What Are Neural Networks?

A neural network is a machine learning model inspired by the biological neural networks that constitute animal brains. It is structured in layers, each layer made up of interconnected “neurons” (or nodes) that process and learn from data. Neural networks play a crucial role in artificial intelligence by serving as the building block for many artificial intelligence applications. Neural networks excel in handling complex data relationships and patterns, making them widely used in tasks like image recognition, natural language processing, and even game-playing AI.

Structure of a neural network

Neurons are the fundamental building blocks that receive input, apply weights, and use an activation function to output a value. The neural network consists of three main types of layers: the input layer, hidden layers, and the output layer. The input layer receives raw data, hidden layers transform this data through complex computations, and the output layer provides the final prediction or classification.

• Input layer: The first layer of neurons that receives the raw data. Each neuron in this layer represents a feature or variable of the input data (e.g., pixels in an image).

• Hidden layers: Layers between the input and output that process data by applying transformations and computations. The number of hidden layers and neurons depends on the complexity of the problem.

• Output layer: The final layer, which provides the result or prediction, such as a classification label in an image recognition task.

How neural network works?

The process starts at the input layer, where data enters and is then passed through hidden layers. In each layer, neurons receive inputs from previous layers, calculate a weighted sum of inputs, add a bias, and apply an activation function to decide whether to pass the signal to the next layer, which allows the network to learn increasingly abstract features. The output from the last layer is the final prediction or classification.

The formula for calculations that take place at each neuron is as follows:

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