What is a Neural Network?

In previous lessons, we learned that a neural network is a machine learning model designed with inspiration from the human brain.

In this lesson, we will explore the components and workings of a neural network in detail.

A neural network is an artificial intelligence model composed of multiple neurons.

Neurons in the human brain receive signals from other neurons through synapses, and if the signals exceed a certain threshold, these signals are transmitted to the next neuron.

Similarly, neurons in a neural network receive inputs and apply weights, bias, and an activation function to determine the final output.

• Weights : These are numbers that indicate the importance of an input value. Weights are adjusted to optimal values during training, and the larger the weight, the greater the influence of that input on the output.

• Bias : This is an additional value added to the result of multiplying the input value by its weight, serving as a threshold to determine how easily a neuron is activated.

• Activation Function : This function determines the output by assessing whether the sum of the input multiplied by weights, plus the bias, exceeds a specific threshold.

In the next lesson, we will explore the neuron component of neural networks in more detail.