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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high ...
The MNIST database (Modified National Institute of Standards and Technology database[1]) is a large database of handwritten digits that is commonly used for training various image processing systems. [2][3] The database is also widely used for training and testing in the field of machine learning. [4][5] It was created by "re-mixing" the ...
t. e. TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. [3][4] It is one of the two most popular deep learning libraries alongside PyTorch.
The Pile (dataset) The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1][2] It is composed of 22 smaller datasets, including 14 new ones. [1]
If both the validation score and the training score converge to a value that is too low with increasing size of the training set, it will not benefit much from more training data. [8] In the machine learning domain, there are two implications of learning curves differing in the x-axis of the curves, with experience of the model graphed either ...
The naive version of the algorithm is easy to implement by computing the distances from the test example to all stored examples, but it is computationally intensive for large training sets. Using an approximate nearest neighbor search algorithm makes k- NN computationally tractable even for large data sets.
List of datasets in computer vision and image processing. This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.