Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
4 (2)
Book Image

TensorFlow Developer Certificate Guide

4 (2)
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

The Yelp Polarity dataset

In this experiment, we will work with the Yelp Polarity dataset. This dataset is made up of a training size of 560,000 reviews and 38,000 reviews for testing, with each entry consisting of a text-based review and a label (positive – 1 and negative – 0). The data was drawn from customer reviews of restaurants, hair salons, locksmiths, and so on. This dataset presents some real challenges – for example, the reviews are made up of text with varying lengths, from short reviews to very long reviews. Also, the data contains the use of slang and different dialects. The dataset is available at this link: https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews.

Let’s start building our model:

  1. We will begin by loading the required libraries:
    import tensorflow as tf
    import tensorflow_datasets as tfds
    from tensorflow.keras.preprocessing.text import Tokenizer
    from tensorflow.keras.preprocessing.sequence import pad_sequences...