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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

Training a CNN from Scratch

Deep neural networks have revolutionized computer vision. In fact, I'd argue that the advances made in computer vision in just the last few years have made deep neural networks something that many consumers use every day. We've already seen a computer vision classifier in Chapter 5, Using Keras for Multiclass Classification, where we used a deep network to classify handwritten digits. Now I want to show you how convolutional layers work, how you can use them, and how you can build your own convolutional neural network in Keras to build better, more powerful deep neural networks to solve computer vision problems.

We will cover the following topics in this chapter:

  • Introducing convolutions
  • Training a convolutional neural network in Keras
  • Using data augmentation
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