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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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 Araujo Araujo
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Araujo
 Zafar Zafar
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Zafar
 Tzanidou Tzanidou
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Tzanidou
 Burton Burton
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Burton
 Patel Patel
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Patel
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Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
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 1. References 2. Other Books You May Enjoy Index

Appendix 2. Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Deep Learning with TensorFlow - Second Edition Giancarlo Zaccone, Md. Rezaul Karim

ISBN: 9781788831109

  • Apply deep machine intelligence and GPU computing with TensorFlow
  • Access public datasets and use TensorFlow to load, process, and transform the data
  • Discover how to use the high-level TensorFlow API to build more powerful applications
  • Use deep learning for scalable object detection and mobile computing
  • Train machines quickly to learn from data by exploring reinforcement learning techniques
  • Explore active areas of deep learning research and applications

Natural Language Processing with TensorFlow Thushan Ganegedara

ISBN: 9781788478311

  • Core concepts of NLP and various approaches to natural language processing
  • How to solve NLP tasks by applying TensorFlow functions to create neural networks
  • Strategies to process large amounts of data into word representations that can be used by deep learning applications
  • Techniques for performing sentence classification and language generation using CNNs and RNNs
  • About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks
  • How to write automatic translation programs and implement an actual neural machine translator from scratch
  • The trends and innovations that are paving the future in NLP
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