1 مستودع
Graphical representations of mathematical weight updates and gradient movements during neural network training.
Distinguishing note: None of the candidates relate to machine learning interpretability or gradient tracking; they cover database schema and mobile updates.
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This project is a collection of interactive graphical tools designed for monitoring neural network training, latent space mappings, and the internal mechanisms of transformers. It functions as a visual learning environment for understanding how large language models process tokens and an educational tool for analyzing the interactions between generators and discriminators within adversarial networks. The system provides a browser-based transformer architecture visualizer to show the mathematical operations used for token prediction in real time. It also includes a generative adversarial netwo
Provides real-time visualization of gradient movements to show how generator networks update to fool discriminators.