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 concept of time series, examined the core characteristics and types of time series, and looked at some well-known applications of time series in machine learning. We also covered concepts such as trailing and centered windows and examined how to prepare time series for modeling with neural networks with the aid of utilities from TensorFlow. In our case study, we applied both statistical and deep learning techniques in order to build a sales forecasting model for a fictional company.

In the next chapter, we will extend our modeling using more complex architectures such as RNNs, CNNs, and CNN-LSTM architecture in forecasting time series data. Also, we will explore concepts such as learning rate scheduler and Lambda layers. To conclude the final chapter of this book, we will build a forecasting model for Apple’s closing stock price.