Artificial Neural Networks

An Artificial Neural Network (ANN) is a computing system loosely inspired by the way biological brains process information - built from layers of interconnected neurons that learn patterns directly from data.

Biological Inspiration

Just as a biological neuron receives signals through dendrites, processes them in the cell body, and fires an output through its axon, an artificial neuron receives weighted inputs, sums them, and passes the result through an activation function.

Core Components

• Neurons (Nodes) - basic computational units
• Weights - determine the strength of each connection
• Bias - allows shifting the activation threshold
• Layers - input, hidden and output layers organize the neurons
• Activation Function - introduces non-linearity

Types of Neural Networks

Beyond the standard feedforward ANN, specialized architectures exist for different data types - Convolutional Neural Networks (CNNs) for images, Recurrent Neural Networks (RNNs) for sequential data, and Transformers for language, all building on the same foundational neuron concept.

Coming Up Next

Now that you know the structure, let's build an intuition for what actually happens when a neural network processes data and learns from it.