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

Overfitting in ML

From the previous chapters, we now know what overfitting is and its adverse effect when used on unseen data. Let's take a step further by digging into what the root causes of overfitting are, how we can spot overfitting when we build our models, and some important strategies we can apply to curb overfitting. When we gain this understanding, we can go on to build effective and robust ML models.

What triggers overfitting

In Chapter 6, Improving the Model, we saw that by adding more neurons to our hidden layer, our model became too complex. This made our model not only capture the patterns in our data but also the noise in it, leading to overfitting. Another root cause of overfitting is working with insufficient data volume. If our data does not truly capture the full spectrum of variations our model will be faced with upon deployment, when we train our model on such a dataset, it becomes too specialized and fails to generalize when used in the real world...