Deep learning mathematics (2017)
Institute for quantitative theory and methods (QTM)
Jeremy Jacobson
Lecturer
Neural networks
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Mathematical intuition
Kolmogorov
Every continuous function of several variables defined on the unit cube can be represented as a superposition of continuous functions of one variable and the operation of addition (1957).
f(x_1,x_2, \ldots, x_n) = \sum\limits_{i=1}^{2n+1}f_i(\sum\limits_{j=1}^{n}\phi_{i,j}(x_j))
f_1
f_i
f_{2n+1}
f(x_1,x_2, \ldots, x_n) = \sum\limits_{i=1}^{2n+1}f_i(\sum\limits_{j=1}^{n}\phi_{i,j}(x_j))
x_1
x_2
x_n
\phi_{1,n}
\phi_{2n+1,n}
\phi_{2n+1,1}
\phi_{1,1}
\phi_{1,2}
\phi_{2n+1,2}
f
Neural network approach to counting real roots of polynomial systems
Mourrain, Pavlidis, Tasoulis,Vrahatis:
univariate polynomials of degree 2
ax^2+bx+c=0
x = \frac{-b \pm \sqrt{b^2-4ac}}{2a}
Reproducing results using Google's TensorFlow and ML workbench
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Google Cloud Platform Datalab (https://cloud.google.com/datalab/)
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TensorFlow
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and high-level framework
import google.datalab.contrib.mlworkbench.commandsOur results:
| Class | Classification Accuracy |
|---|---|
| Class 1: Zero real roots | 99.17 % |
| Class 2: Two real roots | 100% |
Thank you!
Job talk
By Jeremy Jacobson
