Neural networks have become the backbone of modern AI. From image recognition to natural language processing, they power the systems we interact with every day. But what exactly are they, and how do they actually learn?
The Basics: Layers and Neurons
A neural network is a computational model loosely inspired by the human brain. At its core, it consists of layers of interconnected nodes (neurons). The input layer receives raw data — pixels, tokens, or numerical features. The output layer produces a prediction. Between them, hidden layers learn increasingly abstract representations.
Backpropagation: How Networks Learn
Learning happens through backpropagation combined with gradient descent. When the network makes a prediction, we compare it to the ground truth using a loss function. The error is propagated backwards, and each weight is nudged to reduce it. Repeat this millions of times over diverse data, and the network learns.
What I've Learned Building Them
Working with neural networks practically — fine-tuning LLMs with LangChain, building classifiers with PyTorch — taught me that the math is only half the story. Architecture decisions, data quality, and training stability matter just as much. There is no substitute for running experiments yourself.