Adam optimizer

The Adam optimizer is a widely used algorithm in deep learning that combines the advantages of two other popular optimization techniques: momentum and RMSProp. It adapts the learning rate for each parameter based on the first and second moments of the gradients, allowing for efficient convergence in training neural networks. By maintaining a moving average of both the gradients and their squares, Adam effectively reduces the impact of noisy updates and improves the model’s performance. Its robustness and efficiency make it a preferred choice for many machine learning practitioners when optimizing complex models.