Neural network training often involves optimizing model parameters. The Adam optimizer is popular for its adaptive learning rates for each parameter, but it demands high memory. This poses challenges with large-scale models, often leading to increased training time due to CPU offloading. The recent Adam-mini optimizer offers a solution by reducing memory usage while maintaining performance. It simplifies learning rate assignments by grouping parameters and using a single rate per block. This change cuts memory consumption significantly and boosts efficiency, enabling larger batch sizes on GPUs. For MQL5 implementation, modifications across classes are necessary to integrate Adam-mini, ensuring memory optimization and efficient computation.
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