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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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Table of Contents (15) Chapters Close

Preface 1. Section 1: Neural Network Fundamentals FREE CHAPTER
2. What is Machine Learning? 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

  1. Describe the concept of transfer learning.
  2. When can the transfer learning process bring good results?
  3. What are the differences between transfer learning and fine-tuning?
  4. If a model has been trained on a small dataset with low variance (similar examples), is it an excellent candidate to be used as a fixed-feature extractor for transfer learning?
  5. The flower classifier built in the transfer learning section has no performance evaluation on the test dataset: add it.
  6. Extend the flower classifier source code, making it log the metrics on TensorBoard. Use the summary writers that are already defined.
  7. Extend the flower classifier to save the training status using a checkpoint (and its checkpoint manager).
  8. Create a second checkpoint for the model that reached the highest validation accuracy.
  9. Since the model suffers from overfitting, a good test is to reduce the number of neurons...
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