HN user
Sceptical. Performs really well on 1000 docs. Let’s see it for real..! No model supplied.
https://github.com/herniqeu/extract0
To quote Mulder: I want to believe.
Well that was a fascinating diversion. This is bonkers!
What beats M-Disc? Genuinely curious just having bought one.
For anyone else wondering what CIA is:
https://www.techtarget.com/whatis/definition/Confidentiality...
I recently took on a new, super complex project and can attest to Obsidian beyond useful. I’ve put all notes and info into it, everything.. and it’s really paid off. I now see this process rather like unit testing whereby the structure of the notes mirrors your own thoughts but with a better, searchable memory.
Can concur. This works for me when faced with some tricky code.
Same. :|
Reduce / Recycle / Reuse
Tell me which of those is going to make the situation worse?
Well.. for both your points here there are no guarantees. Progress isn't linear and just because we can adapt doesn't mean we will.
What is known is that our entire eco-system is failing to adapt fast enough to keep up with the rate of change - what makes you think we can beat it?
Its disappointingly reductive to throw the towel in and, really, that point of view is no better than 'the world is ending in 12 years'
To deal with a problem you must first acknowledge that it exists - any action is clearly going to beat sticking ones head in the sand.
What about their children and their children's children and ... (you get the idea)
It s a bit myopic no?
Link to Uber Engineering page on this: https://eng.uber.com/go-explore/
From the linked page:
To enable the community to benefit from Go-Explore and help investigate its potential, source code and a full paper describing Go-Explore will be available here shortly.
You haven't met Serena yet then...
Yes, see: https://www.coursera.org/learn/machine-learning
It starts out reaaaally basic but give a thorough grounding of the maths and the intuition behind it.
Coursera - Andrew NG's course => Classic starting point, very thorough and digestable introduction to Neural Networks. I found he covered the 'how the heck do I use this?' rather well.. :)
From there, Coursera has a paid(?) DL course by Andrew NG or there's Fast.ai which looks good.
Good luck!
www.nengo.ai
wow, this is really cool! thx for sharing :]
Sounds interesting, any papers that I/we should read..?
'rollouts' ELI5? I didnt pick this up from the paper..
thx :)
+1 See it in 70mm if you can. :>
oO that's so awesome!
+1 !
The only winning move is not to play.
Shout out to the author, I'm enjoying this content thankyou!
How does the engine know the direction the converge will take? I'm missing something here.. :?
~7000 elements per compound eye according to this page:
https://www.google.co.uk/amp/s/brookfieldfarmhoney.wordpress...
I strongly recommend running as a burnout cure...
If you're getting started I totally recommend Andrew NGs course...
Nice discussion, but where's the example src code?
This may help someone out there. I had chronic tennis elbow for years, tried a huge array of options to fix it and failed... until: I found a _vertical_ mouse.
Turns out it was a micro-injury thing...
GFX HEAVY
did you apply Deep Learning / ML techniques here? ;)