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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Deep learning for Scalable Object Detection

In this section, we will learn how to make image recognition using TensorFlow. Also, we will be using transfer learning which is the ability to use the network weights of a pre-trained model that was trained on large dataset like ImageNet), usually people use transfer learning when they have small datasets. So we will be starting with a pre-trained model and use it on another problem. We will retrain this model on a similar problem because if we started from scratch then it will take days.

Figure 1: Flowers dataset (Image by TesnorFlow, Source: https://www.tensorflow.org/images/daisies.jpg)

There are lots of pre-trained networks that you can start using and they already packed with TensorFlow. In this section, we will be using Inception V3 network which is trained for the ImageNet (http://image-net.org/ ). This network can differentiate between 1000 different classes...

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