Machine Learning & Neural Network

[changtimwu]

http://colah.github.io/ https://iamtrask.github.io/ http://www.slideshare.net/tw_dsconf/ss-62245351 great point to start with

[changtimwu]

worthes to read http://highscalability.com/blog/2016/3/16/jeff-dean-on-large-scale-deep-learning-at-google.html google deep learning class https://www.udacity.com/course/viewer#!/c-ud730/l-6370362152/e-6406301860/m-6374107827

[changtimwu]

CNN in chip

• project: http://www.mit.edu/~sze/eyeriss.html
• paper: http://www.rle.mit.edu/eems/wp-content/uploads/2016/02/eyeriss_isscc_2016.pdf
• slide: http://www.rle.mit.edu/eems/wp-content/uploads/2016/02/eyeriss_isscc_2016_slides.pdf

[changtimwu]

fundamental knowledge like Linear Algebra excellent starting points. http://machinelearningmastery.com/linear-algebra-machine-learning/ https://www.youtube.com/playlist?list=PLnnr1O8OWc6boN4WHeuisJWmeQHH9D_Vg https://www.khanacademy.org/math/linear-algebra

learn: https://www.youtube.com/channel/UCMoXOGX9mgrYNEwpcIQUcag

[changtimwu]

binary study https://github.com/MatthieuCourbariaux/BinaryConnect http://arxiv.org/pdf/1602.02830v3.pdf https://github.com/MatthieuCourbariaux/BinaryNet https://github.com/Lasagne/Lasagne

[changtimwu]

BP basics http://mnemstudio.org/neural-networks-backpropagation-xor.htm http://cs231n.github.io/optimization-2/

http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

[changtimwu]

binaryconnect in ASIC

https://arxiv.org/pdf/1606.05487v1.pdf

http://www.pulp-platform.org/core-team/

another http://iis-projects.ee.ethz.ch/index.php/Bringing_XNOR-nets_(ConvNets)_to_Silicon

[changtimwu]

binary RNN https://arxiv.org/abs/1608.06902

chord identification https://very-deep-flagon.quora.com/Audio-chord-identification-using-neural-networks-part-1

[changtimwu]

https://developer.qualcomm.com/blog/matrix-multiply-adreno-gpus-part-1-opencl-optimization https://developer.qualcomm.com/blog/matrix-multiply-adreno-gpus-part-2-host-code-and-kernel

accelerated with renderscript https://arxiv.org/pdf/1611.07151v1.pdf

quite a lot to see https://github.com/mtmd/

[changtimwu]

https://community.arm.com/docs/DOC-11763

[changtimwu]

nice blog: http://zyddora.github.io/ eigen: http://eigen.tuxfamily.org/index.php?title=Main_Page ne10: https://community.arm.com/groups/android-community/blog/2013/12/18/projectne10-fft-is-updated

[changtimwu]

QNN! https://arxiv.org/pdf/1609.07061v1.pdf more accurate

[changtimwu]

http://fpl2016.org/#programme

good survey site: http://hgpu.org/