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

Introduction to TensorFlow

Before the era of TensorFlow, the landscape of deep learning was markedly different. Data professionals had fewer comprehensive tools to aid in the development, training, and deployment of neural networks. This posed challenges in experimenting with various architectures and tuning model settings to solve complex tasks, as data experts often had to construct their models from scratch. The process was time-consuming, with some experts spending days or even weeks developing effective models. Another bottleneck was the difficulty in deploying trained models, which made the practical application of neural networks challenging during those early days.

But today, everything has changed; with TensorFlow, you can do lots of amazing things. In this chapter, we will begin by examining the TensorFlow ecosystem and discussing, at a high level, the various components relevant to building state-of-the-art applications with TensorFlow. We will begin our journey by setting...