📚 node [[batch|batch]]
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⥅ related node [[2010 05 12 tungle and batchbook now integrated]]
⥅ related node [[2010 06 08 batchbook user group and intro june 15th 2010]]
⥅ related node [[stephen batchelor]]
⥅ related node [[batch]]
⥅ related node [[batch_normalization]]
⥅ related node [[batch_size]]
⥅ related node [[mini batch]]
⥅ related node [[mini batch_stochastic_gradient_descent_(sgd)]]
⥅ node [[batch]] pulled by Agora
📓
garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Batch.md by @KGBicheno
batch
Go back to the [[AI Glossary]]
The set of examples used in one iteration (that is, one gradient update) of model training.
See also batch size.
⥅ node [[batch_normalization]] pulled by Agora
📓
garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Batch_Normalization.md by @KGBicheno
batch normalization
Go back to the [[AI Glossary]]
Normalizing the input or output of the activation functions in a hidden layer. Batch normalization can provide the following benefits:
Make neural networks more stable by protecting against outlier weights.
Enable higher learning rates.
Reduce overfitting.
⥅ node [[batch_size]] pulled by Agora
📓
garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Batch_Size.md by @KGBicheno
batch size
Go back to the [[AI Glossary]]
The number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference; however, TensorFlow does permit dynamic batch sizes.
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