Here is an article I found in my Time Magazine about AI
December 24, 2015-January 4, 2016
Pg. 44

Progress Presentation

Here is my progress presentation.

Perceptron Algorithm Notes

Introduction

• Perceptron models a neuron
• n inputs are received by it that correspond to features
• It takes the inputs and sums them, then produces an output after checking the result
• Its main use is to classify groups that are linearly separable
• It is often used for binary classification

Components

• It is composed of a summation processor, and activation function (does the thresholding) and weights.
• It takes a weighted sum of the inputs and outputs 
• e.g. 1 if the sum is greater than the threshold and 0 if it isn't
• Usually the input and output weights are real numbers

• Perceptron can also have an input called the bias
• Bias allows the transfer function curve to be shifted horizontally along the input axis while the curve is left unaltered.
• Weight determines the slope 
• e.g. in the eq. y = mx + b, m is the weight and b is the bias

• Activation Function
• Also know as transfer function
• It translates input signals into output signals 
• The output is produced using a threshold
• The 4 types of activation functions most commonly used are: Unit step, sigmoid, piecewise linear, and Gaussian
• The unit step is a simple threshold where the output is 1 of 2 levels and it depends on whether the input is greater than or less than the threshold value
• The sigmoid contains 2 functions: tangential and logistic. Values of tangential functions are from -1 to 1 and the values of logistic functions are from 0 to 1.
• In the piecewise linear function the output arrived at from being proportional to the total weighted output.
• The Gaussian functions are continuous bell shaped curved functions. The node output which is high or low is interpreted from class membership which is 1 or 0, and depends on how close the net input is to a chosen value or average. 
• Learning Rate
• Used when updating the weights and bias to get a smaller error
• Helps control how much the weights and bias are changed

• Learning In Perceptrons 
• when trying to find a line to separate two classes or groups it responds to the input vectors with a target value of 0 or 1
• If the classes can be separated the solution will be found in a finite amount of time.