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

Step-by-step Tutorial: Understanding Transformers in Deep Learning

1. Introduction to GPT and Transformers:

   • The initials GPT stand for Generative Pretrained Transformer, which are bots that generate new text.
   • Pretrained refers to models that have gone through learning processes and fine-tuning for specific tasks.
   • A transformer is a type of neural network used in machine learning models.

2. Different Models using Transformers:

   • Models can take in audio and produce transcripts or generate synthetic speech from text.
   • Tools like Dolly and Midjourney, which create images from text, are based on transformers.
   • The original transformer by Google was for language translation, but newer variants focus on predicting text based on context.

3. Prediction and Sampling in Transformers:

   • Transformers predict the next word in a passage by creating a probability distribution over possible text chunks.
   • To generate text, the model predicts the next word, samples from the distribution, appends it to the text, and repeats the process.

4. Role of GPT-3 in Text Generation:

   • Using GPT-3 for API calls can generate coherent stories based on the input text.
   • The process involves repeated prediction and sampling to produce text one word at a time.

5. Data Flow in Transformers:

   • Input text is broken into tokens (words or character combinations) and associated with vectors for encoding meanings.
   • Attention blocks allow vectors to communicate and update values based on context, enhancing the model's understanding.

6. Multi-Layer Perceptron Blocks:

   • Vectors pass through multi-layer perceptron blocks for further processing in parallel.
   • These blocks involve matrix multiplications and updating vectors based on learned parameters.

7. Understanding Word Embeddings:

   • Words are converted into vectors known as embeddings, representing meanings in a high-dimensional space.
   • Embeddings encode semantic information and context, allowing the model to understand word relationships.

8. Context Size in Transformers:

   • Transformers have a fixed context size limiting the amount of text they can process at once.
   • Context size influences the model's ability to retain information and generate coherent responses.

9. Prediction Process in Transformers:

   • The final step involves mapping the context vectors to a probability distribution over all possible next tokens.
   • The softmax function is used to convert raw values into a valid probability distribution.

10. Temperature in Softmax Function:

    • Temperature in the softmax function controls the distribution of probabilities, impacting word selection.
    • Higher temperature values allow for more diverse word choices, while lower values prioritize common words.

11. Logits and Next Word Prediction:

    • The raw, unnormalized output of the model for next word prediction is referred to as logits.
    • Logits are processed through the softmax function to generate a probability distribution for word selection.

12. Foundation for Attention Mechanism:

    • Understanding word embeddings, softmax, dot products, and matrix multiplications lays the foundation for comprehending the attention mechanism in transformers.
    • Attention is a crucial component in modern AI models and enhances the model's ability to focus on relevant information.

13. Further Learning:

    • Dive deeper into the attention mechanism in the next chapter for a comprehensive understanding of transformers in deep learning.

By following these steps, you can gain a solid understanding of transformers in deep learning and their role in text generation using models like GPT-3.