Neural Network
A computational system loosely inspired by the brain — layers of nodes that transform input into output.
What is Neural Network?
A neural network is the basic building block of deep learning. It consists of layers of "neurons" (nodes), each performing a simple math operation. Connecting them in layers gives the network the ability to learn complex patterns.
A neural network has three parts: **input layer** (where data enters), **hidden layers** (where the work happens — there can be dozens), and **output layer** (the prediction). Each connection between neurons has a "weight" that gets adjusted during training.
Modern neural networks can have billions of parameters (weights). GPT-4 has roughly a trillion. They are powerful but expensive to train — which is why pre-training is concentrated at a few large labs (OpenAI, Anthropic, Google, Meta) and most engineers work on fine-tuning or inference instead.
Neural networks are the engine behind LLMs, computer vision, recommendation systems. Knowing the basics is essential to debug or evaluate any modern AI system.
When you upload a photo to Instagram, a neural network categorises it (food / landscape / portrait) for the feed algorithm. The same network powers content moderation across 50 languages including Hindi and Tamil.
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