The Unix epoch (or Unix time or POSIX time or Unix timestamp) is the number of seconds that have elapsed since January 1, 1970 (midnight UTC/GMT), not counting leap seconds (in ISO 8601: 1970-01-01T00:00:00Z).Literally speaking the epoch is Unix time 0 (midnight 1/1/1970), but 'epoch' is often used as a synonym for Unix time.Some systems store epoch dates as a signed 32-bit integer, which might cause problems on January 19, 2038 (known as the Year 2038 problem or Y2038).The converter on this page converts timestamps in seconds (10-digit), milliseconds (13-digit) and microseconds (16-digit) to readable dates.
In chronology and periodization, an epoch or reference epoch is an instant in time chosen as the origin of a particular calendar era. The "epoch" serves as a reference point from which time is measured.
The moment of epoch is usually decided by congruity, or by following conventions understood from the epoch in question. The epoch moment or date is usually defined from a specific, clear event of change, an epoch event. In a more gradual change, a deciding moment is chosen when the epoch criterion was reached.
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I looked for what is that mean , then I conclude that we use an epoch of data to update weights, If I choose to train the data with 5 epochs as pybrain advice, the dataset will be divided into 5 subsets, and the wights will update 5 times as maximum.
I'm familiar with online training where the wights are updated after each sample data or feature vector, My question is how to be sure that 5 epochs will be enough to build a model and setting the weights probably? what is the advantage of this way on online training? Also the term "epoch" is used on online training, does it mean one feature vector?
This has nothing to do with batch or online training per se. Batch means that you update once at the end of the epoch (after every sample is seen, i.e. #epoch updates) and online that you update after each sample (#samples * #epoch updates).
You can't be sure if 5 epochs or 500 is enough for convergence since it will vary from data to data. You can stop training when the error converges or gets lower than a certain threshold. This also goes into the territory of preventing overfitting. You can read up on early stopping and cross-validation regarding that.
so far, as i understand it, an epoch (as runDOSrun is saying) is a through use of all in the TrainingSet (not DataSet. because DataSet = TrainingSet + ValidationSet). in mini batch training, you can sub divide the TrainingSet into small Sets and update weights inside an epoch. 'hopefully' this would make the network 'converge' faster.
The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset. One epoch means that each sample in the training dataset has had an opportunity to update the internal model parameters.
1610s, epocha, "point marking the start of a new period in time" (such as the founding of Rome, the birth of Christ, the Hegira), from Medieval Latin epocha, from Greek epokhe "stoppage, fixed point of time," from epekhein "to pause, take up a position," from epi "on" (see epi-) + ekhein "to hold" (from PIE root *segh- "to hold"). Transferred sense of "a period of time" is 1620s; geological usage (not a precise measurement) is from 1802. 041b061a72