ADADELTA is a novel adaptive learning rate method for gradient descent that dynamically adjusts learning rates per dimension using only first-order information. It has minimal computational overhead compared to standard stochastic gradient descent (SGD) and requires no manual tuning of the learning rate. The method is robust to noisy gradients, different model architectures, and various data modalities. It performs well on tasks such as MNIST digit classification and large-scale speech recognition. The method addresses two main drawbacks of existing approaches: the continual decay of learning rates and the need for a manually selected global learning rate. ADADELTA uses a windowed accumulation of squared gradients and an exponentially decaying average of the squared updates to compute the learning rate. This ensures that the learning rate adapts dynamically to the problem, improving convergence and performance. In experiments, ADADELTA outperformed other methods such as SGD, Momentum, and ADAGRAD on the MNIST dataset, achieving a lower test error rate. It also performed well on a large-scale speech recognition task, showing robustness to noise and varying hyperparameters. The method's effective learning rates adapt to the problem, with larger learning rates for lower layers of the network to compensate for vanishing gradients. ADADELTA is computationally efficient and can be applied in both local and distributed environments. It does not require explicit annealing schedules, which makes it suitable for a wide range of applications. The method's ability to adapt learning rates per dimension and its robustness to various input data types and hyperparameters make it a promising approach for training deep neural networks.ADADELTA is a novel adaptive learning rate method for gradient descent that dynamically adjusts learning rates per dimension using only first-order information. It has minimal computational overhead compared to standard stochastic gradient descent (SGD) and requires no manual tuning of the learning rate. The method is robust to noisy gradients, different model architectures, and various data modalities. It performs well on tasks such as MNIST digit classification and large-scale speech recognition. The method addresses two main drawbacks of existing approaches: the continual decay of learning rates and the need for a manually selected global learning rate. ADADELTA uses a windowed accumulation of squared gradients and an exponentially decaying average of the squared updates to compute the learning rate. This ensures that the learning rate adapts dynamically to the problem, improving convergence and performance. In experiments, ADADELTA outperformed other methods such as SGD, Momentum, and ADAGRAD on the MNIST dataset, achieving a lower test error rate. It also performed well on a large-scale speech recognition task, showing robustness to noise and varying hyperparameters. The method's effective learning rates adapt to the problem, with larger learning rates for lower layers of the network to compensate for vanishing gradients. ADADELTA is computationally efficient and can be applied in both local and distributed environments. It does not require explicit annealing schedules, which makes it suitable for a wide range of applications. The method's ability to adapt learning rates per dimension and its robustness to various input data types and hyperparameters make it a promising approach for training deep neural networks.