Numerical Sensitivity: Why FP16 Breaks NAdam and How to Fix It
Visual exploration of floating-point arithmetic and numerical stability. Learn why NAdam fails in FP16 and how machine epsilon affects deep learning.
Deep dive into machine learning, computer vision, and software engineering. Expert insights on AI, local LLMs, quantization, and practical implementation details from real-world projects.
Visual exploration of floating-point arithmetic and numerical stability. Learn why NAdam fails in FP16 and how machine epsilon affects deep learning.
Deep dive into how SAM resolves point prompt ambiguity through three-mask output design, IoU prediction, and intelligent mode switching.
Understand YOLOv11's loss functions through interactive visualizations. Compare IoU variants (GIoU, DIoU, CIoU), explore Distribution Focal Loss (DFL), and see why anchor-free detection matters.
H.264 Part 3: Implementation guide covering profiles, levels, hardware vs software encoding, and real-world video compression applications.
H.264 Part 2: Dive into DCT transforms, quantization strategies, rate-distortion optimization, and entropy coding at the mathematical heart of compression.
H.264 Part 1: Explore video compression fundamentals, core pipeline architecture, block-based processing, and motion estimation with interactive demos.