Lecture on GPT and Transformers

Introduction to GPT

• GPT stands for Generative Pretrained Transformer.
  • Generative: Bots that generate new text.
  • Pretrained: Model is trained on massive data, ready for fine-tuning on specific tasks.
  • Transformer: A type of neural network central to AI advancements.

Overview of Transformers

• Used for various models: audio transcription, text-to-speech, text-to-image.
• Originally designed for language translation (introduced by Google in 2017).
• Key models:
  • GPT models focus on predicting the next piece of text.

Key Concepts in Transformers

• Tokens: Input is broken into tokens, which can be words, partial words, or common character combinations.
• Vectors: Each token is associated with a vector representing its meaning.
• Attention Mechanism: Vectors communicate to update meanings based on context.
• Multi-layer Perceptron/Feed-forward Layer: Processes vectors in parallel.

Generating Text

• Prediction involves a probability distribution over possible next tokens.
• Repeated predictions and sampling enable text generation.

Understanding Embeddings

• Embedding Matrix: Converts tokens into vectors.
• Semantic Space: Vectors represent semantic meaning and context.
• Word Relationships: Similar meanings align closely in vector space.

The Role of Matrices

• Matrix Multiplications: Core of computations in transformers.
• Embedding and Unembedding Matrices: Convert between words and vectors, contributing to model parameters.

Probability and Softmax

• Softmax Function: Converts values into a probability distribution.
• Temperature: Adjusts distribution randomness (higher means more uniform, lower means more deterministic).

Training and Parameters

• Many parameters (e.g., 175 billion in GPT-3) trained using backpropagation.
• Model weights are learned through training examples.

Next Steps in Learning

• Attention Blocks: Central to transformers, will be explored in detail.
• Multi-layer Perceptron Blocks: More about their role and training in upcoming sections.

Conclusion

• Foundation set for understanding attention mechanisms in transformers.
• Future chapters will delve into specifics of attention and more details on training and model functioning.