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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

Overview of transfer learning

In Chapter 7, Convolutional Neural Networks, we trained a convolutional neural network on about 50,000 observations and we saw that, because of the complexity of the network and problem, we were overfitting on the training set after just a few epochs. If you recall, I had made the comment that 50,000 observations in our training set wasn't very large for a computer vision problem. That's true. Computer vision problems love data and the more data we can give them, the better they perform.

The deep neural networks that we might consider state-of-the-art in computer vision are often trained on a dataset called ImageNet. The ImageNet dataset (http://www.image-net.org/) is a 1,000 class classifier that contains 1.2 million images. That's more like it! A dataset this large allows researchers the ability to build really complex deep neural...

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