What are Hyperparameters in Deep Learning?
In the realm of deep learning, hyperparameters are crucial settings that govern the training process of neural networks. Unlike model parameters, which are learned during training, hyperparameters are set before the training begins and significantly influence model performance.
Types of Hyperparameters
- Learning Rate: This determines how much to adjust the model parameters during optimization. A small learning rate may require more epochs to converge, while a large one might overshoot optimal values.
- Batch Size: This parameter defines the number of training samples used in one iteration. Smaller batch sizes can lead to noisy gradient estimates, while larger sizes may lead to more stable gradients.
- Number of Epochs: This indicates how many times the learning algorithm will work through the entire training dataset. More epochs can improve training but risk overfitting.
- Network Architecture: This includes the number of layers, types of layers (e.g., convolutional, recurrent), and the number of neurons per layer.
- Dropout Rate: This is used to prevent overfitting by randomly dropping units during training.
Importance of Hyperparameter Tuning
Tuning hyperparameters is essential for achieving optimal performance. Techniques such as Grid Search, Random Search, and Bayesian Optimization are commonly used for this purpose. Properly adjusted hyperparameters can significantly enhance the model's ability to generalize well on unseen data, which is a primary goal in machine learning.