Layers and Networks

By James

The brain consists of billions of neurons and cells responsible for setting up everything we do and think. Neural Networks (NNs) aim to copy the brain's inner components to produce an output based on an input. Neural Networks have recently been bunched into what is basically "The Next Big Thing" at this point, an inseparable portion of AI. That is true. Without Neural Networks, where would AI have been? It's probably not someplace as far as it is right now.

Neural Networks (Basically)

Neural Networks are algorithms (models) based on the structure of the human brain. They serve as a foundation for many AI models, allowing models to make out patterns from the data fed into them and create an output. Each Neural Network has at least one Input Layer, Hidden Layer, and Output Layer. The Input Layer receives all inputs given to the Neural Network and passes inputs onto the Hidden Layers. In the Hidden Layers, the inputs are processed, and patterns are recognized. As inputs pass through the Hidden Layers, they are transformed into outputs placed in an Output Layer. In the Output Layer, the final result of the Neural Network's processing is produced and returned from the network as an output. To conclude this short description of Neural Networks, the overview of the Input, Hidden, and Output Layers is a simplification of the overall networks that they compose, which involve many other components aside from those layers.