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Hands-On Neural Networks with TensorFlow 2.0

You're reading from   Hands-On Neural Networks with TensorFlow 2.0 Understand TensorFlow, from static graph to eager execution, and design neural networks

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Length 358 pages
Edition 1st Edition
Languages
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Author (1):
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Paolo Galeone Paolo Galeone
Author Profile Icon Paolo Galeone
Paolo Galeone
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Neural Network Fundamentals
2. What is Machine Learning? FREE CHAPTER 3. Neural Networks and Deep Learning 4. Section 2: TensorFlow Fundamentals
5. TensorFlow Graph Architecture 6. TensorFlow 2.0 Architecture 7. Efficient Data Input Pipelines and Estimator API 8. Section 3: The Application of Neural Networks
9. Image Classification Using TensorFlow Hub 10. Introduction to Object Detection 11. Semantic Segmentation and Custom Dataset Builder 12. Generative Adversarial Networks 13. Bringing a Model to Production 14. Other Books You May Enjoy

Transfer learning

Only academia and some industries have the required budget and computing power to train an entire CNN from scratch, starting from random weights, on a massive dataset such as ImageNet.

Since this expensive and time-consuming work has already been done, it is a smart idea to reuse parts of the trained model to solve our classification problem.

In fact, it is possible to transfer what the network has learned from one dataset to a new one, thereby transferring the knowledge.

Transfer learning is the process of learning a new task by relying on a previously learned task: the learning process can be faster, more accurate, and require less training data.

The transfer learning idea is bright, and it can be successfully applied when using convolutional neural networks.

In fact, all convolutional architectures for classification have a fixed structure, and we can reuse...

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