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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 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

When transfer learning should be used

Transfer learning works really well when you have limited data and when a network exists that solves a similar problem. You can use transfer learning to bring state-of-the art networks and giant volumes of data to an otherwise small problem. So, when should you use transfer learning? Anytime you can! But, there are two stipulations that I'd like you to think about first. We will discuss them in the following sections.

Limited data

The question I'm most often asked when it comes to computer vision and transfer learning is: How many images do I have to have? It's a difficult question to answer because, as we will see in the next section, more is usually better. A better question...

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