{"id":297,"date":"2021-10-26T00:00:00","date_gmt":"2021-10-26T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=297"},"modified":"2023-06-22T09:27:08","modified_gmt":"2023-06-22T09:27:08","slug":"transfer-learning","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/transfer-learning\/","title":{"rendered":"Transfer Learning: The Highest Leverage Deep Learning Skill You Can Learn"},"content":{"rendered":"\n\n\n
Training a deep learning model can take days, weeks, or even months.<\/p>\n\n\n\n
Transfer Learning could solve this problem. It\u2019s a machine learning method where trained models are reused as starting points for new tasks. This speeds up training and improves performance on related issues.<\/p>\n\n\n\n
It is one of the most popular methods in Deep Learning because it saves time and money by reusing pre-trained models from other tasks that have a similar structure to your own task. In this post, you\u2019ll learn how to transfer learning works and how you can use it to speed up your deep learning training process!<\/p>\n\n\n\n
Transfer learning is a machine learning technique in which a model trained on a specific task is reused as part of the training process for another, different task.<\/p>\n\n\n\n
Here is a simple analogy to help you understand how transfer learning works: imagine that one person has learned everything there is to know about dogs. In contrast, another person has learned everything about cats. If both people are asked, \u201cWhat\u2019s an animal with four legs, a tail, and barks?\u201d The person who knows all about dogs would answer \u201cdog,\u201d while the individual who knows everything about cats would say \u201ccat.\u201d<\/p>\n\n\n\n
Since both people already know half of what they need to know to solve the problem at hand, each one only has to fill in their missing information before answering correctly. This is how transfer learning works in machine learning. Combining the information that one model has learned about certain features with another model\u2019s knowledge of other features can result in a new task.<\/p>\n\n\n\n