Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
*Transfer learning *is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
Traditional Machine Learning VS Transfer Learning (source: Dipanjan Sarkar)
The traditional machine learning approach generalizes unseen data based on patterns learned from the training data, whereas for transfer learning, it begins from previously learned patterns to solve a different task.
Basic Idea of Transfer Learning (source: Integrate.ai)
In this post, we shall focus on the pre-trained model approach as it is commonly used in the field of deep learning.
A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. One can use the pre-trained model as it is or use transfer learning to customize this model to a given task.
The intuition behind transfer learning is that if a model is trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. We can then take advantage of these learned feature maps without having to start from scratch by training a large model on a large dataset.
Let’s take a deep dive into VGG16 - a notable pre-trained model submitted to the Large Scale Visual Recognition Challenge in 2014.
Google Reveals "What is being Transferred” in Transfer Learning. Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community.
Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.
Introduction to Transfer Learning for NLP using fast.ai. This is the third part of a series of posts showing the improvements in NLP modeling approaches.
A practical and hands-on example to know how to use transfer learning using TensorFlow. We will learn how to use transfer learning for a classification task.
Transfer learning is a method of reusing a pre-trained model knowledge for another task. It can be used for classification, regression and clustering problems.