AI Software
Artificial Neural Networks
Presentation at AIBrain, Inc.
Jeff Shomaker
July 6, 2016
Introduction
• Neural network software has been open-sourced so it can
be used widely.
• I’ll discuss/demonstrate the following:
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Neural networks – what are they?
Uses of Neural Networks
TensorFlow
Torch
CNTK
Caffe
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Neural Networks
Neural networks are a paradigm for processing information
loosely based on the idea of neurons that communicate
information in the brain and spinal cord. 2)
Source: 1) Raschka, S. (2016). What is the difference between deep learning and ‘Regular’ machine learning.
www.kdnuggets.com, Diagram accessed 7-1-16. 2) Geoffrey Hinton, et al (2012). Neural networks for machine
learning course. U of Toronto, Coursera.com, Oct 2012. Accessed 2013.
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Examples of Neural Network (NN) Use
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Medicine
– Per IOM (Institute of Medicine, 2015) one of ten patient deaths in the US is due to misdiagnosis.
– NNs can be used in diagnosis of multiple sclerosis, colon cancer, pancreatic disease,
gynecological diseases, diabetes, coronary artery disease, breast/thyroid cancer and others. 1)
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Finance
– In 2014, card not present fraud was $2.9B in US – expected to be $6.4B by 2018.
– NNs can be used for credit card fraud detection along with other machine learning approaches
such as Support Vector Machines, K-nearest neighbor, etc. 2)
•
Network Security
– The direct annual loss in 2011 from global cyber crime was $114B.
– Authors propose a Artificial Immune System that uses neural networks as detectors. 3)
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Energy Efficiency
– During the next 10 years, electricity demand expected to grow by 13% to 15% per year.
– Authors describe a system using neural networks that can communicate with electricity grids.
– Expected to reduce energy loss from 16% to between 3% -- 5%. 4)
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1) Amato, F., et al (2013). Artificial neural networks in medical diagnosis. J Applied Biomedicine. 11:47-58.
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2) Deshpande, PM, et al., (2016 Jan). Applications of data mining techniques for fraud detection in credit-debit card transactions. ISJRD,
Conference on Technological Advancement and Automatization in Engineering. 339-345.
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3) Komar, M., et al (2016). Intelligent cyber defense system. ICTERI, Kyiv, Ukraine, June 21-24 meeting, 534-549.
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4) Buyuk, OO, et al (2016). A novel application to increase energy efficiency using artificial neural networks. IEEE. 1-5.
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TensorFlow
• What is it:
– Neural networks software for numerical computation - uses data flow
graphs for computation
– Developed at Google’s machine intelligence research organization
• What can it be used for:
– Any machine neural network problem
• Video Demonstration
– Six minute video introduction on TensorFlow on youtube.
• Further information:
– www.tensorflow.org
– https://www.youtube.com/watch?v=bYeBL92v99Y
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Torch
• What is it:
– Torch is a scientific computing framework for machine learning.
– The goal is to be flexible and allow the building of scientific algorithms
quickly - contains neural network and optimization libraries
• What can it be used for:
– Machine learning neural network problems
• Video Demonstration
– Three minute introduction on youtube.
• Further information:
– http://torch.ch/
– https://www.youtube.com/watch?v=uxja6iwOnc4&list=PLjJh1vlSEYgvGod9
wWiydumYl8hOXixNu&index=19
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CNTK
• What is it:
– CNTK stands for Computational Network Toolkit - created by Microsoft.
– Designed for use with CPUs or GPUs (ie, graphical processing units)
• What can it be used for:
– Can be used for image classification problems, video analysis, speech
recognition and natural language processing.
• Video Demonstration
– A two minute introduction on youtube.
• Further information:
– https://www.cntk.ai/
– https://www.youtube.com/watch?v=-mLdConF1EU
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Caffee
• What is it:
– Caffee is a deep learning framework designed to be modular and fast –
used with CPUs or GPUs.
– Developed by Berkeley Vision and Learning Center (BLVC) and community
contributors.
• What can it be used for:
– Originally developed for machine vision; but, now able to handle speech
and text problems.
• Video Demonstration
– A three minute introduction on youtube.
• Further information:
– http://caffe.berkeleyvision.org/
– https://www.youtube.com/watch?v=bOIZ74rOik0
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Further References
•
What is a neural network – Episode 2 in Deep Learning Simplified,
DeepLearning.TV, www.youtube.com.
•
Zhang, Zhongheng (2016). A gentle introduction to artificial neural networks.
Ann Translational Med. 1-5.
•
Soniya, et al (2016). A review on advances in deep learning. IEEE, 1-6.
•
Andrew Ng. Machine Learning Course, Stanford University, Coursera.com.
https://www.coursera.org/learn/machine-learning
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Yaser Abu-Mostafa. Learning from Data: Introductory Machine Learning
Course. CalTech. April 2012. Available on youtube.
https://www.youtube.com/watch?v=mbyG85GZ0PI
•
Geoffrey Hinton. Neural Networks for Machine Learning Course, University of
Toronto, Coursera.com, October 2012. https://www.coursera.org/learn/neuralnetworks
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Contacts
• Jeff Shomaker – Founder/President 21 SP, Inc.
– [email protected]
– http://www.21spinc.com
• 21 SP, Inc. is a small privately held startup working in the area of
genetic-based personalized medicine. The company's mission is to
reduce the use of traditional trial-and-error medicine by using
pharmacogenetics and other evidence-based data, such as the results
of high quality clinical trials, to improve decision making in the medical
clinic.
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