But what is a GPT? Visual intro to transformers | Chapter 5, Deep Learning

This paragraph introduces the concept of GPT, which stands for Generative Pre-trained Transformer, as a type of machine learning model trained on vast amounts of data to generate new text. It explains the significance of the Transformer model, which is central to the current AI boom, and the intention of the video series to visually explain the inner workings of a Transformer. The paragraph also touches on the versatility of Transformer models in various applications, such as generating images from text descriptions, and the foundational role of Transformers in tools like ChatGPT. The process of predicting text and generating new stories using GPT-2 and GPT-3 is also discussed, highlighting the iterative prediction and sampling process.

The second paragraph delves into the mechanics of Transformers, starting with the breakdown of input data into smaller components called tokens. It discusses the encoding of these tokens into vectors and the process of passing them through attention blocks that allow for the updating of vector values based on context. The paragraph also explains the role of perceptron layers or feedforward networks in processing the vectors and the importance of understanding basic matrix operations. It sets the stage for a deeper exploration of the Transformer's initial and final sections and the fundamental concepts that are essential for any machine learning engineer working with Transformers.

This paragraph focuses on the foundational concepts of machine learning, emphasizing the approach of using data to determine how a model operates. It explains the process of training models with adjustable parameters, such as weights in linear regression, to mimic behavior based on input-output examples. The paragraph also discusses the scalability of deep learning models, which have been proven to be highly expandable over decades, and the importance of the training algorithm called backpropagation. It touches on the need for models to adhere to a specific format to function effectively on a large scale and introduces the concept of tensors as multi-dimensional arrays of real numbers used in deep learning.

The fourth paragraph explores the concept of embeddings, where words are transformed into vectors, a common method in machine learning used even before the advent of Transformers. It discusses the embedding process in the context of GPT-3, which uses a vocabulary size of 50,257 with 12,288-dimensional embeddings, resulting in over 617 million parameters. The paragraph illustrates the idea of embeddings as points in a high-dimensional space, where similar meanings tend to cluster together. It also touches on the model's ability to learn vector representations that capture semantic relationships between words, such as gender or familial relationships, and the use of vector arithmetic to explore these relationships.

This paragraph discusses the importance of context in language models, explaining how the initial vector representations of words can be enriched with contextual information as they pass through the network. It mentions the model's context size, which for GPT-3 is 2048, and how this limits the amount of text that can be considered when predicting the next word. The paragraph also explains the role of the final vector in the context in making predictions about the next tokens, and the use of a separate matrix to map these vectors to vocabulary values, introducing the concept of the 'Output Embedding' matrix.

The final paragraph of the script explains the use of the Softmax function in normalizing raw logits into a valid probability distribution for predicting the next token. It introduces the concept of 'temperature' as a parameter that can adjust the randomness of the model's predictions, allowing for more diverse or more predictable outcomes. The paragraph also discusses the practical constraints of using different temperatures and the impact on the generated text's coherence and variety. It concludes with a teaser for the next chapter, which will focus on the attention mechanism, a fundamental part of the Transformer architecture.