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Fascinating stuff, thanks

amazing work

Great

perfect profoser

Hello thank you for this video is there a possibility to get the article ?

Bro I'm planning to do my master's in Cyber Security. Will this topic be helpful when I'm covering the syllabus?

Thanks for sharing

So so abstract

Thank you for the video! Listening while working!

Interesting. Where can I read the work that lies behind the figures at about 12 min in?

Insightful

wondering how easy it is too disable these drones and take the packages . Africa a bad country for theft .

52:10 Hello TH-cam privacy :)

Thanks for uploading this video, I always love a behind the scenes look with more Eng and Tech talk.

How to involve data variations caused by power flow change?

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Predecting high resolution results using ML+ low resolution mesh seems interesting to me

How do I get hired?

So you're saying diversity is not a strength?

Do we need historical data to represent our states for reinforcement learning agent?

The Earth is cooler with the atmos/GHGs/albedo not warmer. To perform as advertised the GHGs require “extra” energy upwelling from the surface radiating as a black body. (th-cam.com/video/0Jijw7-YG-U/w-d-xo.html) The kinetic heat transfer processes of the contiguous atmos molecules render that scenario impossible. No greenhouse effect, no GHG warming, no man/CO2 driven climate change or Gorebal warming.

Very positive and well proud of his company he will have big success for what he enjoy doing.

These guys are inspiring many in Rwanda

Guy seems like a Neo-Gastonist

Nice!

Bird strikes?

Are the slides available anywhere for this workshop ? Thanks for any suggestions.

I am a PhD candidate at the University of Kentucky, USA. I am interested in extending these ideas to power system distribution state estimation. Are you aware of any research in this area? If so, please let me know where I may find any papers and/or presentations in this area. So far I have applied feed forward fully connected multi-layer perceptron and convolutional neural networks. This discussion helps explain limitations of these two fully data driven approaches. Any information would be greatly appreciated! I will definitely be following your work going forward.

Great discussion! Are slides available for this presentation?

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Stupid as all hell

the first question is purely from a scoreboard chaser, she might believe Einstein should get GRT empirically proved before he can publish his work.

Interesting presentatin! Where could I download the slides?

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İts sooo boreed.I'am never loss my time.

A new complete manuscript about the presented work can be found: arxiv.org/abs/2105.10446

Good presentation. THX

Changing the world by unlocking endless impossibilities. Great work

Great job!

uhmm. uhmm

It's so inspiring! Go Zipline

That's great work and technology.......

Amazing and beautiful!!

Awesome 👏🏽 information in this digital age 🥂👀

You can throw sparse mutations at GPUs that have the full neural model and part of the training data each. Get the costs back and then send a global accept or reject message. There is not much data movement. Continuous Gray Code Optimization mutations are good. Anyway if you need very fast nets then fast-transform fixed_filter_bank nets are an option.

You can't get overfitting in large networks so easily. If one neuron over expresses that may correct errors related to one forward dot product but it will make the errors related to many other connected forward dot products worse. The training algorithm will stomp on the over expressing neuron. Hence no neuron can be exceptional and the search is limited to small subset of statictical solutions that require neurons to work in groups. You can see this very clearly in fast transform fixed-filter-bank neural networks.

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