Warm Up Training
Warm up training is crucial for stabilizing the gradient in the initial epochs, allowing for dramatic adjustments without noise interference. This approach accelerates convergence without necessarily improving accuracy, as simply increasing training iterations can lead to fluctuations. A consistent 1% warm up period has proven effective across various tasks, minimizing the need for extensive tuning.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Deep Gradient Compression for Distributed Training with Song Han - #146
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