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@foxtel in Australia has chosen @GoogleCloud_ANZ to build out their cloud architecture and shift data warehousing to public cloud. Thank you @camjo89 for sharing the journey with #GoogleCloudNext lnkd.in/g4_c8B4
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This is so cool. + open source. @netflix 👏
"Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more." 🤯
medium.com/netflix-techblog/…
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Federated learning enables many devices to collaboratively train a global ML model without centralizing their data. Check out a comic book style introduction to FL, and dive deeper into details at g.co/federated
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Deep learning via category theory and functional programming .. github.com/bgavran/Compositi…
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In collaboration with colleagues at @OpenAI, we're publishing "Exploring Neural Networks with Activation Atlases", a new @distillpub that describes a technique to help determine what image classification neural networks "see" when provided an image. goo.gl/bakQE7
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Yesterday we announced TensorFlow Federated: an open framework for federated learning experiments and other computations on decentralized data. We are excited to put this framework in the hands of TensorFlow users! #TFDevSummit
More info on the blog ↓ bit.ly/2Tys8hG
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Ladies and gentlemen, the Death of Tagless Final 😂 github.com/scalaz/scalaz-zio…
This looks massive. Need some time to understand this.
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In support of the research community and to encourage reproducible research efforts, we have open-sourced Lingvo, a general deep learning @TensorFlow framework with a focus on language-related tasks.
Lingvo is a deep learning framework used for sequence modeling tasks like machine translation, speech recognition, and speech synthesis.
Learn more here ↓ bit.ly/2TgNsYC
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In collaboration with @DeepMindAI, we introduce the Deep Planning Network (PlaNet), a purely model-based agent that learns a world model from image inputs and successfully leverages it for planning. Learn more and find the source code at ↓ goo.gl/Ckaiof
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Introducing Natural Questions, a new, large-scale corpus and challenge for training and evaluating open-domain question answering systems, and the first to replicate the end-to-end process in which people find answers to questions. Learn more at ↓ goo.gl/bfqJ7a
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Here are the slides of my talk types vs tests at #scalax julien-truffaut.github.io/ty…
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Cool feature (1) from @ApacheFlink 1.7:
**Temporal Table Joins**
Join a stream of events against a table, for each event taking the table as it would have looked at the (event) time of that event.
Example: Joining orders against currency rate changes.
teapawt.net/ApacheFlink/status/106…
Apache Flink 1.7.0 is out with many great features such as Scala 2.12 support, exactly-once S3 file sink, pattern matching/CEP with Streaming #SQL! Check the release announcement to stay up to date: flink.apache.org/news/2018/1…
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We have hunted through the internet to find the most appealing #Scala, #Haskell & #FP reads for you. Hear from @apnylle, @propensive, @brianclapper, @adamgordonbell, @odersky & @CamJo89.
So take a few minutes out & settle down with our #SundayReads 📚 ow.ly/l0C030mJ7Sk
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All the way from Australia, @CamJo89 discussed "The Cost of Abstraction" on his Scala Up North talk. Check it out here: youtu.be/d5OiukU26Mw
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It's still possible to change the world with just a decision tree and a basic understanding of data science.
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Best paper award at #recsys2018 main idea: Fusing new content generation & recommendation. eg. what *new* movies should be made & who should they be recommended to? Presents a joint problem
formalization. dl.acm.org/citation.cfm?doid…
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Excited to see the @nvidia launch of @rapidsai, end-to-end data science on the GPU, powered by @ApacheArrow! rapids.ai/
Gave my talk on "The Cost of Abstraction" at #sun2018. Huge thanks to @japgolly for the initial benchamrk code inspiration and @jedws for the pointers and feedback. Legends! And thanks to @ScalaUpNorth for having me along!
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Deep Learning is getting really good on Big Data/millions of images. But Small Data is important too. Am seeing many exciting applications at Landing AI where you can get good results w/100 images. Hope more researchers work on Small Data--ML needs more innovations there. – at Palo Alto, CA