IEEE Consumer Electronics Magazine - April 2017 - 56

which was never quite good enough to make its way out of the
laboratory and into everyday use. This year, however, we have
seen several launches of "smart speakers" that can control your
home-and behind these new devices lies a web of deep learning technologies, both analyzing your needs, translating your
voice requests and marshalling the necessary logistics to deliver everything to your doorstep or onto your television set.
To get started with solving your own problem, you'll
need a state-of-the-art GPU-the same technology going
into the latest gaming personal computers-and most of the
core software is freely available on the Internet. There are
several software packages trending in the deep learning
field, including but not limited to Theano (on Python), Lasagne (on Theano), Tensorflow (on Python and C++), Caffe
(on Python and MATLAB), and MatConvNet (on MATLAB).
The good news is that all of these tools are relatively inexpensive and readily available!
Finally, you'll need a large data set-for your particular
application we'll assume you have your own specialized data
sources, but there are many public sources of suitable data.
From here on, it's "deep oceans of learning ahoy!"

aBout the authors
Joseph Lemley ( jlemley@fotonation.com) is a Ph.D. student
with the Center for Cognition, Connected, and Computational Imaging, National University of Ireland, Galway.
Shabab Bazrafkan (sbazrafkan@fotonation.com) is a Ph.D.
student with the Center for Cognition, Connected, and Computational Imaging, National University of Ireland, Galway.
Peter Corcoran (dr.peter.corcoran@ieee.org) is a professor of electrical engineering in the College of Engineering
and Informatics, National University of Ireland Galway. He is
the emeritus editor-in-chief of IEEE Consumer Electronics
Magazine and an IEEE Fellow.

reFerenCes
[1] B. Yegnanarayana, Artificial Neural Networks. New Delhi, India: PHI
Learning Pvt. Ltd., 2009.
[2] S. Lawrence, C. L. Giles, and A. C. Tsoi, "What size neural network
gives optimal generalization? Convergence properties of backpropagation," Univ. Maryland, College Park, Tech. Rep. UMIACS-TR-96-22 and
CS-TR-3617, 1996, pp. 1-37.
[3] Y. Chauvin, "Generalization performance of overtrained backpropagation networks," in Neural Networks, New York: Springer, 1990, pp. 45-55.
[4] S. Theodoridis and K. Koutroumbas, "Pattern recognition and neural
networks," in Machine Learning and Its Applications, New York: Springer, 2001, pp. 169-195.
[5] P. Lewis. (2007, Feb. 22) Analogy between human and artificial neural nets. [Online]. Available: http://users.ecs.soton.ac.uk/phl/ctit/nn/
node2.html
[6] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent
in nervous activity," Bull. Math. Biophys, vol. 5, no. 4, pp. 115-133, 1943.
[7] F. Rosenblatt, "The Perceptron: A perceiving and recognizing automaton," Cornell Aeronautical Laboratory, Buffalo, NY, Report 85, 1957, pp.
460-461.
[8] A. McWilliams, "Smart machines: Technologies and global markets,"
BCC Research, Wellesley, MA, Rep. IAS094A , May 2014.
[9] G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Comput. Electr. Eng, vol. 40, no. 1, pp. 16-28, 2014.

