Data augmentation is a technique used in machine learning and artificial intelligence to increase the amount of data available to train a model. This technique involves generating new data from existing data by applying transformations that maintain the original labels.
Data augmentation is commonly used in computer vision and image processing applications, where transformations such as rotation, cropping, colour inversion and resizing are applied to create variations of the original images. In this way, the model can learn to recognise relevant image features regardless of their orientation, size and other factors.
Data augmentation is an effective technique to avoid over-fitting in machine learning models, where the model fits too closely to the training data and does not generalise well to new data. By increasing the amount and variety of training data, the model's ability to generalise to new data and improve its accuracy and performance can be improved.
The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
Read More »Cloud computing services or solutions, whether in Spain or anywhere else in the world, are infrastructures, platforms or systems that are used in the cloud.
Read More »The Big Data market is booming. Although the need to transform data into information for decision making is not new, the need to [...]
Read More »The semantic web or "internet of knowledge" is an extension of the current web. Unlike the latter, the semantic web is based on proportional [...]
Read More »
