QHAdam Optimizer - Theory and Implementation in PyTorch
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about the Quasi-Hyperbolic Adam (QHAdam) optimizer in this 54-minute tutorial that breaks down both theoretical concepts and practical implementation. Explore the mathematical foundations, performance characteristics, and motivations behind QHAdam and QHM (Quasi-Hyperbolic Momentum) optimizers through detailed formula breakdowns and code examples. Follow along with both naive and official PyTorch implementations from the authors, examining the optimizer's architecture and real-world applications. Gain hands-on experience working with code examples available on GitHub while understanding the research paper's key findings and practical implications for machine learning optimization tasks.