// Stage 0 · Prep: AI Literacy
What AI, ML, Deep Learning and LLMs Are
One map of the AI family, and how an LLM “predicts the next token”.
about 3 hours2 algorithms1 paperslesson text ready
01Goals
- Explain AI ⊃ machine learning ⊃ deep learning ⊃ generative AI
- Understand four words: token, parameter, training, inference
- Know what LLMs are good and bad at
02Read on this site
03Algorithms to master
Each card has the original paper, prerequisites and resources.
04Must-read papers
- Language Models are Few-Shot Learners ↗2020 ✓ verified in Essentials
05Courses & videos
- Elements of AI ↗CoursesMULTI
- DeepLearning.AI：AI For Everyone ↗CoursesEN
- Karpathy ↗VideoEN
06Hands-on: project, code & exercises
Free, official or author-published pages. Most run in the browser or on a free Colab GPU.
PROJECT
Ask two different chat models the same 10 questions (facts, arithmetic, writing), log each one’s error types and write a one-page comparison.
- 1Google Teachable Machine ↗Interactive
No code: train an image classifier with your webcam in 5 minutes and feel data → training → prediction.
- 2TensorFlow Playground ↗Interactive
Change layers, activations and learning rate in the browser and watch the decision boundary move.
07Self-test
Answer in your head, then open to check.
What is a “parameter”?
A number inside the model that training adjusts; together they decide how input becomes output.
How does an LLM generate text?
It repeatedly predicts a probability for the next token given the context, and samples from it.
Why can the same question get two different answers?
Generation involves sampling randomness (temperature, top-p).
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