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.
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Explore technical articles related to deep learning. Find in-depth analysis, tutorials, and insights.
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Articles Related to deep learning
SAM's Multi-Mask Ambiguity: A Visual Deep Dive
Deep dive into how SAM resolves point prompt ambiguity through three-mask output design, IoU prediction, and intelligent mode switching.
YOLOv11 Loss Functions Explained: Interactive Visual Guide
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.
Accelerating PyTorch Models: Inside torch.compile’s Kernel Optimization
Explore how torch.compile accelerates PyTorch models through kernel optimization. This article visualizes PyTorch kernel structures and their file mappings.
How to Fix PyTorch "view size is not compatible" Error: Memory Layout Explained
Learn why PyTorch throws the "view size is not compatible" error, understand tensor memory layout, and discover optimal solutions with performance benchmarks.
Understanding GGML Files: A Deep Dive into Quantization and Visualization of File Structure
Understand GGML file structure and quantization formats used by local LLMs. Visual guide to how llama.cpp stores and loads model weights efficiently.
Quantization Deep Dive: From FP32 to INT4 - The Complete Guide
Master neural network quantization with interactive visualizations. Explore QAT, PTQ, GPTQ, AWQ, and SmoothQuant methods for efficient model deployment.
How TensorRT Works: Deep Dive into NVIDIA Inference Optimization Engine
Explore TensorRT optimization: layer fusion, INT8 quantization, kernel auto-tuning, and deployment strategies with 8+ interactive visualizations.
Kernel Fusion: A Smart Way to Enhance Neural Networks Performance
Dive deep into Kernel Fusion, a technique that combines multiple neural network operations into unified kernels improving performance in deep learning models.
YOLOv5 Simplified: A Beginner's Visual Guide to Understanding Each Step
Visual guide to YOLOv5 architecture for beginners. Understand backbone, neck, and detection head components with step-by-step visualizations.