Overview of GPT and Neural Networks

Sep 21, 2024

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Lecture Notes on Generative Pre-trained Transformers (GPT) and Neural Networks

Introduction to GPT

  • GPT Stands for:
    • Generative: Bots that generate new text.
    • Pre-trained: Initially trained on massive data, can be fine-tuned.
    • Transformer: A specific kind of neural network, core to current AI.

Importance of Transformers

  • Models like DALL-E and Midjourney are based on transformers.
  • Originally developed for text translation by Google in 2017.

Focus of the Lecture

  • Explore how data flows through a transformer visually.
  • Analyze step by step with a focus on models like ChatGPT.

Functionality of Transformers

  • Convert input text/audio into predictions or transformations (e.g., text to speech, text to images).
  • Use attention mechanism for context understanding.

Understanding Transformers

  • Core Concept: Predict the next word based on context.
  • Use repeated prediction and sampling to generate coherent outputs.
  • Example: GPT-2 vs. GPT-3 comparison in text generation.

Technical Overview

  • Input is tokenized into vectors.
  • Tokens could be parts of words, patches of images, or sound chunks.
  • Embeddings involve mapping tokens to high-dimensional vectors.
  • Attention blocks enable the model to determine contextual relevance and update meanings.

Steps in Processing

  1. Tokenization and Embedding:

    • Tokens are mapped to vectors using an embedding matrix.
    • Vectors represent coordinates in a high-dimensional space.
    • Embedding matrix is a core set of weights in the model.

  2. Attention and Perceptron Blocks:

    • Attention Blocks: Allow vectors to interact with context.
    • Perceptron Blocks: Apply transformations independently.

  3. Probability Distribution (Softmax):

    • Used to predict the next token.
    • Normalizes logits to probabilities.

  4. Iterative Process:

    • Predictions are refined over multiple layers.

Training Neural Networks

  • Deep Learning Models:

    • Scale effectively with backpropagation.
    • Input formatted as arrays (tensors) of real numbers.

  • Parameters and Weights:

    • Large models like GPT-3 have billions of parameters.
    • Organized into matrices that perform transformations.

Embedding and Matrix Operations

  • Word Embeddings: Capture semantic similarities.
  • Directional Properties: Capture relatedness (e.g., gender, plural forms).
  • Dot Products: Measure vector alignment and similarity.

Final Steps in Prediction

  • Unembedding Matrix:
    • Maps final vectors to list of possible tokens.
  • Softmax Function:
    • Converts logits to probabilities.
    • Temperature parameter adjusts prediction randomness.

Conclusion

  • Transformers and AI: Present remarkable capabilities in text prediction and generation.
  • Next Steps: Dive deeper into attention mechanisms, critical to transformer functionality.

Additional Notes

  • Understanding matrix multiplication and weight tuning is essential.
  • Emphasis on high-dimensional vector space for meaning representation.
  • Context size limitation affects long text processing.

These notes summarize the key concepts discussed in the lecture about GPT and neural networks, providing a foundational understanding of how transformers work and their impact on AI advancements.