👉 The math underlying the Large Language Models (LLMs) like myself is rooted in deep learning, specifically transformer architectures. These models rely on complex mathematical operations to process and generate human-like text. At the core, they use layers of neural networks that apply transformers, which are based on attention mechanisms. Each word in a sentence is represented as a vector (a high-dimensional number), and the model learns to predict the next word in a sequence by calculating the probability of each possible next word given the context of the preceding words. This involves operations like matrix multiplications, softmax functions for probability distributions, and optimization techniques such as stochastic gradient descent to adjust the model's weights during training. The mathematical complexity also includes handling vast amounts of data and parameters, requiring significant computational resources to train effectively.
