GPT stands for Generative Pretrained Transformer, a neural network model core to AI advancement, enabling text generation and prediction. Transformers process data through tokens, vectors, attention blocks, and multi-layer perceptrons, culminating in a probability distribution for next tokens, with key components like embedding and unembedding matrices.

GPT, short for Generative Pretrained Transformer, is a neural network model that generates new text by learning from vast amounts of data. This chapter delves into the inner workings of transformers, explaining how they process data and make predictions.

This chapter provides a high-level overview of how data flows through a transformer, breaking down inputs into tokens associated with vectors that encode meaning. The process involves attention blocks and multi-layer perceptron blocks to update and interpret the vectors, ultimately aiming to predict the next tokens in a sequence.

This chapter delves into the fundamental concepts of deep learning models and training algorithms, emphasizing the importance of understanding the structure and parameters involved. It highlights the significance of model weights, backpropagation, and the transformation of input data into real numbers for effective training.

Exploring the process of turning words into vectors in machine learning models, highlighting how word embeddings capture semantic relationships in high-dimensional spaces. Demonstrating how models learn to associate directions with specific meanings, such as gender or nationality, through vector operations.

Transformers utilize vectors in an embedding space to represent words, incorporating context and position information. The goal is to empower each vector to encode rich and specific meanings beyond individual words.