Facebook Trains ImageNet in 1 Hour
Facebook published a paper today detailing how they are able to train nearly 1.3 million images in under an hour using 256 Tesla P100 GPUs that previously took days on a single system.
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Facebook published a paper today detailing how they are able to train nearly 1.3 million images in under an hour using 256 Tesla P100 GPUs that previously took days on a single system.
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This is a guest post by Dana Van Aken, Andy Pavlo, and Geoff Gordon of Carnegie Mellon University. This project demonstrates how academic researchers can leverage our AWS Cloud Credits for Research Program to support their scientific breakthroughs. Database management systems (DBMSs) are the most important component…
Today we’re rolling out new features in Sheets that make it even easier for you to visualize and share your data, and find insights your teams can act on.
With just three stones on the board, it was clear that this was going to be no ordinary game of Go.
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In a previous post, we described the details of NSynth (Neural Audio Synthesis), a new approach to audio synthesis using neural networks. We hinted at furthe...
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
Sketch-RNN, a generative model for vector drawings, is now available in Magenta. For an overview of the model, see the Google Research blog fromApril 2017, ...
Posted by Zak Stone, Product Manager for TensorFlowResearchers require enormous computational resources to train the machine learning (ML) models t...
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
Mapillary Vistas, the world’s largest and most diverse publicly available, pixel-accurately and instance-specifically annotated street-level imagery dataset, will empower autonomous mobility and transport at the global scale.
After open sourcing Caffe2 at F8 last month, today we are are excited to share our recent work on low precision 16 bit floating point (FP16) training in collaboration with NVIDIA.
Posted by Jennifer Daniel, Expressions Creative Director, Allo Taking, sharing, and viewing selfies has become a daily habit for many — the car sel...
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Meet Fathom, a mini-language for writing semantic extractors, that you can use client- or server-side to extract meaning from the content of a web page. Scoop up all those ideas ...
We’re on a mission to keep the magic of human connection alive. Learn more about life at Tinder and the many ways we're igniting our impact—for our employees and the millions of people seeking connection on our app.
Posted by Florian Kainz, Software Engineer, Google DaydreamOn a full moon night last year I carried a professional DSLR camera, a heavy lens and a ...
We’re adding the ability for multiple people to connect their account to one Google Home. So now with the Google Assistant you can hear your commute time, your daily bri…
Posted by Ce Liu, Michael Rubinstein, Mike Krainin and Bill Freeman, Research ScientistsYesterday, we released an update to PhotoScan, an app for i...
Training and deploying AI models is often associated with massive data centers or super computers, with good reason. The ability to continually process, create, and improve models from all kinds of information: images, video, text, and voice, at massive scale, is no small computing feat. Deploying these models on…