variables: 935635

This table contains all the variables of all the datasets we have at Our World In Data. Note that this includes only the information about the variables like name and default display settings, not the data itself.

Data license: CC-BY

This data as json

id name unit description createdAt updatedAt code coverage timespan datasetId sourceId shortUnit display columnOrder originalMetadata grapherConfigAdmin shortName catalogPath dimensions schemaVersion processingLevel processingLog titlePublic titleVariant attributionShort attribution descriptionShort descriptionFromProducer descriptionKey descriptionProcessing licenses license grapherConfigETL type sort dataChecksum metadataChecksum
935635 Training dataset size datapoints   2024-06-19 14:36:00 2024-07-08 16:38:43       6571     
{
    "unit": "datapoints",
    "zeroDay": "1949-01-01",
    "yearIsDay": true,
    "numDecimalPlaces": 0
}
 0     training_dataset_size__datapoints grapher/artificial_intelligence/2024-06-19/epoch_compute_intensive/epoch_compute_intensive#training_dataset_size__datapoints   2 major           The number of examples provided to train an AI model. Typically, more data results in a more comprehensive understanding by the model.   
[
    "Training data size refers to the volume of data employed to train an artificial intelligence (AI) model effectively. It's a representation of the number of examples that the model learns from during its training process. It is a fundamental measure of the scope of the data used in the model's learning phase.",
    "To grasp the concept of training data size, imagine teaching a friend the art of distinguishing different types of birds. In this analogy, each bird picture presented to your friend corresponds to an individual piece of training data. If you showed them 100 unique bird photos, then the training data size in this scenario would be quantified as 100.",
    "Training data size is an essential indicator in AI and machine learning. First and foremost, it directly impacts the depth of learning achieved by the model. The more extensive the dataset, the more profound and comprehensive the model's understanding of the subject matter becomes. Additionally, a large training data size contributes significantly to improved recognition capabilities. By exposing the model to a diverse array of examples, it becomes adept at identifying subtle nuances, much like how it becomes skilled at distinguishing various bird species through exposure to a large variety of bird images."
]
       
{
    "note": "Confirmed large-scale AI models are those where the training compute exceeds 10\u00b2\u00b3 floating-point operations (FLOP)."
}
 int 
[]
 ce9794088bd67cebb772ba5bc0e2c603 f5704ce08886df6236f8023974c06d70

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