Deep learning with ZNN Aleksandar Zlateski and Kisuk Lee October 20, 2014 12pm seminar 1:30pm hands-on session Princeton Neuroscience Institute 159 Aleks will describe ZNN, his multicore CPU implementation of deep learning for 2D and 3D convolutional networks. He will present preliminary benchmarks showing that ZNN is competitive with GPU-accelerated deep learning (currently the dominant approach). The use of CPUs avoids the limited RAM of GPUs, and reduces software development cost. Kisuk will present a how-to tutorial on using ZNN, illustrated with the application of boundary detection in 3D nanoscale brain images. The talks will be followed by a hands-on session including installation of ZNN and application to an image segmentation challenge (http://brainiac2.mit.edu/isbi_challenge/). Participants should bring their own laptops, and will require access to their own Linux machine. Participants are encouraged to install ZNN (https://github.com/seung-lab/znn-release) prior to the event, as well as the boost and fftw libraries. RSVP tosseung@princeton.edu, as food will be provided.