Gradienttitietoihin

Gradienttitietoihin is a term used in Finnish-language discussions to refer to data describing gradient information produced by gradient-based computational processes, such as neural network training or numerical optimization.

Definition and scope: It includes per-parameter gradients, layer-wise gradient norms, histograms of gradient magnitudes, and statistics of gradient updates over time. The concept helps characterize optimization dynamics and model behavior.

Data collection and formats: Gradient information is typically captured during backpropagation through autograd or hooks, and stored as tensors or arrays. Common representations are per-parameter gradients, per-layer gradient norms, gradient histograms, and tracking of gradient variance.

Applications: It can be used to monitor training stability, detect vanishing or exploding gradients, tune learning rates and regularization, diagnose bottlenecks, and inform the design of optimizers or training curricula.

Limitations and challenges: Collecting full gradient data adds computational and storage overhead. Gradient information can be noisy and difficult to interpret in high dimensions, and results may not generalize across tasks or architectures. Privacy considerations may arise if gradients leak training data.

See also: gradient descent, backpropagation, gradient clipping, gradient norms, saliency maps.