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

Understanding and applying learning rate schedulers

In Chapter 12, Introduction to Time Series, Sequences, and Predictions. we built a DNN that achieved a mean absolute error (MAE) of 4.5. While this result was much better than our basic statistical methods, our next line of thought was how we could improve the performance of our DNN. One way of doing this is by finding the optimal learning rate. In Chapter 7, Image Classification with Convolutional Neural Networks, we discussed the important role of the learning rate in our modeling process as it controls the optimization process. Manually updating the learning rate can be a laborious process as the challenge lies in pinpointing what value works best. To have better control over the learning process, we apply a learning rate scheduler that adapts the learning rates based on defined criteria such as the number of epochs. With the aid of a LearningRateScheduler callback from TensorFlow, we can dynamically adjust the learning rate during...