Encord Computer Vision Glossary
A dataset in machine learning and artificial intelligence refers to a collection of data that is used to train and test algorithms and models. These datasets are crucial to the development and success of machine learning and AI systems, as they provide the necessary input and output data for the algorithms to learn from.
Datasets of all kinds, including both structured and unstructured data, can be employed in machine learning and AI. Data that has been organized in a certain manner, such as a spreadsheet or database table, is referred to as structured data. Given that information is already in a useful form, this type of data is simple to assess and deal with. Unstructured data, on the other hand, describes the information that isn't set out in a particular format, such as text or images. Before being used in machine learning and AI systems, this type of data needs to be further processed and analyzed.
Datasets from other sources, such as generated datasets, generated datasets, and private datasets, can also be utilized in machine learning and AI. Datasets that are publicly accessible to the public are known as "public datasets" and researchers and developers frequently use them to test and assess machine learning and artificial intelligence (AI) algorithms. Datasets that are proprietary are those that belong to a certain business or organization and are only accessible to select people or groups. Generated datasets, which are frequently used in the development of new systems, are datasets that are developed particularly for the purpose of training and testing machine learning and AI algorithms.
The quality and size of a dataset can also impact the performance of machine learning and AI systems. A dataset that is too small may not be representative of the problem that the system is trying to solve and may result in poor performance. On the other hand, a dataset that is too large may be difficult to process and may require additional resources, such as computing power and storage. Therefore, it is important to carefully select and prepare datasets for use in machine learning and AI systems to ensure optimal performance.
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