作者：禅与计算机程序设计艺术 文章来源：https://www.toymoban.com/news/detail-734567.html
1.简介
Transfer learning is a machine learning technique that allows a model to learn new knowledge from an existing trained model on a similar task. Transfer learning can be useful for a variety of tasks such as image classification, object detection, and speech recognition. However, transfer learning has its own set of challenges including data availability, complexity of the original model, computational resources required during training, etc. In this article, we will explore how to use TensorFlow Hub (TF-Hub) for implementing transfer learning in computer vision applications. TF-Hub provides pre-trained models that have been trained on large datasets and can be fine-tuned for specific tasks by retraining them on smaller amounts of data. This makes it easier to leverage pre-trained models for transfer learning while still benefiting from their ability to generalize to new domains or tasks. We will demonstrate how to apply transfer 文章来源地址https://www.toymoban.com/news/detail-734567.html
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