Transformers, the tech behind LLMs | Deep Learning Chapter 5

@hao CC BY 4.0

Video: https://www.youtube.com/watch?v=wjZofJX0v4M

  • Introduction to GPT and Transformers ([00:00])

  • Explanation of Generative Pretrained Transformer (GPT)

  • Importance of transformers in AI advancements

  • Detailed Explanation of Transformers ([02:00])

  • Visual walkthrough of data processing in transformers

  • Examples like DALL-E and Midjourney using transformers

  • Core Functionality of Transformers ([04:00])

  • Focus on prediction models and probability distributions

  • Process of generating text through repeated predictions

  • Data Flow in Transformers ([06:00])

  • Tokens and vectors as fundamental units in data processing

  • Attention blocks facilitating context understanding

  • Multi-Layer Perceptron Blocks ([08:00])

  • Explanation of feed-forward layers and their role

  • Matrix multiplications as core computations

  • Embedding and Unembedding Matrices ([10:00])

  • Embedding words into high-dimensional vectors

  • Importance of semantic directions in the vector space

  • Context Size and Limitations ([14:00])

  • GPT-3's context size limitation

  • Impact on long conversations and prediction accuracy

  • Training and Parameters ([16:00])

  • Training process and matrix vector multiplication

  • GPT-3's 175 billion parameters organized into matrices

  • Softmax Function and Temperature ([20:00])

  • Use of softmax to create probability distributions

  • Role of temperature in adjusting output randomness

  • Final Thoughts on Attention Mechanism ([24:00])

  • Importance of attention in modern AI

  • Preview of upcoming content on attention blocks and deeper insights