Artificial neural networks are statistical learning models, inspired by biological neural networks (central nervous systems, such as the brain), that are used in machine learning. These networks are represented as systems of interconnected “neurons”, which send messages to each other. The connections within the network can be systematically adjusted based on inputs and outputs, making them ideal for supervised learning.
If you’re like me, you forget the difference between
.unshift() all the time. Here’s a little trick to remembering them. Picture a keyboard. Now think of that keyboard as an array, with the left side of the keyboard corresponding to the front of the array, and the right side of the keyboard corresponding to the back of the array. Imagine yourself pressing down the left
shift key. Think of this as “removing” it from the keyboard (array). Similarly, the
shift function removes an element from the front of the array. Now picture yourself removing your finger from the left
shift key, and it comes back up. You just “added” (or unshifted) an element to the array.