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Introduction
• Neural network software has been open-sourced so it can
be used widely.
• I’ll discuss/demonstrate the following:
– Neural networks – what are they?
– Uses of Neural Networks
– TensorFlow
– Torch
– CNTK
– Caffe
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Neural Networks
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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
• 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)
• 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)
• 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)
• 1) Amato, F., et al (2013). Artificial neural networks in medical diagnosis. J Applied Biomedicine. 11:47-58.
• 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.
• 3) Komar, M., et al (2016). Intelligent cyber defense system. ICTERI, Kyiv, Ukraine, June 21-24 meeting, 534-549.
• 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=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNu&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
• 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/neural-
networks
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Contacts
• Jeff Shomaker – Founder/President 21 SP, Inc.
–jshomaker@21spinc.com
–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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Editor's Notes
Talk about myself --> human genome project ---> this led to Omicia ---> started Omicia in 2002 (with initial focus on direct to consumer testing).
Of course, super hype, you have all heard about, but there haven’t been real successful business models. My really big vision and/or next thing I wanted to do is bring these discoveries into the physicians office (use it)
Start with some brand new news…
Or
2. We have heard talks about personalizing medicine and general technology talks in this area, but I will present to you a concrete example for the clinic.
IC: Individualized Medicine unlocked
General Comments:One overall comment, you tend to want to lead the investor through your decision process.ハ This is a typically scientific approach, but doesn't work for investors.ハ It is best to state the direction and then backfill with the reason why.
Design Comments:Your slides overall are too dense, when people start to read, you lose the focus fromハ you and makes the pitch weaker.ハ I removed lots of words from the slides.
I know a lot of people like the black background (and if you are one of them I'm sorry!), but I find it difficult to work with and print. Also it always looks sloppy when you have to insert logos and images over it. ハ
Slides should have the point of the slide as the headline at the top so that people get it immediately.
I have been asked to maintain his confidentiality.