AI News, Machine Learning - Some Bones

Machine Learning - Some Bones

To start to illustrate the computational process we will look at a very simple example of a neural network.

The perceptron starts by calculating a weighted sum of its inputs The perceptron has five parts: We can ask the perceptron to give us an answer to a question where we have three factors that influence the outcome.

“is it good for you?” “does it taste good?” “does it look good?” We give numerical values to all of the questions and the answers.

the inputs are multiplied by the weights The next step is to sum all the inputs and the weights The neuron’s output is determined by whether the weighted sum is less than or greater than a thresholdValue.

As we know the answers to the question we can use the answers to adjust Now all this is very basic and it would be easy to write a few lines of code to work out that we have three conditions that have a value and are weighted, measure the output against our threshold it can then make a decision of true or false.

single layer, single neuron network (using a linear activation function) receives an input with two features x1 and x2;

It does this by taking a set of weighted inputs, calculating their sum with a function to activate the neuron and passing the output of the activation function to other nodes in the network.

In general terms a network learns from having an input and a known output, so we can give pairs of values (x, y) where x is the input and y the known output The aim is to find the weights (w) that fit closest to the training data.

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