To improve the convergence of a neural network, adjusting the learning rate is one of the most effective techniques. A high learning rate can speed up training but may cause the model to overshoot the optimal point, while a low learning rate can make training slow and inefficient. Using adaptive learning rate optimizers like Adam or RMSprop helps dynamically adjust the learning rate, balancing speed and stability. For instance, Adam adapts learning rates for each parameter, ensuring faster and smoother convergence.
Another critical factor is weight initialization. Proper initialization prevents the gradients from vanishing or exploding, which can significantly delay convergence. Modern initialization methods like He initialization (for ReLU activations) or Xavier initialization (for tanh activations) are widely used. These methods scale weights to maintain stable gradients during backpropagation, enhancing the training process.
Regularization techniques such as dropout and batch normalization also improve convergence. Batch normalization stabilizes input distributions across layers, enabling faster learning. Dropout prevents overfitting, allowing the model to generalize better. Combining these techniques with a well-tuned architecture ensures a more efficient and reliable training process.