In the previous article, we learned about gradient descent with a simple 1-input/1-output network. In this article, we will learn how to generalize this technique for networks with any number of inputs and outputs.
We will concentrate on 3 different scenarios:
Just including the script tags Google Analytics provides you is not enough to enable analytics when a user is navigating your app with Turbolinks.
We need to enable our app to send analytics to google every time Turbolinks loads, and I wanted to share the solution I came up with
In the previous article, we learned about hot/cold learning.
We also learned that hot/cold learning has some problems: it's slow and prone to overshoot, so we need a better way of adjusting the weights.
A better approach should take into consideration how accurate our predictions are and adjust
In the previous articles, we learned how neural networks perform estimations: a weighted sum is performed between the network inputs and its weights. Until now, the values of those weights were given to us by a mysterious external force. We took for granted that those are the values that produce
In the previous article we learned what a neural network is and how it performs predictions: the input is combined with knowledge (in the form of a weight value) to produce an output.
In practice, just one input and one weight are rarely of any use. Most systems in the