TensorFlow
In this episode of TensorFlow Meets, Laurence Moroney sits down with Jeff Dean, a Google Senior Fellow working in the area of Machine Intelligence Engineering. Laurence taps into Jeff’s insights about machine learning (ML) and how it’s impacting many different engineering domains and scientific domains in general. Jeff Dean and his team have conducted research on how to use ML to tackle quantum chemistry problems at ~300,000x the speed of traditional methods. From broadening research horizons to decreasing the cost of solar energy and increasing the efficiency of health care systems, ML has massive potential to solve global problems. Subscribe to the channel and stay tuned for more TensorFlow Meets!
TF Dev Summit ’18 Keynote w/ Jeff Dean → https://goo.gl/k81f5N
TensorFlow Meets Playlist → https://goo.gl/Wy3DSc
Subscribe to the TensorFlow channel → https://goo.gl/ht3WGe
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what is the difference between machine learning and artificial intelligence for newbs
I appreciated the energy and passion Jeff brings to the interview.
Machine learning can solve all problems???
When will the next update to SyntaxNet coming up? I wanted to use that tool for a project 1.5 year ago, But I was not able to use it as I found it lacking a lot such as
1. Python 3 Support
2. Easy installation
3. Better Doc
4. Better they split it from TF package such as TF-SyntaxNet
5. DRAGNN Doc
When can we expect more about SyntaxNet
Where can we find Jeff's talk?
I've heard a lot of machine learning when it comes down to image processing but what about other sets of data such as audio, 3d imagery, etc?
How long until we have Slap-drones? They'd solve most of humanities problems. Even virtual ones that could be assigned to internet based user accounts would make a huge difference. Imagine that, every time some clown posts "First!" they get a virtual slap.
One question I’ve had is what aspects of small business could ML be most applicable to, specifically for businesses working without a lot of data or data collection practices. What are areas to focus on in small business to find ML applicable problems, and how should you go about implementing data collection?
Where can I get the Tensorflow jacket or shirt?
Plan Disease Detection is also our specialty: plantix.net ! Powered by TensorFlow of course!
I have one and only one question. Whether we have to make seperate algorithm and model for each problem rather than using the existing one like cnn, rnn so and so?