Optimizers in deep learning are algorithms or methods that adjust the attributes of neural networks, such as weights and learning rates, to minimize the loss function. This process is crucial for training models effectively, as it helps in reducing errors and improving the model’s performance. Common optimizers include Stochastic Gradient Descent (SGD), Adam, Adagrad, and Adadelta, each with its unique approach to updating weights.

SGD is widely used due to its simplicity and effectiveness, but it can be slow, especially with a fixed learning rate. Adam, often referred to as the “king of optimizers,” combines the benefits of momentum and RMSprop, making it suitable for complex datasets. Adagrad and Adadelta adapt the learning rate based on past gradients, which can lead to better convergence in certain scenarios ¹³⁵.

Understanding the strengths and weaknesses of each optimizer is essential for selecting the right one for a specific task, as different models and datasets may require different optimization strategies.