Artificial Intelligence
The broad goal: build systems that perform tasks associated with human intelligence, whether they learn from data or follow authored rules.
The outer field
Generative AI Engineering Master Tree
Build the foundations before the stack.
Explore the first branch: what GenAI is, where it sits inside AI, and why language and image models learn to generate differently.
1 of 7 nodes explored14%
The tree becomes useful when every branch answers a different question.
Select a node to reveal its job. Your explored branches are saved on this device.
Active branch
Foundations
One field, nested scopes.
These labels are related, but not interchangeable. Read from the broadest ambition to the generative behavior.
Machine Learning
A subset of AI where behavior is learned from examples and data instead of being specified only as explicit rules.
AI ⟶ learning from data
Deep Learning
A subset of machine learning built from multi-layer neural networks that learn useful representations.
ML ⟶ layered neural networks
Generative AI
Models that learn patterns in data well enough to produce new text, images, audio, code, or other content.
Often DL ⟶ new content
Same generative ambition. Different training signal.
Open each model family to compare what it processes and how it creates.
Recognition is easy. Retrieval proves the foundation.
Answer all three questions. The score and your selections persist after reload.
A master tree is only mastered one branch at a time.
Your exported record turns today’s explored nodes and scored answers into the next study session’s starting point.
Review the map