What is transfer learning in Python machine learning?
An example of an AI chatbot conversation about transfer learning in Python ML.
What is transfer learning in Python machine learning?
Transfer learning is a technique where a model developed for a specific task is reused as the starting point for a model on a second task.
How does it work in practice?
In practice, you typically take a pre-trained model, like those from TensorFlow or PyTorch, and fine-tune it on your specific dataset.
What are the benefits of using transfer learning?
The benefits include reduced training time, improved performance on small datasets, and leveraging existing knowledge from large datasets.
Can you give an example of a popular model used for transfer learning?
Sure! A popular model is VGG16, which is often used for image classification tasks.
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