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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

Setting up our environment

Before we examine data representations in TensorFlow, let’s set up our work environment. We will begin by importing TensorFlow and checking the version:

import tensorflow as tf
#To check the version of TensorFlow
print(tf.__version__)

When we run this block of code, we get the following output:

2.8.0

Hurray! We have successfully imported TensorFlow. Next, let us import NumPy and a couple of data types, as we will be using them shortly in this chapter:

import numpy as np
from numpy import *

We have successfully completed all our import steps without errors. We will now look at data representations in TensorFlow as our working environment is fully set up.

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