Data preprocessing: Ensuring high-quality dataset by performing operations like data cleaning, normalization, and standardization can improve the training speed and performance of the model.
Model selection: Choose a model architecture that suits the task, based on the complexity of the task and the characteristics of the dataset, in order to avoid overly complex or overly simple models.
Hyperparameter tuning: Searching for the optimal combination of hyperparameters through methods such as grid search and random search can enhance the performance of the model.
Learning rate adjustment: Dynamically adjusting the learning rate can help the model converge faster and prevent it from getting stuck in local optimal solutions during the training process.
Batch normalization: Using batch normalization in deep neural networks can accelerate the training process and improve model performance.
Regularization: Adding a regularization term can prevent the model from overfitting and improve its generalization ability.
Early stopping: in the training process, monitor the performance of the validation set to determine if the model is overfitting, in order to prevent further training leading to a decrease in performance.
Data augmentation: Increasing the diversity of the dataset and improving the generalization ability of the model can be achieved through operations such as rotation, flipping, and scaling.
Ensemble learning: Using methods like Bagging and Boosting in ensemble learning can combine multiple models to improve model performance.
GPU acceleration: Utilizing a GPU for model training can greatly speed up the training process and enhance model performance.
