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Practical Guide to Transfer Learning in TensorFlow for Multiclass Image Classification
Clearly-explained step-by-step tutorial for implementing transfer learning in image classification
Often we do not have access to a wealth of labeled data or computing power to build image classification deep learning models from scratch.
Fortunately, transfer learning empowers us to develop robust image classifiers for our specific classification tasks, even if we have limited resources.
In this easy-to-follow walkthrough, we will learn how to leverage pre-trained models as part of transfer learning in TensorFlow to classify images effectively and efficiently.
Table of Contents
(1) Motivation and Benefits of Transfer Learning (Optional)
(2) About the Dataset
(3) Step-by-Step Guide
(4) Wrapping it up
The accompanying GitHub repo to this article can be found here.
(1) Motivation and Benefits of Transfer Learning
OPTIONAL READING
Transfer learning is a powerful method that uses pre-trained models as the starting point for creating new models instead of building them from scratch.
