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 discussed overfitting in image classification and explored the different techniques to overcome it. We started by examining what overfitting is and why it happens, and we discussed how we can apply different techniques such as early stopping, model simplification, L1 and L2 regularization, dropout, and data augmentation to mitigate against overfitting in image classification tasks. Furthermore, we applied each of these techniques in our weather dataset case study and saw, hands-on, the effects of these techniques on our case study. We also explored combining these techniques in a quest to build an optimal model. By now, you should have a good understanding of overfitting and how to mitigate it in your own image classification projects.

In the next chapter, we will dive into transfer learning, a powerful technique that allows you to leverage pre-trained models for your specific image classification tasks, saving time and resources while achieving impressive...