Experimenting with AdaBelief Optimizer

This repository focuses on experimenting with the AdaBelief optimizer, an advanced optimization algorithm that adapts the learning rate based on the belief in the gradient. AdaBelief combines the strengths of Adam and RMSProp while improving generalization performance

Features

• AdaBelief Optimizer: Implements the AdaBelief algorithm for improved training stability
• Comparison with Adam: Experimentation includes performance comparisons with traditional optimizers like Adam
• PyTorch Implementation: The project is implemented in PyTorch for flexibility and ease of use