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TensorFlow Developer Certificate Guide

You're reading from   TensorFlow Developer Certificate Guide Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803240138
Length 344 pages
Edition 1st Edition
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Author (1):
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Oluwole Fagbohun Oluwole Fagbohun
Author Profile Icon Oluwole Fagbohun
Oluwole Fagbohun
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1 – Introduction to TensorFlow
2. Chapter 1: Introduction to Machine Learning FREE CHAPTER 3. Chapter 2: Introduction to TensorFlow 4. Chapter 3: Linear Regression with TensorFlow 5. Chapter 4: Classification with TensorFlow 6. Part 2 – Image Classification with TensorFlow
7. Chapter 5: Image Classification with Neural Networks 8. Chapter 6: Improving the Model 9. Chapter 7: Image Classification with Convolutional Neural Networks 10. Chapter 8: Handling Overfitting 11. Chapter 9: Transfer Learning 12. Part 3 – Natural Language Processing with TensorFlow
13. Chapter 10: Introduction to Natural Language Processing 14. Chapter 11: NLP with TensorFlow 15. Part 4 – Time Series with TensorFlow
16. Chapter 12: Introduction to Time Series, Sequences, and Predictions 17. Chapter 13: Time Series, Sequences, and Prediction with TensorFlow 18. Index 19. Other Books You May Enjoy

Technical requirements

We will use a Google Colaboratory (Google Colab) notebook as our work environment, as it is a free, cloud-based Jupyter notebook that is easy to use, providing us with GPU and TPU backends. For the coding exercises in this chapter, we will use python >= 3.8.0, along with the following packages, which can be installed using the pip install command:

  • tensorflow>=2.7.0
  • tensorflow-datasets==4.4.0
  • pillow==8.4.0
  • pandas==1.3.4
  • numpy==1.21.4
  • scipy==1.7.3
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