Dataset taken from the StatLib library which is maintained at Carnegie Mellon University. Samples contain 13 attributes of houses at different locations around the Boston suburbs in the late 1970s.Targets are the median values of the houses at a location(in k$).
Dataset of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images.
Dataset of 50,000 32x32 color training images, labeled over 100 categories, and 10,000 test images.
Dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST. The class labels are:
Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer "3" encodes the 3rd most frequent word in the data. This allows for quick filtering operations such as: "only consider the top 10,000 most common words, but eliminate the top 20 most common words". As a convention, "0" does not stand for a specific word, but instead is used to encode any unknown word.
Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
Dataset of 11,228 newswires from Reuters, labeled over 46 topics. As with the IMDB dataset, each wire is encoded as a sequence of word indexes (same conventions).