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Adadelta is one of the gradient descent-based optimization algorithms designed to provide a more efficient learning process. Adadelta offers significant advantages in scenarios common in deep learning and machine learning, where manually tuning hyperparameters such as the learning rate is challenging, by employing adaptive learning rates. Core Concepts Adadelta enhances the basic gradient descent method by introducing a mechanism that automatically adjusts the learning rate for each parameter. T

AdamW (Adam with Weight Decay) is a variant of the Adam optimization algorithm and provides a significant improvement related to model regularization. This variant aims to enhance Adam’s overall performance and generalization capability by incorporating an L2 penalty term (weight decay). In the traditional Adam algorithm, weight decay is computed together with the gradient updates; however, AdamW applies this penalty term independently of the update step, enabling more effective regularization.

Adafactor is an efficient, low-memory optimization algorithm developed by Google, specifically designed for memory-intensive models such as large-scale language models. It was first introduced in 2018 in the paper titled "Adafactor: Adaptive Learning Rates with Sublinear Memory Cost". Like the Adam algorithm, Adafactor performs moment-based updates but computes second-moment estimates using significantly less memory, thereby enabling the training of large models. Adafactor Optimization Algorithm

Adamax is a generalized version of the Adam algorithm, distinguished by its operation over the infinity norm (∞-norm). Introduced by Kingma and Ba in 2015 alongside Adam, this algorithm aims to provide more stable and effective updates, particularly in high-dimensional parameter spaces. By replacing the L2 norm in Adam with the infinity norm, Adamax controls the influence of large gradients and delivers a more stable learning process. Adamax Optimization Algorithm Key Difference Between Adam and