Description: Regularization techniques in neural networks are methods designed to prevent overfitting, a phenomenon where the model learns the training data too well, including noise and random variations, resulting in poor performance on unseen data. These techniques are crucial for improving the model’s generalization, allowing it to perform better in real-world situations. Among the most common techniques are Dropout, which involves randomly disabling a percentage of neurons during training, forcing the network to learn more robust representations. Another technique is L2 regularization, which penalizes large weights in the network, promoting simpler solutions and preventing the model from fitting too closely to the data. Batch Normalization is also used, which helps stabilize and accelerate training by normalizing the activations of the layers. These techniques not only improve the model’s accuracy but also allow for training deeper and more complex networks without falling into overfitting. In summary, regularization is an essential component in the design of neural networks, ensuring that models are both accurate and generalizable.
