Training machine learning models is not without challenges. Some of the most common difficulties include:
Overfitting – The model learns patterns too well, including noise, making it perform poorly on new data.
Underfitting – The model is too simple and fails to capture the underlying patterns in the data.
Data Quality Issues – Noisy, incomplete, or imbalanced datasets can negatively impact training.
Computational Costs – Training deep learning models requires significant processing power and memory.
Hyperparameter Tuning – Finding the best configuration for learning rates, batch sizes, and other parameters can be complex.
To mitigate these challenges, techniques such as **data augmentation, dropout layers, regularization, and early stopping** are commonly used. Additionally, cross-validation ensures that the model performs well across different subsets of data.
