{"id":36619,"date":"2024-11-01T09:50:02","date_gmt":"2024-11-01T09:50:02","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36619"},"modified":"2024-11-01T11:52:29","modified_gmt":"2024-11-01T11:52:29","slug":"deep-learning-pytorch-course-transfer-learning","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36619\/","title":{"rendered":"Deep Learning PyTorch Course, Transfer Learning"},"content":{"rendered":"

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1. Introduction<\/h2>\n

\n Transfer Learning is a very important technology in the fields of machine learning and deep learning. This technology refers to the process of reusing the weights or parameters learned for one task on another similar task. Transfer learning can save a lot of time and resources when the number of samples is small or when using a new dataset.\n <\/p>\n

2. The Necessity of Transfer Learning<\/h2>\n

\n Collecting data and training models require a lot of time and cost. Therefore, by utilizing the knowledge learned from existing models for new tasks, efficiency can be increased. For example, if a model for image classification has already been trained, such a model can be utilized for similar tasks like plant classification.\n <\/p>\n

3. The Concept of Transfer Learning<\/h2>\n

\n In general, transfer learning includes the following steps:\n <\/p>\n