From the course: Deep Learning with Python: Optimizing Deep Learning Models

Adaptive Delta (AdaDelta) - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Adaptive Delta (AdaDelta)

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- [Instructor] Adaptive Delta, commonly known as AdaDelta, addresses AdaGrad's diminishing learning rate problem by restricting the window of accumulated past gradients to a fixed size. Instead of accumulating all past squared gradients, AdaDelta uses a moving average of the gradients, similar to RMSprop, however, it goes a step further by adapting the update step size, effectively, eliminating the need for a default learning rate. AdaDelta adapts learning rates based on the moving window of gradient updates, addressing the diminishing learning rate issue observed in AdaGrad. By focusing on recent gradients, it maintains a consistent learning rate throughout training, facilitating better convergence. Additionally, AdaDelta performs well on problems with sparse gradients, similar to AdaGrad, making it suitable for various applications in natural language processing on other domains where data sparsity is a concern. While AdaDelta addresses some of the limitations of AdaGrad, it introduces additional complexity to the optimization algorithm. It's calculations are more intricate, which can make it more challenging to understand and implement correctly, especially for those new to deep learning. AdaDelta is less widely used compared to optimizers like Adam and RMSprop. As a result, there may be less community support, fewer tutorials, and limited empirical studies on its performance across different types of problems. This can make it harder to find the resources when troubleshooting or optimizing models using AdaDelta.

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