Neural Networks: Core Concepts and Architectures
1. What is ANN? What is a Neuron?
Artificial Neural Networks (ANNs) are inspired by the biological neural networks of the human brain. They are a set of algorithms designed to recognize patterns. ANN consists of layers: input layer, hidden layers, and output layer. Each layer is made up of nodes called neurons.
A neuron in ANN is a mathematical function modeled after a biological neuron. It receives one or more inputs, applies a weight and bias to them, sums them up, and passes the result through an activation function to produce output.
ANNs are widely used in tasks like image recognition, natural language processing, and more. They learn from data by adjusting weights using optimization algorithms like gradient descent.
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