Artificial Neural Networks History

Artificial Neural Networks/Activation Functions
1229 x 630 Activation Functions
There are a number of common activation functions in use with neural networks. This is not an exhaustive list.
Step Function
A step function is a function like that used by the original Perceptron. The output is a certain value, A1, if the input sum is above a certain threshold and A0 if the input sum is below a certain threshold. The values used by the Perceptron were A1 = 1 and A0 = 0.
These kinds of step activation functions are useful for binary classification schemes. In other words, when we want to classify an input pattern into one of two groups, we can use a binary classifier with a step activation function. Another use for this would be to create a set of small feature identifiers. Each identifier would be a small network that would output a 1 if a particular input feature is present, and a 0 otherwise. Combining multiple feature detectors into a single network would allow a very complicated clustering or classification problem to be solved.
Linear Combination
A linear combination is where the weighted sum input of the neuron plus a linearly dependant bias becomes the system output. Specifically:
y = ζ + b
In these cases, the sign of the output is considered to be equivalent to the 1 or 0 of the step function systems, which

Source: Research

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With the job market being in the dump here in Virginia as well as the rest of the nation I am considering the possibility of teaching in SK. I do hold a BA degree and have done my share of substitute teaching in some local public schools. What I need to know if this is a job that is worth pursuing. Also with the current exchange rate being so skewed between the USD and the KRW would it be better to negotiate a contract in US currency or just have the base salary increased if they will only pay in the local currency?
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