Neural Networks: Neural Networks Tools and Techniques for Beginners by John Slavio

Author:John Slavio [Slavio, John]
Language: eng
Format: azw3
Published: 2018-05-02T04:00:00+00:00

This gives a nice big chunk of a data log that I can then build another function for to analyze the results so that I can perfect the prediction table based off of the weights supplied. I can then produce an algorithm that should give me a better weighting score. I would then begin testing it against giant collages of male and female generations to perfect the prediction. Needless to say, we have passed the point where I introduce the concept to you and I begin to spend days of testing the data and making minor tweaks. It is at this point that we have a Perceptron and we are now just making it better. Oh wait, now we have to talk about “Learning Rate” before ending because constantly changing the weights manually is not feasible.

The Learning Rate is determined by the error minus the guess. Therefore, for each weight that was wrong, we set that weight to the error - the guess, which means that if the weight was previously a 1 and the error was 2(the difference between -1 and 1) then that weight becomes -1. Normally, this is an iterative process that takes in one or two inputs at a time instead of fifty, but many have found ways to scale it to that size. This is often known as Gradient Descent.

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