Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
How CUDA contexts, streams, and MPS compare: a context is a per-process container of GPU state, a stream is an in-order queue inside a context, and MPS lets multiple processes share a single GPU concurrently. Three layers, three different problems.
A CUDA stream is a queue of GPU operations that execute in order. Understanding streams is the difference between a GPU at 30% utilization and one running flat out — they are how kernels and memory copies overlap on real hardware.
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
Interactive guide to convolution in CNNs: visualize sliding windows, kernels, stride, padding, and feature detection with step-by-step demos.
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
Understand dilated (atrous) convolutions: how dilation rates expand receptive fields exponentially without extra parameters and how to avoid gridding artifacts.