awesome-deep-learning-papers

Maybe one of the various Github "Awesome XYZ" lists that exists out there. https://github.com/terryum/awesome-deep-learning-papers https://endymecy.github.io/awesome-deeplearning-resources/ You might also track down the website for a DL class from a reputable university and mine the syllabus for a list of assigned readings. Do that for two or three such instances and you could probably come up with a pretty solid list. Or may just ask Carmack to share the list he was referring to? Does anybody know if he responds to Tweets / Twitter DM's, etc? EDIT: Also, check out https://paperswithcode.com/ EDIT 2: Also, not reading, but.. Andrej Karpathy has a Youtube channel now and has been putting up some videos on ML topics. Might be of interest? https://www.youtube.com/c/AndrejKarpathy

You can start here: https://github.com/terryum/awesome-deep-learning-papers

It would be nice if there was a readme for this repository. Like these AI and speaker recognition examples [1],[2]. [1] https://github.com/wq2012/awesome-diarization [2] https://github.com/terryum/awesome-deep-learning-papers

It's not a zero-sum game of open jobs to potential employees. If you're able to interact with the smartest people in the world - and not just your own country - you end up forming more effective and larger companies. Which in turn create jobs. If you shut out your country to the rest of the world, the next Google will be founded somewhere else. And I'll dispute your claim that the US has enough talent. There was a submission on HN recently about the top deep learning papers. Go through it. Count the number of American-born people. Geoff Hinton? Yann LeCun? Russ Salakhutdinov? Demis Hassibis (of Deep Mind)? Virtually everyone on there was born somewhere else. They're not Americans, but they're mostly working for American companies and contributing their talent to YOUR economy. [0] https://github.com/terryum/awesome-deep-learning-papers/bl

I've been spending my free time getting myself familiar with ML and personally I'm focused on resharpening my math skills, reading papers from the "awesome deep learning papers" repo [1], playing with TensorFlow, and reading the Neural Networks and Deep Learning book [2]. I did my undergrad degree in math, so a lot of the math is just review for me, but ML seems to get fairly math heavy pretty quick. I would suggest anyone looking at ML to spend a good amount of time going through the backing math in addition to the CS parts, otherwise you might not develop much intuition for what's going on. [1] https://github.com/terryum/awesome-deep-learning-papers [2] http://neuralnetworksanddeeplearning.com/