a.) Given that my IoT endpoints will be limited in capabilities due to size, cost, and the power requirements, how can companion computing that is either embedded in the larger system or in a companion gateway help me?
b.) How can knowledge that is created on a given device be exported and used in many other locations?
c.) How will machine learning (with a small “m.l.”) affect behaviour at the edge?
sensors have to be connected to
a smartphone or to the cloud
to perform any useful classification
Sensor technology needs to be:
connects to the LPWAN and accepts communications back and forth across the network
Photo Credits: http://www.silicon.co.uk/e-innovation/nvidia-jetson-tx2-206831
G2 GPU instance
a full-featured development platform for visual computing embedded applications
256 Cuda Cores
32 GB storage
Ethernet, WLAN, Bluetooth!
Too Expensive @ $500??
for image classification, segmentation and object detection
DIGITS is available as a free download to the members of the NVIDIA Developer Program. If you are not already a member, clicking “Download” will ask you join the program.
DIGITS is available as a Amazon Machine Image (AMI) for on-demand usage. Get started instantly by clicking the button below. Visit the GPU-accelerated cloud images to learn more. (DIGITS 5 AMI coming soon)
Deep Learning Classes and Courses
NVIDIA Deep Learning Institute offers self-paced training and instructor-led workshops
CS229: Machine Learning by Andrew Ng (Baidu)
Deep Learning at Oxford by Nando de Freitas (University of Oxford)
Neural Networks for Machine Learning by Geoffrey Hinton (Google, University of Toronto)
Deep Learning for Computer Vision by Rob Fergus (Facebook, NYU)
Learning From Data by Yasser Abu-Mostafa (Caltech)
Deep Learning Posts on the ParallelForAll technical blog