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Mini-batch: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ==Introduction== Mini-batch training is a machine learning technique used to efficiently train large datasets. This division of the entire dataset into smaller batches allows for faster training as well as improved convergence of the model to its optimal solution. ==Theoretical Background== Traditional machine learning relies on batch gradient descent to train the model on all data in one iteration. Unfortunately, when the dataset gro...") |
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Mini-batch training has several advantages over batch training, which involves running the model on all data at once. Some of these advantages include: | Mini-batch training has several advantages over batch training, which involves running the model on all data at once. Some of these advantages include: | ||
===Faster convergence== | ===Faster convergence=== | ||
Mini-batch training typically converges faster than batch training, since the model is updated more frequently. This is because the gradients calculated on a mini-batch are more representative of the entire dataset than those calculated from individual data points. | Mini-batch training typically converges faster than batch training, since the model is updated more frequently. This is because the gradients calculated on a mini-batch are more representative of the entire dataset than those calculated from individual data points. | ||
===Less memory usage== | ===Less memory usage=== | ||
Mini-batch training uses less memory than batch processing, as only a portion of the dataset is loaded into memory at once. This enables larger models to be trained using limited computational resources. | Mini-batch training uses less memory than batch processing, as only a portion of the dataset is loaded into memory at once. This enables larger models to be trained using limited computational resources. | ||
===Improved generalization== | ===Improved generalization=== | ||
Mini-batch training can improve generalization performance as the model is exposed to a wider variety of examples during instruction, making it more likely that it will learn how to apply its knowledge to new, unseen situations. | Mini-batch training can improve generalization performance as the model is exposed to a wider variety of examples during instruction, making it more likely that it will learn how to apply its knowledge to new, unseen situations. | ||
===Better optimization== | ===Better optimization=== | ||
Mini-batch training can aid the optimization process, as the stochastic nature of gradients computed from mini-batches can help the model escape local minima and find more favorable solutions. | Mini-batch training can aid the optimization process, as the stochastic nature of gradients computed from mini-batches can help the model escape local minima and find more favorable solutions. | ||
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Mini-batch training has its advantages, but also some drawbacks, such as: | Mini-batch training has its advantages, but also some drawbacks, such as: | ||
===Hyperparameter tuning== | ===Hyperparameter tuning=== | ||
The selection of mini-batch size is an important parameter that needs to be optimized for optimal performance. This can be a laborious process requiring extensive experimentation. | The selection of mini-batch size is an important parameter that needs to be optimized for optimal performance. This can be a laborious process requiring extensive experimentation. | ||
===Noise== | ===Noise=== | ||
Mini-batch training can be noisy due to the approximations made when computing gradients from a mini-batch; this may lead to oscillations in the learning process and slower convergence speeds. | Mini-batch training can be noisy due to the approximations made when computing gradients from a mini-batch; this may lead to oscillations in the learning process and slower convergence speeds. | ||
===Hardware requirements== | ===Hardware requirements=== | ||
Mini-batch training requires more CPU/GPU memory than training on a single data point, making it difficult to train large models with limited hardware resources. | Mini-batch training requires more CPU/GPU memory than training on a single data point, making it difficult to train large models with limited hardware resources. | ||