Introduction
Published in · 6 min read · Jan 20, 2024
--
The field of machine learning, particularly in the context of neural network training, involves a myriad of hyperparameters that influence the learning process. Among these, batch size and learning rate are pivotal. While it is a common misconception that these two parameters are inversely related, the relationship is far more nuanced. This essay aims to demystify this relationship, exploring how these parameters interact and affect the learning dynamics of neural networks.
In the realm of machine learning, the relationship between batch size and learning rate is like a dance: finding the right rhythm and balance is key to a harmonious performance.
Understanding Batch Size and Learning Rate
Before delving into their relationship, it is crucial to understand what each parameter signifies. Batch size refers to the number of training samples used in one iteration of model updates. It plays a vital role in determining the model’s generalization and computational efficiency. On the other hand, the learning rate dictates the step size during the optimization process, influencing the convergence speed and stability of the training process.