A neutron network is a type of computer program inspired by how the human brain works. It is made up of layers of “neurons,” which are simple units that process information.

Input Layer: This is where the network receives data, such as numbers or images.

Hidden Layers: These are the intermediate layers where the network processes the data and learns patterns. Neurons in each layer connect to those in the next, passing information forward.

Output Layer: This layer provides the final result, such as predicting a value, classifying an image, or translating text.

Connections between neurons have adjustable weights that change as the network learns, improving its ability to solve problems. Neural networks form the basis of deep learning.

Each connection between neurons has a weight.

When an input passes through a connection, it is multiplied by the weight.

The result determines how much influence that input has on the next layer.

For example, in an image recognition network: 

If a pixel in an image is important (like part of a face), the weight for that pixel’s connection might be high.

If a pixel is less important (like part of the background), its weight might be low.

During training, the neural network adjusts these weights to improve its accuracy. This process is called learning

Deep learning models are files that data scientists train to perform tasks with minimal human intervention. 

Deep learning models include predefined sets of steps (algorithms) that tell the file how to treat certain data. 

This training method enables deep learning models to recognize more complicated patterns in text, images, or sounds.

Voice assistants like Siri and Alexa

Face unlock for smartphones

Personalized recommendations on Netflix and YouTube

Real-time language translation with Google Translate

Photo filters on Instagram and Snapchat

Email spam detection

Route optimization in ride-sharing apps like Uber