tf.optimizers.Adam

The tf.optimizers.Adam is a popular optimization algorithm used in deep learning for training neural networks. It is a stochastic gradient descent (SGD) optimization algorithm that uses adaptive learning rates based on the exponential moving averages of the gradients.

The Adam optimizer is designed to overcome the limitations of other optimization algorithms such as the vanishing or exploding gradient problem, and slow convergence caused by a fixed learning rate. It adapts the learning rate for each parameter based on the first and second moments of the gradients, which helps it converge faster and more efficiently.

The Adam optimizer provides several hyperparameters that can be tuned to optimize the training process, such as the learning rate, beta1, beta2, and epsilon. The default values are often suitable for most cases, but they can be adjusted based on the specific problem and dataset.

Overall, the Adam optimizer is a powerful and widely used optimization algorithm that has been shown to perform well on a variety of deep learning tasks.