We love seeing all of the NVIDIA GPU-related tweets – here’s some that we came across this week:
How many Jetsons are too many Jetsons? Correct ans = number of Jetsons you own + 1. @nvidia @nvidiadeveloper @GPUComputing #DeepLearning pic.twitter.com/7ALPHBWqq0
— Shreyas Skandan (@ShreyasSkandan) May 27, 2017
Our mission is to make #healthcare accessible and affordable using #deeplearning.Thank you Nvidia and @Netexplo for this recognition. pic.twitter.com/dm8WyjJM4m
— Qure.ai (@qure_ai) May 29, 2017
with a little tinkering, i got #CUDA 8.0 and #cuDNN 5.1 to work like a dream on my #windows machine. #deepLearning #rhymes #nvidia
— qqq (@gtfoDelta) June 1, 2017
https://twitter.com/stevoelreevo/status/870321010444128256
https://twitter.com/xrb/status/870328933400674306
https://twitter.com/anasvaf/status/869702516749078530
A $5000 GPU might sound outrageous at first but it can reduce your training time by 80x.
— Gaurav Singh (@sgaurav_baghel) May 29, 2017
Ok now that @GIGABYTEUSA x299 is out I'm going to plan my build. 18-core i9 with @nvidia Titan Xp for deep learning and creative.
— Bob Warren (@augmentedjs) May 31, 2017
Supercharging @PeptoneInc with @nvidia #gpu for #ai #deeplearning #biotech pic.twitter.com/BiDTK4Ryru
— Kamil Tamiola, PhD (@KamilTamiola) May 29, 2017
Under-appreciated fact: NVIDIA now has three deep learning architectures:
– CUDA (FP32)
– Tensor Unit (FP16/32)
– DLA (FP16/INT8)— James Wang (@draecomino) May 26, 2017
https://twitter.com/joacimstahl/status/870023854470647809
