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

Summary

In this chapter, we explored the foundations of NLP. We began by looking at how to handle real-world text data, and we explored some preprocessing ideas, using tools such as Beautiful Soup, requests, and regular expressions. Then, we unpacked various ideas, such as tokenization, sequencing, and the use of word embedding to transform text data into vector representations, which not only preserved the sequential order of text data but also captured the relationships between words. We took a step further by building a sentiment analysis classifier using the Yelp Polarity dataset from the TensorFlow dataset. Finally, we performed a series of experiments with different hyperparameters in a bid to improve our base model’s performance and overcome overfitting.

In the next chapter, we will introduce Recurrent Neural Networks (RNNs) and see how they do things differently from the DNN we used in this chapter. We will put RNNs to the test as we will build a new classifier with...