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Martin Görner
Works at Google
Lives in Paris, France
5,119 followers|1,503,712 views
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Martin Görner

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Concurrency is hard. Go makes it easier. And a drawing makes anything simpler to explain, including concurrency patterns in GO! #golang
Let's take a look at common concurrency patterns and visualize the way they look using Go and WebGL.
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Martin Görner

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I work for Google Cloud Platform. I am not allowed to say these things. Customers are. #GCEvsAWS
Foreword This story relates my experience at a typical web startup. We are running hundreds of instances on AWS, and we’ve been doing so for some time, growing at a sustained pace. Our full o…
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Is easy for me to see  in todays World the symptomatic submissiveness to the corporative slavery, even within United Estate of America , where americans are loosing more and more of their freedom and rights....and the freedom of speech , in order to keep a job and bring some food to the table, to satisfy de dark agenda that is behind the Cartel of Washington DC and the NWO interests that is pulling the strings on everything. No doubt Google is working for the NWO.
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Martin Görner

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This is beautiful. And even more beuatiful is how it is done.
DeepArts are beautiful! Using deepart.io, we can turn photos into beautiful pieces of art. [gallery size=medium link=none columns=2 ids=71,73] But what if we want to keep the beautiful colors in the photograph and still draw it in the style of the great Picasso? So far this wasn't really possible, since our style transfer algorithm would also transfer the colors from the style image. Well, we fixed that. Together with Aaron Hertzmann and E...
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Teaser super-sympa pour DevFest Nantes. J'💙💚💜
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Indeed! Trop fort le logo GDG façon lego.
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Liquid sharding, the secret sauce in Google Cloud Dataflow. A worker can dynamically shed part of its workload to the rest of the pool. And do not forget you are computing exact results on Dataflow (aggregations, parallelDo's, ...) so this not so easy to implement without losing a single piece of data.
 
https://cloud.google.com/blog/big-data/2016/05/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow
Finally posting publicly about some of the most powerful secret sauce in Cloud Dataflow we've been working on!
Cloud Dataflow takes care of stragglers with dynamic work rebalancing, a feature that maintains a near optimal parallelization of the work across workers
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Lots of water in Paris today. Some of these pictures do not show the Seine river but its riverbanks, where you can usually walk. "Les péniches" are moored to underwater docks and riverbank parties are cancelled.
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I'm speaking at Pycon Russia tomorrow, about Tensorflow and deep learning. But before that, I would like to share with you a couple of pictures of Moscow.

All pictures taken with a Nexus6, often with the HDR+ mode, postprocessed on Google photos.
57 new photos · Album by Martin Görner
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On va commencer par accord de confidentialité sebastien lablanche
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Le RER et les trains de banlieue enfin sur Google Maps! Merci +Google France et +SNCF. Ils ont même fait une video pour célébrer l'événement: https://youtu.be/-TTaQLpzIQ8
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j'aime google et merci a eux
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Impressive setting for Google's GCP NEXT cloud conference in Amsterdam. (Don't forget #Tensorfow at 4pm, that's my session 😉).
The most impressive keynote demo was an on-premise + in the cloud simultaneous multi-zone deployment of an app using Kubernetes, while it was in production serving 40K QPS.
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On the borders! If you want to slide a 4x4 patch on a 28x28 image, you need to make the image larger by 1 or 2 pixels on all sides to have 28 possible positions for your patch in each direction.
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There are so many parallel processing technologies today: MapReduce, Spak, Dataflow, Flink, Storm ... Why is it so? In his Strata session, +Tyler Akidau deconstructs 10+ years of scientific advances in parallel processing and data streaming technologies and explains the insights that shaped the construction of Google Cloud Dataflow / Apache Beam.
Drive
The Evolution of Massive-Scale Data Processing -- Strata London 2016 [Public]The Evolution of Massive-Scale Data Processing Tyler Akidau Staff Software Engineer @takidau With slides by Frances Perry (@francesjperry) and Kenn Knowles (@kennknowles). 1 https://goo.gl/VlVAEp A high-level tour of MapReduce, Apache Hadoop, Google Flume, Apache Storm, Apache Spark, MillWheel, G...
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We have it working for ⚽, 💻 and 🏔. What else?
By the way, the underlying machine learning goodness is available to all app developers as the Google Cloud Vision API.
 
Haha! It totally works. XD
Search with emojis. Didn't believe until I tried myself.

h/t +Stefan Hoth +Martin Görner 
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Currently
Paris, France
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Berlin, Deutschland - Bristol, United Kingdom - Košice, Slovak Republic
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Google Developer Relations
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    Developer Relations, 2011 - present
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