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Deep Learning Quick Reference

You're reading from  Deep Learning Quick Reference

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Pages 272 pages
Edition 1st Edition
Languages
Author (1):
Mike Bernico Mike Bernico
Profile icon Mike Bernico

Table of Contents (15) Chapters

Preface 1. The Building Blocks of Deep Learning 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Transfer Learning with Pretrained CNNs

Transfer learning is amazing. In fact, in a book full of amazing things, it might be the most amazing thing I have to tell you about. If not, it's at least perhaps the most useful and pragmatic deep learning technique I can teach you. Transfer learning helps you solve deep learning problems, especially computer vision problems, with very little data and very little computational power relative the problem's scope. In this chapter, we're going to talk about what transfer learning is, when you should use it, and finally how to do transfer learning in Keras.

We will cover the following topics in this chapter:

  • Overview of transfer learning
  • When transfer learning should be used
  • The impact of source/target volume and similarity
  • Transfer learning in Keras
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