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Hands-On Neural Networks with TensorFlow 2.0

You're reading from  Hands-On Neural Networks with TensorFlow 2.0

Product type Book
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Pages 358 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Paolo Galeone Paolo Galeone
Profile icon Paolo Galeone
LinkedIn
Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy. In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.
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Table of Contents (15) Chapters

Preface Section 1: Neural Network Fundamentals
What is Machine Learning? Neural Networks and Deep Learning Section 2: TensorFlow Fundamentals
TensorFlow Graph Architecture TensorFlow 2.0 Architecture Efficient Data Input Pipelines and Estimator API Section 3: The Application of Neural Networks
Image Classification Using TensorFlow Hub Introduction to Object Detection Semantic Segmentation and Custom Dataset Builder Generative Adversarial Networks Bringing a Model to Production Other Books You May Enjoy

Exercises

The following exercises are programming challenges, combining the expressive power of the TensorFlow Python API and the advantages brought by other programming languages:

  1. What is a checkpoint file?
  2. What is a SavedModel file?
  3. What are the differences between a checkpoint and a SavedModel?
  4. What is a SignatureDef?
  5. Can a checkpoint have a SignatureDef?
  6. Can a SavedModel have more than one SignatureDef?
  7. Export a computational graph as a SavedModel that computes the batch matrix multiplication; the returned dictionary must have a meaningful key value.
  8. Convert the SavedModel defined in the previous exercise into its TensorFlow.js representation.
  9. Use the model.json file we created in the previous exercise to develop a simple web page that computes the multiplication of matrices chosen by the user.
  10. Restore the semantic segmentation model defined in Chapter 8, Semantic Segmentation...
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Authors (1)

Profile icon Paolo Galeone
LinkedIn
Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy. In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.
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See other products by Paolo Galeone

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