How Neural Networks Process Text: Tokenization, Matrix Math, and Deep Learning Explained

Natural language processing models cannot work with raw text directly, so words are first converted into numerical token IDs before being fed into a neural network. Each layer of the network takes an input vector, multiplies it by a matrix of learned weights, adds a bias, and passes the result through a non-linear activation function like ReLU. Stacking many such layers — the basis of deep learning — allows networks to build progressively richer representations of the original input. Recurrent neural networks extend this by combining the current input vector with a running hidden state at every timestep, though the fixed size of that state limits how much information can be retained over long sequences. This matrix-based structure is also why GPUs are central to deep learning, as they are purpose-built to execute large-scale matrix multiplications efficiently.