A computational model inspired by the human brain, composed of interconnected layers of nodes (neurons) that process information and learn complex mappings from inputs to outputs.
In Depth
An Artificial Neural Network is loosely inspired by the biological neural networks in animal brains, though the resemblance is more metaphorical than mechanistic. It consists of layers of computational units called neurons, each of which receives numeric inputs, multiplies them by learned weights, sums the results, and passes the output through an activation function. The first layer receives raw data; intermediate 'hidden' layers learn progressively abstract representations; the final layer produces the model's prediction.
The network learns by adjusting its weights through backpropagation and gradient descent. For each training example, the network makes a prediction, computes the error (loss), and propagates that error backward through the network, calculating how much each weight contributed to the mistake. The optimizer then adjusts each weight slightly in the direction that reduces the error — a process repeated millions or billions of times until the network's predictions are accurate.
Modern neural networks bear little resemblance to early perceptrons. Today's architectures — Convolutional Neural Networks, Recurrent Neural Networks, Transformers — are specialized variants optimized for specific data types and tasks. But all share the core principle of learned, layered representations. The 'depth' in Deep Learning simply refers to having many such layers — deep stacks of interconnected neurons that build complex understanding from simple components.
An Artificial Neural Network learns by repeatedly adjusting millions of weights to minimize prediction errors — the same way a student improves by practicing and correcting mistakes, scaled to billions of examples.
Real-World Applications
Frequently Asked Questions
How does a neural network learn?
A neural network learns through a cycle: (1) Forward pass — input data flows through layers, each applying weights and activation functions to produce an output. (2) Loss calculation — the output is compared to the desired result. (3) Backpropagation — the error is propagated backward to calculate how much each weight contributed to the error. (4) Weight update — gradient descent adjusts each weight to reduce the error. This cycle repeats over millions of examples.
What are the different types of neural networks?
Major types include: Feedforward Neural Networks (the simplest, data flows in one direction), Convolutional Neural Networks (CNNs, specialized for images), Recurrent Neural Networks (RNNs, for sequential data), Transformers (the architecture behind LLMs like GPT and BERT), and Generative Adversarial Networks (GANs, for creating synthetic data). Each is optimized for different data types and tasks.
Are neural networks modeled on the human brain?
Only loosely. The basic idea — layers of interconnected processing units — was inspired by biological neurons. But artificial neurons are mathematical functions, not biological cells. They don't spike, don't use neurotransmitters, and are organized in ways no brain is. Modern neural networks succeed because of their mathematical properties (universal approximation, gradient-based optimization), not because they accurately replicate neuroscience.