Batch: Difference between revisions

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Text replacement - "Category:Machine learning terms" to "Category:Machine learning terms Category:not updated"

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-==Introduction==
+{{see also|machine learning terms}}
-Batch learning, also referred to as "offline learning," is a type of machine learning in which data is processed in batches rather than real-time or online. With this approach, the model is trained using historical data and then applied to make predictions on new data sets.
+[[Batch]] is the set of [[example]]s used in one [[training]] iteration. The [[batch size]] determines the number of examples in a batch. In [[machine learning]], batches are subsets of [[data]] used for training a [[model]]. The goal is to break up an expansive [[dataset]] into smaller, more manageable chunks for easier processing.
-==Background==
+During the training process, the model is fed a batch of data, and its [[parameters]] are adjusted to minimize any discrepancy between the predicted output and actual output for that batch. This procedure, known as [[backpropagation]], involves computing gradients of the [[loss function]] with respect to model parameters which then serve to update those variables accordingly.
-Machine learning encompasses two primary approaches: supervised and unsupervised. Supervised learning involves training a model on labeled data, where both inputs and outputs are known; unsupervised learning takes advantage of an unknown dataset where inputs and outputs may not be known. Batch learning is another type of supervised learning where models are trained using large amounts of historical information before being applied to new data sets that have yet to be observed.
-==Advantages==
+The batch size is the number of examples used in each iteration of the training process. A larger batch size may provide faster progress but requires more memory and may not reach an optimal solution as quickly as desired. On the other hand, smaller batches offer better convergence and generalization rates. Ultimately, selecting a batch size depends on your available hardware as well as the specific problem being tackled.
-Batch learning offers several advantages. One major benefit is its capacity for handling large amounts of data, leading to more precise and resilient models. Furthermore, batch learning enables more complex models like deep neural networks which would otherwise require training in real-time. Furthermore, batch learning enables computationally expensive techniques like grid search or cross-validation which may improve model performance.
-==Disadvantages==
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
-Batch learning has its advantages, but it also has some drawbacks. One major issue is that it cannot be used for real-time applications where predictions must be made quickly and on-the-spot. Furthermore, batch learning requires large amounts of data which may be difficult or expensive to obtain. Furthermore, batch learning requires computationally expensive models which need to be trained on a large dataset before being applied to new data sets.
-==Explain Like I'm 5 (ELI5)==
-Batch learning is a method of instructing a computer how to do something by providing it with many examples at once, similar to giving a student their test after they have studied an entire chapter instead of one problem at a time. While batch learning can make the computer better at certain tasks, it requires many examples and may be slow in some cases.

Batch: Difference between revisions

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Revision as of 20:49, 17 March 2023