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 Time Series, Sequences, and Predictions

Time series cut across various industries, sectors, and aspects of our lives. Finance, healthcare, social sciences, physics – you name it, time series data is there. It’s in sensors monitoring our environment, social media platforms tracking our digital footprint, online transactions recording our financial behavior, and many more avenues. This sequential data represents dynamic processes that evolve over time, and as we increasingly digitize our planet, the volume, and thereby the importance, of this data type is set to grow exponentially.

Time series follow a chronological order, capturing events as they occur in time. This temporal nature of time series bestows a unique quality that differentiates it from cross-sectional data. When we turn on the searchlight on time series data, we can observe attributes such as trends, seasonality, noise, cyclicity, and autocorrelations. These unique characteristics endow...