What is a learning rate in ML?

A learning rate in machine learning is a hyperparameter that controls how much a model should adjust its weights during training. It plays a key role in the optimization algorithm used to update the model parameters in order to minimize the loss function.

Learning Rate Importance

The learning rate determines the size of the steps taken towards optimizing the model's performance. If the learning rate is too small, the model may take a long time to converge to the optimal solution. On the other hand, if the learning rate is too large, the model may not converge at all or even diverge, missing the optimal solution.

Impact on Model Performance

The choice of learning rate can significantly impact the convergence rate and overall performance of the model. It is crucial to find the right balance to ensure efficient training and achieve the best results.

Adjusting Weights

By adjusting the learning rate, the model can fine-tune its weights effectively during training. This process helps the model learn the underlying patterns in the data and improve its predictive power.