Skip to content

Roadmaps Resources Practice Projects Journal Community

Search/Sign in

LearnPath

Learn anything. Build everything. Free, structured learning paths built from the best resources on the web.

Learn

Roadmaps
Skills
Resources
Practice

Build

Challenges
Projects
My learning
Search

Project

Journal
About & RRRTX Labs
Community
Open source
Privacy
Terms

LEARNPATH

Built by RRRTX Labs

Free and open source. No certificates. No paywall. No trackers.

Generative AI Engineer / Neural networks

Learn
Practice
Challenge
Prove
Not started

Generative AI Engineer

01Python syntax & types
02Neural networks
03Transformers & LLMs
04Prompting & specification
05Embeddings & RAG
06Fine-tuning basics
07GenAI evaluation & safety
08AI ethics & responsibility

View the full graph →

Stage 01 · 01 · Foundations · skill 2 of 2

Neural networks

Layers, activations, backprop as computational graph. Intuition before frameworks.

Recommended first: linear-algebra-intuition, ml-fundamentals

After this you will be able to

Draw a 2-layer network
Explain what a loss is optimizing
Relate gradient descent to walking downhill

3Blue1Brown · why we picked it

Completing this unlocks: PyTorch basics, Transformers & LLMs

Previous Next: Transformers & LLMs

Practice

No playground attached to this skill. The practice lab has every runtime.

Evidence

Learned
Practiced
Challenge passed
Used in a project

Not demonstrated yet

Notes

Notes