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 saw the power of CNNs. We began by examining the challenges faced by DNNs for visual recognition tasks. Next, we journeyed through the anatomy of CNNs, zooming in on the various moving parts, such as the convolutional, pooling, and fully connected layers. Here, we saw the impact and effect of different hyperparameters, and we also discussed the boundary effect. Next, we moved on to using all we learned to build a real-world weather classifier using two DNNs and a CNN. Our CNN model outperformed the DNNs, showcasing the strength of CNNs in handling image-based problems. Also, we discussed and applied some TensorFlow functions that streamline data preprocessing and modeling when we are working with image data.

By now you should have a good understanding of the structure and operations of CNNs and how to use them to solve real-world image classification problems, as well as utilizing various tools in TensorFlow to effectively and efficiently preprocess image...