From Logistic Regression to Neural Networks

Neuron

A neuron, in the context of neural networks and artificial intelligence, is a fundamental computational unit that mimics the behavior of biological neurons found in the human brain. Neurons are the building blocks of artificial neural networks, which are used for various machine learning tasks, including image recognition, natural language processing, and more.

Components of a neuron

Let’s discuss the key components and functions of an artificial neuron:

• Input: Neurons receive input signals from other neurons.

• Weights: Each input is associated with a weight that determines its influence on the neuron’s output. These weights are learnable parameters that are adjusted during the training process to optimize the neuron’s performance.

• Summation: The weighted input signals are summed together, often with an additional bias term, to produce a single value. This weighted sum represents the net input to the neuron.

• Activation function: The net input is then passed through an activation function. The activation function introduces nonlinearity into the neuron’s computation.

• Output: The result of the activation function is the output of the neuron, which can be passed to other neurons in subsequent layers of the neural network.

Here’s the illustration for the components of a neuron: