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How to Learn AI - Learning Curriculum

TL;DR

If you want to learn more about AI you reached the right place - you don’t need a PhD to learn the basics of AI. Start by building a mental model of the "Blackbox": BEFORE - IN - AFTER the Black Box is a big deal already to look behind the scenes.

BEFORE → IN → AFTER

Why Learn AI Now

  • Understand the Black Box: move from magic to mental models.
  • You press a button and get a result - but what is really happening in AI under the hood to produce this?
  • Improve results: tune your skills and level up your game.
  • Spot real use cases: with experience you make better decisions where to use AI - and where it might even harm.
  • Manage risk: privacy, bias, hallucinations, prompt injection.
  • Future-proof your career: this is just the beginning.

If AI feels overwhelming, that’s normal. The ecosystem moves fast, the math can be heavy, and the marketing is loud. The way through is a clear mental model and some guideance for navigation.

The Basics: AI Timeline & AI Glossary

How did we get here?

Explore the AI Timeline with the big influencial events listed in chronological order.

What are all these crazy terms?

The Glossary has you covered - including a quiz to validate your knowledge.

Mental Model: BEFORE → INSIDE → AFTER the AI Black Box

Think in three phases: before, in, and after the black box. Many of the concepts described here are listed with details and interaction in the AI Visualizations section.

BEFORE - IN - AFTER

BEFORE the AI Black Box

  • Tokenize: text → tokens (results in Vocabulary).
  • Architecture: Transformer with layers; parameters are learned.
    • Open Weights vs Closed Weights
    • On Prem vs Cloud
    • Text, vision, multimodal
  • Embeddings: tokens → vectors that capture meaning.
  • Model type: text-only, vision, or multimodal; open vs. closed weights; on‑prem vs. cloud.
  • Training data + objectives, watch our for bias in training data.

INSIDE the AI Black Box

  • Giant Mathematical Function
    • hundreds of layers and billions of Parameters - learned during large scale training
    • high-dimensional space
    • Non-deterministic by design
  • Probabilities: predicts the next token distribution, not “truth”.
  • Attention: each token looks at others to decide what matters.
  • Sampling: temperature & top_p control creativity vs. determinism.

AFTER the AI Black Box

  • Context window: how much the model can “hold in mind”.
  • Prompt patterns: role, task, context, constraints, output template.
  • Tools: function calls, retrieval (RAG), code execution, image tools.
  • Guardrails: safety filters, validation, traceability and evaluation.

This mental model allows you to visualize structure and flow - and allows you to improve your knowledge in areas that need improving.

Dive deeper: Dedicated Articles & Interactive Visualizations

Explore these sections in dentro.de/ai to gain insights on dedicated subjects that accomplish the Learning Curriculum.

Articles in the AI Blog section

  • How Large Language Models like ChatGPT Work
  • AI Model Lifecycle - Analogy Explained with Cars
  • Analogy to understand Open Source in generative AI Models

Interactive AI/ML Visualizations and Videos

Interactive Websites

  • Tiktokenizer
  • Moebio Mind
  • Transformer Explainer
  • BertViz
  • LLM Visualization
  • LLM Architectures

Videos

  • Large Language Models explained briefly
  • Deep Dive into LLMs like ChatGPT

Fun Challenge

  • Bouncing Ball Hexagon
  • Vitruvian Robot