What is a Neural Network?
An artificial neural network is a computing system inspired by biological neural networks in the brain. It consists of interconnected nodes (neurons) organized in layers that process information.
Architecture
Input Layer: Receives the raw data (pixels, words, numbers).
Hidden Layers: Intermediate layers where computation happens. Each neuron applies a mathematical function to its inputs and passes the result forward. More layers = deeper network = ability to learn more complex patterns.
Output Layer: Produces the final result — a classification, prediction, or generated content.
How Learning Works
Networks learn through backpropagation: the system makes a prediction, compares it to the correct answer, calculates the error, then adjusts the connection weights backward through the network to reduce that error. This process repeats millions of times.
Common Architectures
CNNs (Convolutional Neural Networks): Specialized for image and video processing. Used in facial recognition and medical imaging.
RNNs (Recurrent Neural Networks): Process sequential data like text and time series. Each step considers previous context.
Transformers: The architecture behind modern LLMs. Uses attention mechanisms to process all parts of the input simultaneously, making it far more efficient than RNNs for language tasks.