cnn
3 free lessons tagged cnn 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.
Architectures for Order Book Data, and Why They Help Less Than Expected
Convolutional and recurrent networks have been applied to order book data with published success, and the architectures encode real assumptions about the book's structure. This lesson explains what each one assumes, why the gains over simple baselines are smaller than headline numbers suggest, and where the modelling effort is better spent.
Cameras 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.
Convolutional Neural Networks
Understand why fully-connected layers fail at image scale, then build up the CNN toolkit: convolutions, kernels, stride, padding, feature maps, pooling, and parameter sharing. Finish with the ResNet residual connection idea that unlocked networks of 100+ layers.