What are artificial neural networks (ANNs)?

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Artificial Neural Networks (ANNs) are computing systems inspired by the structure and function of the human brain. They are the building blocks of deep learning. Concept Overview Just like our brains consist of neurons connected by synapses, ANNs consist of nodes (neurons) organized into layers: Input Layer – receives the raw data.Hidden Layers – perform computations and feature extraction.Output Layer – produces the final result (e.g., classification or prediction). Basic Structure of an ANN Each connection between neurons has a weight and a bias, and each neuron applies an activation function (like ReLU, Sigmoid, Tanh) to determine its output. How It Works (In Simple Steps) Input: Data is fed into the network.Forward Propagation: The data flows through the layers; weights and biases are applied, activations are calculated.Output: The network produces a prediction.Loss Calculation: The difference between predicted and actual values is measured.Backpropagation: The network adjusts its weights to reduce the error (using gradient descent).Repeat: The process is repeated over many iterations (epochs) to improve accuracy. Why Use ANNs? They can learn complex, non-linear relationships.They’re flexible and can be used for tasks like: Image recognition (e.g., detecting cats vs dogs)Speech-to-text conversionForecasting (stock prices, weather)Language translationGame playing (like AlphaGo) Downsides Require lots of data and computing power.Can be a black box—difficult to interpret decisions.May overfit if not trained properly.

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What are artificial neural networks (ANNs)?

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Concept Overview

Basic Structure of an ANN

How It Works (In Simple Steps)

Why Use ANNs?

Downsides