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transfer learning

transfer learning

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What is transfer learning?

Transfer learning is a machine learning technique where a model trained on one task is repurposed as the starting point for a model on a second task. It leverages the knowledge gained from the initial task to improve the learning efficiency and performance on the new, related task. It is commonly used when the target task has limited labeled data, or when the computation cost of training from scratch is high. It can significantly reduce training time and improve model accuracy, especially in areas like image recognition, natural language processing, and speech recognition.

What other technologies are related to transfer learning?

transfer learning Complementary Technologies

Active learning can be used to strategically select data for transfer learning to improve efficiency.
mentioned alongside transfer learning in 21% (108) of relevant job posts
Meta-learning algorithms can learn how to transfer knowledge across different tasks, making them highly complementary to transfer learning.
mentioned alongside transfer learning in 34% (66) of relevant job posts
Domain adaptation is a specific type of transfer learning, thus it is complementary.
mentioned alongside transfer learning in 42% (54) of relevant job posts

Which job functions mention transfer learning?

Job function
Jobs mentioning transfer learning
Orgs mentioning transfer learning
Data, Analytics & Machine Learning

Which organizations are mentioning transfer learning?

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