What are some common mistakes in neural network training?

What are some common mistakes in neural network training?

Some common mistakes include overfitting, underfitting, improper data preprocessing, and not tuning hyperparameters.

How can I avoid overfitting in my model?

You can avoid overfitting by using techniques like dropout, regularization, and ensuring you have enough training data.

What is the importance of data preprocessing?

Data preprocessing is crucial as it helps in normalizing the data, handling missing values, and improving model performance.

Can you explain hyperparameter tuning?

Hyperparameter tuning involves adjusting the parameters that govern the training process to optimize model performance.