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.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
Read More »The term Business Intelligence (or BI) defines the use of information technologies to identify, discover, and analyze business data, such as business [...]
Read More »When it comes to gaining new clients, everything is joy and satisfaction for being able to provide them with our service or sell them our product in the best way possible, and we [...]
Read More »Business opportunities are everywhere and many times we do not know which are the sectors with the greatest potential for entrepreneurship.
Read More »
