This talk will highlight some of Google Brain’s research and computer systems with an eye toward how it can be used to solve challenging problems, and will relate them to the National Academy of Engineering's Grand Engineering Challenges for the 21st Century, including the use of machine learning for healthcare, robotics, and engineering the tools of scientific discovery. He will also cover how machine learning is transforming many aspects of our computing hardware and software systems.
Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → goo.gle/2LkBwCF
TensorFlow at Google I/O 2019 Playlist → bit.ly/2GW7ZJM
Google I/O 2019 All Sessions Playlist → goo.gle/io19allsessions
Learn more on the I/O Website → google.com/io
Subscribe to the TensorFlow Channel → bit.ly/TensorFlow1
Get started at → tensorflow.org/
Speaker: Jeff Dean
Deep Learning to Solve Challenging Problems (Google I/O'19) Video Statistics
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I train my models on GpuClub com and don't worry about maintaining these huge machines. No investment is the best investment...
Thank you a lot for the talk given. The idea of ML automation sounds great.
@8:30 the same technic used by naruto when he was training using his many clones :)
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Nice classes sir I want to neads thisclasses
machine learning is taking machine learning experts' jobs. :-)
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We have two ears and one mouth so that we can listen twice as much as we speak.
Great talk. One thing I hear being said too much, though, is that humans don't get to pool their experiences whereas robots do. I'm sure the efficiency and integrity of robots sharing knowledge is much higher than with humans, but shared knowledge amongst humans is the basis of civilization. There would be no Google if every person ever born had to learn from scratch. Rant over.
This is awe inspiring I love it thank you Google Recommendable!
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With reference to scientific learning: When you have a lot of data, but no data in the particular point in parameter hyperspace that you are interested in, what do you do? Extrapolate the model will result in bias an loss of accuracy. Experiments in real world systems seems unavoidable , experimental datapoint that is ofthen very expensive. The interaction between Machine Learning modeling and the planing and execution of experiments seems to be new and very interesting research area.