adagrad-algorithm
The Adagrad algorithm is an adaptive learning rate optimization technique designed for training machine learning models, particularly in the context of online learning and stochastic optimization. It adjusts the learning rate for each parameter individually based on the historical gradients, allowing for more efficient convergence. By accumulating the squared gradients over time, Adagrad enables the model to take larger steps in directions with infrequent updates and smaller steps in directions with frequent updates. This characteristic makes it particularly useful for sparse data scenarios, where certain features may be more informative than others. Overall, Adagrad enhances the training process by improving stability and performance.
Adagrad
Implements Adagrad algorithm. For further details regarding the algorithm we refer to Adaptive Subgradient Methods for Online Learning and Stochastic Optimization . params ( iterable ) – iterable of p...
📚 Read more at PyTorch documentation🔎 Find similar documents
Adamax
Implements Adamax algorithm (a variant of Adam based on infinity norm). For further details regarding the algorithm we refer to Adam: A Method for Stochastic Optimization . params ( iterable ) – itera...
📚 Read more at PyTorch documentation🔎 Find similar documents