🤓 Do you want to finally understand what AI and Large Language Models (LLMs) are all about, or would you rather stay in the dark and miss out on the future? 🧐

I’ve put together a YouTube playlist that's good for anyone curious about how these powerful technologies work behind the scenes. The entire playlist is less than an hour long and can be watched in about 40 minutes at 1.2x speed. While this isn’t the perfect playlist, it’s a collection of my favorite videos to kickstart your understanding of AI and LLMs.

🎥 Playlist Link: https://www.youtube.com/playlist?list=PLrlR9K40f6XEcXcgKBidpUyrSBhAvJ4b7

What’s on the Playlist?

  • AI, Machine Learning, Deep Learning, and GenAI (10 min) – Understand where LLMs fit in the AI landscape.
  • How Large Language Models Work (5 min) – Discover core functions and business use cases.
  • LLMs in 5 Minutes (Simplified) (8 min) – A visual and easy-to-follow explanation of how LLMs work.
  • LLMs - Everything You Need To Know (25 min) – A deeper dive into LLM architecture and challenges.
  • Stanford CS229 | Building LLMs (1h44m, optional) – A comprehensive lecture that’s one of the best on AI and LLMs, although it requires some basic understanding.

Why It Matters

  • Gain Knowledge: Understand the basic and advanced concepts of AI and LLMs.
  • Informed Conversations: Learn what these technologies entail to discuss and explore their potential.
  • Explore Innovation: See how AI and LLMs can impact various fields and perhaps even your own.

Key Terms to Know

  • Neural Network: Algorithms designed to mimic human brain functions for pattern recognition.
  • Transformer: The architecture behind LLMs, crucial for understanding their work.
  • Training: The process of teaching the model using massive datasets.
  • Tokens: Units of text that models process for input and output.
  • Fine-Tuning: Adjusting a general model with specific data for improved results.
  • Embedding: Vector representations that help models understand the relationship between words.
  • Context Window: The maximum number of tokens a model can process at once.
  • Parameters: The values the model learns during training to represent its “knowledge”.
  • Inference: The model generating predictions based on learned data. Zero-shot Learning: The model’s ability to perform tasks it wasn’t explicitly trained for.
  • Retrieval-Augmented Generation (RAG): Enhancing LLM responses with external information.
  • FLOPs (Floating-Point Operations): A measure of computational effort used during model training.
  • Loss: Indicates the inaccuracy of a model during training; lower loss means better performance.

I advise you to review these key terms before watching the playlist to avoid getting lost in the technical details.

Feel free to share your best LLM videos to help us all understand how things work inside. Let’s explore these resources at our own pace, share our thoughts, and embark on this enlightening journey together! 🚀