Welcome to the Machine Learning Datasets Documentation page. Here, you will find comprehensive information about various datasets available for machine learning research and development.
Overview
Machine learning datasets are crucial for training and testing machine learning models. They provide the necessary data to build and evaluate algorithms. This section covers the most popular and widely used datasets in the field of machine learning.
Datasets
1. MNIST Database
The MNIST database is a large database of handwritten digits commonly used for training various image processing systems. It contains a training set of 60,000 examples and a test set of 10,000 examples.
2. ImageNet
ImageNet is a large visual database designed for use in visual object recognition software research. It contains over 14 million images and is widely used for benchmarking machine learning algorithms.
More information about ImageNet
3. CIFAR-10
The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 test images.
More information about CIFAR-10
4. UCI Machine Learning Repository
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community. It contains a wide range of datasets across various domains.
More information about UCI Machine Learning Repository
Conclusion
Machine learning datasets play a vital role in the development and advancement of machine learning algorithms. By exploring and utilizing these datasets, researchers and developers can build more accurate and efficient models.