Weak AI
Machine Learning
An initial training phase where the learning rate gradually increases from a small value. This helps stabilize early training in deep neural networks.
Detailed Explanation
Warmup Steps are an initial phase in machine learning training where the learning rate gradually increases from a small starting value to a designated level. This approach helps stabilize training, especially in deep neural networks, by preventing large weight updates early on, reducing the risk of divergence, and promoting smoother convergence during the subsequent training process.
Use Cases
- •Implement warmup steps in training deep neural networks to stabilize early learning and prevent divergence during initial epochs.
Related Terms
Other terms in the Machine Learning category