Supervised learning is a type of machine learning in which a machine learning model is trained using labelled examples. That is, the model is trained with input data and corresponding correct answers.
In supervised training, the model learns to make predictions or classifications from the input data and corresponding labels. For example, in image classification, the model can be trained with images labelled with corresponding categories (e.g. dogs, cats, cars, etc.) so that it can classify new images into one of those categories.
There are different types of supervised training algorithms, including regression and classification algorithms. Regression algorithms are used to predict continuous numerical values, such as the price of a house or the number of sales in a given month. Classification algorithms are used to predict discrete categories, such as the image classification categories mentioned above.
Supervised training is a widely used learning technique in the field of artificial intelligence and machine learning, as it allows training accurate and useful models for a wide variety of applications. However, a limitation of this type of training is that it requires large labelled datasets, which can be costly and difficult to obtain in some cases.
Today, consumers of any type of product or service have become demanding. It has been a long time since they were served anything [...]
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 first thing you need to know is the limits of AI and after mastering the basic concepts you will be able to build a large commercial software with intelligent [...]
Read More »OpenAI is a technology company created by the main leaders in artificial intelligence that, in its beginnings, defined itself as an organization that [...]
Read More »