Definition
Artificial Neural Networks (ANN) are computational models inspired by the biological structure of the brain. They consist of layers of interconnected “neurons” that process information by transmitting signals and adjusting the strength (weight) of connections based on training data.
Why It Matters
Neural networks are the ‘engines’ of the AI revolution. Understanding their structure—layers, weights, and activation functions—is essential for anyone who wants to build or even critically evaluate modern technology. They are the new ‘universal function approximators’.
Core Concepts
- Layered Architecture:
- Input Layer: Receives the raw data.
- Hidden Layers: Intermediary layers where feature extraction and computation occur. Multiple hidden layers lead to “Deep Learning.”
- Output Layer: Produces the final classification or value.
- Weight Adjustment: Learning is the process of finding the optimal set of weights to minimize the difference between the network’s output and the target output.
- Backpropagation: An algorithm that calculates the gradient of the error function and propagates it backward through the network to update weights.
- Activation Functions: Mathematical functions (e.g., Sigmoid, ReLU) that determine whether a neuron “fires” based on the sum of its inputs.
- Universal Approximation: Mathematically, a neural network with at least one hidden layer can approximate any continuous function to any desired degree of accuracy.
- Hardware-Software Symbiosis: The 2012 realization by “dissident academics” in Toronto (Geoffrey Hinton’s team) that neural networks trained exponentially faster on parallel graphics hardware (Parallel Computing) than on traditional serial CPUs.
- Scalability Principle: Neural networks scale with the amount of computing power available to them and never seem to plateau, leading to the current LLM boom.