This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer.

Explainer

Just like our brain, a Neural Network is made up of interconnected "neurons". These neurons work together by learning from (input) data and getting better at tasks (in the hidden layer) to give (output) predictions or decisions.

Additional Context

A Neural Network is a computational model inspired by the structure and function of the human brain. It consists of a series of interconnected "neurons" or nodes organized in layers. These layers include:

1. Input Layer: Each node in this layer represents a feature from the input data. For example, if the data is an image, each node might represent a pixel value.
2. Hidden Layers: This is where the real magic happens. This layer consists of multiple nodes that process and transform the input data through a series of weighted connections. Each connection has a weight that gets adjusted during training based on the input data and the desired output to minimize errors.
3. Output Layer: This layer takes the outputs of the last hidden layer and transforms them into the final prediction of the network. For example, if it is an image recognition task, each node in the output layer might represent different predictions to identify the image.

In summary, a Neural Network mimics the human brain's ability to learn and make decisions by processing input data through interconnected layers of neurons, adjusting weights, and minimizing errors to improve performance.