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.

AI Engineer / Neural networks

Learn
Practice
Challenge
Prove
Not started

AI Engineer

01Python syntax & types
02Lists, dicts, sets
03Linear algebra intuition
04Probability & statistics
05SQL fundamentals
06Data wrangling
07Machine learning fundamentals
08Neural networks
09PyTorch basics
10Evaluation & metrics
11MLOps basics
12Containers & orchestration
13AI ethics & responsibility

View the full graph →

Stage 03 · 03 · Deep learning · skill 1 of 2

Neural networks

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

Recommended first: Linear algebra intuition, Machine learning 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: PyTorch basics

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