How can I implement Stochastic Gradient Descent in my machine learning model?

How can I implement Stochastic Gradient Descent in my machine learning model?

To implement Stochastic Gradient Descent (SGD), you first need to define your loss function and the model parameters.

What are the key parameters I should tune for SGD?

The key parameters to tune include the learning rate, batch size, and the number of epochs.

Can you explain how the learning rate affects the training process?

The learning rate determines how much to adjust the model weights with respect to the loss gradient. A high learning rate can lead to overshooting the minimum, while a low rate may slow down convergence.

What are some common pitfalls when using SGD?

Common pitfalls include choosing an inappropriate learning rate, not normalizing your data, and overfitting due to too many epochs.