How to Improve Performance With Transfer Learning for Deep Learning Neural Networks

An interesting benefit of deep learning neural networks is that they can be reused on related problems. Transfer learning refers to a technique for predictive modeling on a different but somehow similar problem that can then be reused partly or wholly to accelerate the training and improve the performance of a model on the problem of interest. In deep learning, this means reusing the weights in one or more layers from a pre-trained network model in a new model and either keeping the weights fixed, fine tuning them, or adapting the weights entirely when training the model. In this tutorial, you will discover how to use transfer learning to improve the performance deep learning neural networks in Python with Keras. How to Improve Performance With Transfer Learning for Deep Learning Neural Networks Photo by Damian Gadal, some rights reserved. Transfer learning generally refers to a process where a model trained on one problem is used in some way on a second related problem.