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Hands-On Convolutional Neural Networks with TensorFlow

You're reading from   Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789130331
Length 272 pages
Edition 1st Edition
Languages
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Authors (5):
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Richard Burton Richard Burton
Author Profile Icon Richard Burton
Richard Burton
Giounona Tzanidou Giounona Tzanidou
Author Profile Icon Giounona Tzanidou
Giounona Tzanidou
Iffat Zafar Iffat Zafar
Author Profile Icon Iffat Zafar
Iffat Zafar
Leonardo Araujo Leonardo Araujo
Author Profile Icon Leonardo Araujo
Leonardo Araujo
Nimesh Patel Nimesh Patel
Author Profile Icon Nimesh Patel
Nimesh Patel
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Table of Contents (12) Chapters Close

Preface 1. Setup and Introduction to TensorFlow FREE CHAPTER 2. Deep Learning and Convolutional Neural Networks 3. Image Classification in TensorFlow 4. Object Detection and Segmentation 5. VGG, Inception Modules, Residuals, and MobileNets 6. Autoencoders, Variational Autoencoders, and Generative Adversarial Networks 7. Transfer Learning 8. Machine Learning Best Practices and Troubleshooting 9. Training at Scale 10. References 11. Other Books You May Enjoy

Summary

In this chapter, we have explained how programming with TensorFlow works and how to set up your work environment for working with TensorFlow. We have also looked at how to build, train, and evaluate your own linear model using TensorFlow for classifying iris flowers. In doing so, we briefly looked at loss functions and gradient descent optimizers.

In the next chapter, we will learn more about some key deep-learning concepts, including convolutional neural networks. We'll also look at how to use TensorFlow to build and train deep neural networks.

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Hands-On Convolutional Neural Networks with TensorFlow
Published in: Aug 2018
Publisher: Packt
ISBN-13: 9781789130331
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