O’Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies.

Building Intelligent Applications with Deep Learning and TensorFlow

BLOOD

Scientist.

Un soffio carico di pensieri
percorre il mio senso privato del dolore.

Iroshi Ito - Digital Sound
S. Zafarana 2012

foto di Kuno Ichi

Shunch'

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.

Building Intelligent Applications with Deep Learning and TensorFlow

O'Reilly Radar

Related tracks