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Hands-On Neural Networks with TensorFlow 2.0

You're reading from   Hands-On Neural Networks with TensorFlow 2.0 Understand TensorFlow, from static graph to eager execution, and design neural networks

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Length 358 pages
Edition 1st Edition
Languages
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Author (1):
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Paolo Galeone Paolo Galeone
Author Profile Icon Paolo Galeone
Paolo Galeone
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Neural Network Fundamentals
2. What is Machine Learning? FREE CHAPTER 3. Neural Networks and Deep Learning 4. Section 2: TensorFlow Fundamentals
5. TensorFlow Graph Architecture 6. TensorFlow 2.0 Architecture 7. Efficient Data Input Pipelines and Estimator API 8. Section 3: The Application of Neural Networks
9. Image Classification Using TensorFlow Hub 10. Introduction to Object Detection 11. Semantic Segmentation and Custom Dataset Builder 12. Generative Adversarial Networks 13. Bringing a Model to Production 14. Other Books You May Enjoy

Exercises

The following exercises are of fundamental importance and you are invited to answer to every theoretical question and solve all of the code challenges presented:

  1. What is the semantic segmentation?
  2. Why is semantic segmentation a difficult problem?
  3. What is deconvolution? Is the deconvolution operation in deep learning a real deconvolution operation?
  4. It is possible to use Keras models as layers?
  5. Is it possible to use a single Keras Sequential model to implement a model architecture with skip connections?
  6. Describe the original U-Net architecture: what are the differences between the custom implementation presented in this chapter and the original one?
  7. Implement, using Keras, the original U-Net architecture.
  8. What is a DatasetBuilder?
  9. Describe the hierarchical organization of TensorFlow Datasets.
  10. The _info method contains the description of every single example of the dataset...
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