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In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]
A learning rate schedule changes the learning rate during learning and is most often changed between epochs/iterations. This is mainly done with two parameters: decay and momentum. There are many different learning rate schedules but the most common are time-based, step-based and exponential. [4]
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
The form the population iteration, which converges to , but cannot be used in computation, while the form the sample iteration which usually converges to an overfitting solution. We want to control the difference between the expected risk of the sample iteration and the minimum expected risk, that is, the expected risk of the regression function:
Many computer systems measure time and date using Unix time, an international standard for digital timekeeping.Unix time is defined as the number of seconds elapsed since 00:00:00 UTC on 1 January 1970 (an arbitrarily chosen time based on the creation of the first Unix system), which has been dubbed the Unix epoch.
Software timekeeping systems vary widely in the resolution of time measurement; some systems may use time units as large as a day, while others may use nanoseconds.For example, for an epoch date of midnight UTC (00:00) on 1 January 1900, and a time unit of a second, the time of the midnight (24:00) between 1 January 1900 and 2 January 1900 is represented by the number 86400, the number of ...
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
The batch size refers to the number of work units to be processed within one batch operation. Some examples are: The number of lines from a file to load into a database before committing the transaction. The number of messages to dequeue from a queue. The number of requests to send within one payload.