Transfer learning is a machine learning technique that involves using the knowledge acquired by a model trained on a specific task to improve the performance of another model on a related but different task.
In transfer learning, a pre-trained model, called a source model, which has learned to solve a specific task, is used to initialise the weights of the target model, which is used to solve a related but different task. The idea behind transfer learning is that the pre-trained model has already learned general and useful features of the source task domain, which may also be useful for the target task.
Transfer learning is commonly used in computer vision and natural language processing applications, where training data may be limited or expensive to obtain. It is also used in cases where the target task is sufficiently different from the source task to require model adaptation, but there are still some similarities that can be exploited.
Clustering methods, or grouping, are a fundamental part of the data analysis process, since they allow an automatic segmentation of the data [...]
Read More »Churn, or customer churn rate, is a constant challenge for today's businesses. The ability to retain customers is a constant challenge for today's companies.
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 »Today we are going to talk about how to foresee payment problems and foresee the problems in those customers who are currently not giving it to you. In G [...]
Read More »
