What is Neural Network?

A neural network is a computational model inspired by the structure and function of biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information by passing numerical signals forward through the network. Each connection between neurons has a weight that is adjusted during training to minimize prediction errors. Neural networks are the fundamental building blocks of modern AI, underpinning everything from image recognition to large language models.

A basic neural network has three types of layers: an input layer that receives raw data (pixels, token IDs, numerical features), one or more hidden layers that transform the data through weighted sums and nonlinear activation functions, and an output layer that produces the final prediction or generation. During training, the network processes examples, calculates the error between its predictions and the correct answers (using a loss function), and adjusts its weights through backpropagation — a process of propagating error gradients backward through the network to update each connection's weight.

Neural network architectures have diversified to handle different types of data and tasks. Feedforward networks process fixed-size inputs in one direction. Convolutional Neural Networks (CNNs) detect spatial patterns in images through learnable filters. Recurrent Neural Networks (RNNs) process sequences by maintaining hidden state across time steps. Transformers use self-attention to process all elements of a sequence in parallel. Graph Neural Networks process data represented as graphs. Each architecture encodes different assumptions about the structure of the data it processes.

Modern neural networks can be staggeringly large. GPT-4 is rumored to have over a trillion parameters. LLaMA 70B has 70 billion parameters. Training these networks requires distributed computing across thousands of GPUs and months of training time. Techniques like quantization, pruning, and distillation make it possible to deploy these models on smaller hardware for inference. Understanding neural network fundamentals — forward passes, backpropagation, activation functions, regularization, and optimization — provides the foundation for understanding all modern AI systems.