Today we continue Machine Learning domain post, focus on Benefits of Transfer Learning to Deep Learning. Transfer Learning (TL) for Deep Learning (DL) is a machine learning (ML) approach and techniques used to save time and resources from having to train multiple machine learning models from scratch to complete similar tasks, in particular for tasks
The developing of technologies nowadays spring up like mushrooms. Everyday there will be always new discovery. Despite been known to discovery a long time ago, transfer learning have evolve a lot since then. Today we’re going to talk about Transfer Learning in IoT (Internet of Things). To make the linkage between the two issues, transfer
Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. This post focuses on discussing the theory of transfer learning, under Deep Learning (DL) in Machine Learning (ML) context from the educational aspect. The first theory of transfer
What is Transfer Learning? As human we know and practice related knowledge from past learning and using it to the new one. The more related our previous knowledge with the new job, the easier we can become proficient. The idea is same with Transfer Learning which knowledge learned in one or more source tasks is