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[10] (2013). Convolutional Neural Networks (LeNet). [Online]. Available: http://deeplearning.net/tutorial/lenet.html
[11] D. A. Clevert, T. Unterthiner, and S. Hochreiter, "Fast and accurate
deep network learning by exponential linear units (ELUs)," presented at
the 2016 Int. Conf. Learning Representations (ICLR 2016), San Juan,
Puerto Rico. doi: abs/1511.07289.
[12] J. Schmidhuber. (2014). Who invented backpropagation? [Online]. Available: http://people.idsia.ch/~juergen/who-invented-backpropagation.html
[13] V. Badrinarayanan, A. Kendall, and R. Cipolla. (2015, Nov.). SegNet:
A deep convolutional encoder-decoder architecture for image segmentation. [Online]. Available: https://arxiv.org/abs/1511.00561
[14] K. Krewell. (2009). What's the difference between a CPU and a
GPU. [Online]. Available: https://blogs.nvidia.com/blog/2009/12/16/
whats-the-difference-between-a-cpu-and-a-gpu/
[15] Nvidia. Geforce GTX 10-series notebooks. [Online]. Available:
http://www.geforce.com/hardware/10series/notebook
[16] Nvidia. NVIDIA TitanX graphics card with Pascal. [Online]. Available: http://www.geforce.com/hardware/10series/titan-x-pascal
[17] Nvidia. NVIDIA cuDNN. [Online]. Available: https://developer
.nvidia.com/cudnn
[18] Berkeley Vision and Learning Center. Caffe. [Online]. Available:
http://caffe.berkeleyvision.org/
[19] Theano Development Team. Theano. [Online]. Available: http://
deeplearning.net/software/theano/
[20] TensorFlow. TensorFlow is an open source software library for
machine intelligence. [Online]. Available: https://www.tensorflow.org/
[21] TensorFlow. Mobile TensorFlow. [Online]. Available: https://www
.tensorflow.org/mobile.html
[22] N. Lenke. (2016, Aug. 16). Why we're using Deep Learning for our
Dragon speech recognition engine. [Online]. Available: http://whatsnext
.nuance.com/in-the-labs/dragon-deep-learning-speech-recognition/
[23] Nvidia. (2016, July 29). The technology behind the viral Prisma
photo app. [Online]. Available: https://news.developer.nvidia.com/thetechnology-behind-the-viral-prisma-photo-app/
[24] A. Galella. (2016, Jan. 5). SoundHound Inc. collaborates with
NVIDIA to bring Deep Learning-based natural language understanding
to cars. [Online]. Available: http://www.businesswire.com/news/
home/20160105006247/en/SoundHound-Collaborates-NVIDIA-BringDeep-Learning-Based-Natural
[25] T. Hoff. (2016, June 20). The technology behind Apple photos and
the future of Deep Learning and privacy. [Online]. Available: http://
highscalability.com/blog/2016/6/20/the-technology-behind-apple-photosand-the-future-of-deep-le.html
[26] R. Matus. (2016, Sept. 28). Neurala becomes largest supplier of
Deep Learning software running in real time on a drone. [Online]. Available: http://www.neurala.com/top-deep-learning-software/
[27] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D.
Erhan, V. Vanhoucke, and A. Rabinovich. (2014, Sept.). Going deeper
with convolutions. [Online]. Available: https://arxiv.org/abs/1409.4842
[28] Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep Learning face representation by joint identification-verification," in Proc. Advances in Neural Information Processing Systems, 2014, pp. 1988-1996.
[29] X. L. Zhang and J. Wu, "Deep belief networks based voice activity
detection," IEEE Trans. Audio. Speech. Lang. Processing, vol. 21, no. 4,
pp. 697-710, 2013.
[30] S. Bazrafkan, T. Nedelcu, P. Filipczuk, and P. Corcoran, "Deep
Learning for facial expression recognition: A step closer to a smartphone
that knows your moods," in Proc. IEEE Int. Conf. Consumer Electronics,
2017, pp. 236-239.
[31] J. Lemley, S. Abdul-Wahid, D. Banik, and R. Andonie, "Comparison of recent machine learning techniques for gender recognition from
facial images," in Proc. Modern Artificial Intelligence and Cognitive Science 2016, Dayton, OH, pp. 97-102.

http://www.deeplearning.net/tutorial/lenet.html http://people.idsia.ch/~juergen/who-invented-backpropagation.html https://www.arxiv.org/abs/1511.00561 https://blogs.nvidia.com/blog/2009/12/16/ http://www.geforce.com/hardware/10series/notebook http://www.geforce.com/hardware/10series/titan-x-pascal https://developer http://www.nvidia.com/cudnn http://caffe.berkeleyvision.org/ http://http:// http://www.deeplearning.net/software/theano/ https://www.tensorflow.org/ https://www http://www.tensorflow.org/mobile.html http://whatsnext http://www.nuance.com/in-the-labs/dragon-deep-learning-speech-recognition/ https://news.developer.nvidia.com/the http://www.businesswire.com/news/ http://http:// http://www.highscalability.com/blog/2016/6/20/the-technology-behind-apple-photos http://www.neurala.com/top-deep-learning-software/ https://www.arxiv.org/abs/1409.4842 http://users.ecs.soton.ac.uk/phl/ctit/nn/

Table of Contents for the Digital Edition of IEEE Consumer Electronics Magazine - April 2017