All lessons
features
2 free lessons tagged features across AI, Robotics. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
AI
advancedSparse autoencoders: reading the features hidden inside a neural network
Why neurons are polysemantic, how the superposition hypothesis explains it, and how sparse autoencoders use dictionary learning to pull a model's activations apart into monosemantic, steerable features, plus the failure modes and the top-k and gated fixes.
12 steps·~18 min
Robotics
advancedCameras and Visual Perception
From photons to 3D geometry: the pinhole model, intrinsic matrix K, lens distortion, feature matching, stereo depth, and where CNNs help (and fail) in robot perception pipelines.
9 steps·~14 min