Optimizers in deep learning are essential algorithms that adjust the parameters of neural networks, such as weights and learning rates, to minimize the loss function. By effectively reducing errors during training, optimizers enhance the model’s performance and generalization capabilities. Various optimization techniques exist, including Batch Gradient Descent, Momentum, RMSprop, and Adam, each with unique advantages and disadvantages. Understanding these optimizers is crucial for developing efficient deep learning models, as they directly influence the convergence speed and accuracy of the training process. Ultimately, the choice of optimizer can significantly impact the success of a deep learning project.