Machine learning datasets are crucial for training and testing algorithms. They provide the necessary data for models to learn from and improve their performance. Here are some popular datasets in the field of machine learning:
MNIST Dataset: A dataset of handwritten digits commonly used for training various image processing systems. It contains 60,000 training images and 10,000 testing images. Learn more about MNIST.
ImageNet: A large visual database designed for use in visual object recognition software research. It contains over 14 million images, categorized into 20,000 different classes.
CIFAR-10: A dataset of 60,000 32x32 color images, containing 10 different classes, such as airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
UCI Machine Learning Repository: A collection of datasets for machine learning research, including regression, classification, and clustering tasks.
These datasets are widely used by researchers and developers in the field of machine learning. They help in building and improving algorithms for various applications, such as image recognition, natural language processing, and speech recognition.