On the real cycle class map
Department of Quantitative Theory and Methods
Jeremy Jacobson
Current
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Mathematical intuition
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Definition
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))
Thus, it is as if there are no functions of several variables at all. There are only simple combinations of functions of one variable.
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
f(x_1,x_2,\cdots,x_n) = \phi(\sum\limits_{i=1}^n w_i x_i+\theta)
w_1
w_2
w_n
x_1
x_2
x_n
\mathbb{R}^n \stackrel{f}{\rightarrow}\mathbb{R}^1
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one ''hidden layer"
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one "node"
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"activation" phi
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"threshold" theta
\phi
Definition of a feedforward neural network
Definition of a feedforward neural network
f(x_1,x_2,\cdots,x_n) = \sum\limits_{i=1}^{2}W_i\phi_i(\sum\limits_{j=1}^n w_{i,j} x_i+\theta_i)
w_{1,1}
w_{1,2}
w_{1,n}
x_1
x_2
x_n
\mathbb{R}^n \stackrel{f}{\rightarrow}\mathbb{R}^1
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one ''hidden layer"
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two "nodes"
W_1
W_2
w_{2,1}
w_{2,2}
w_{2,n}
Definition of a feedforward neural network
(vector notation)
f(\vec{x}) = \vec{W}^T\phi(\vec{w}^T\vec{x}+\vec{\theta})+\eta
\vec{x}
\mathbb{R}^n \stackrel{f}{\rightarrow}\mathbb{R}^1
\vec{W}
\vec{w}
Google's TensorFlow and ML workbench
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Google Cloud Platform Datalab (https://cloud.google.com/datalab/)
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TensorFlow and high-level framework (ML Workbench)
import google.datalab.contrib.mlworkbench.commandsThank you!
Copy of Copy of Introduction to neural networks
By Jeremy Jacobson
