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Keras documentation: Transfer learning & fine-tuning

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Author: fchollet Date created: 2020/04/15 Last modified: 2020/05/12 Description: Complete guide to transfer learning & fine-tuning in Keras. Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis. Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch. A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate.


Transfer Learning in Deep Learning

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It is a branch of Machine Learning which uses a simulation of the human brain which is known as neural networks. These neural networks are made up of neurons that are similar to the fundamental unit of the human brain. The neurons make up a neural network model and this field of study altogether is named deep learning. The end result of a neural network is called a deep learning model. Mostly, in deep learning, unstructured data is used from which the deep learning model extracts features on its own by repeated training on the data.


Transfer Learning

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Transfer learning is a machine learning technique. With the help of this article master transfer learning by using pretrained models in deep learning.


Transfer learning & The art of using Pre-trained Models in Deep Learning

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I hope that you would now be able to apply pre-trained models to your problem statements. Be sure that the pre-trained model you have selected has been trained on a similar data set as the one that you wish to use it on. There are various architectures people have tried on different types of data sets and I strongly encourage you to go through these architectures and apply them on your own problem statements. Please feel free to discuss your doubts and concerns in the comments section.