A Gentle Introduction to Transfer Learning for Deep Learning

Last Updated on September 16, 2019

Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task.

It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems.

In this post, you will discover how you can use transfer learning to speed up training and improve the performance of your deep learning model.

After reading this post, you will know:

  • What transfer learning is and how to use it.
  • Common examples of transfer learning in deep learning.
  • When to use transfer learning on your own predictive modeling problems.

Kick-start your project with my new book Deep Learning for Computer Vision, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

For an example of how to use transfer learning in computer vision, see the post:

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