Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
3 (1)
Book Image

TensorFlow Developer Certificate Guide

3 (1)
By: Oluwole Fagbohun

Overview of this book

The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Table of Contents (20 chapters)
1
Part 1 – Introduction to TensorFlow
6
Part 2 – Image Classification with TensorFlow
12
Part 3 – Natural Language Processing with TensorFlow
15
Part 4 – Time Series with TensorFlow

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.