<ul><li>While large batch sizes can improve computational parallelism, they may degrade model performance.</li><li>Yann LeCun suggests that a batch size of 32 is optimal for model training and performance.</li><li>In a recent study, researchers found that batch sizes between 2 and 32 outperform larger sizes in the thousands.</li><li>Smaller batch sizes enable more frequent gradient updates, resulting in more stable and reliable training.</li></ul>

32 is the Magic Number: Deep Learning’s Perfect Batch Size Revealed!

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