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

Time series analysis – characteristics, applications, and forecasting techniques

We know time series data is defined by the ordering of data points in a sequence over time. Imagine we are forecasting energy consumption patterns in London. Over the years, there has been a growing increase in energy consumption, perhaps due to urbanization – this signifies a positive upward trend. During winter each year, we expect energy consumption to rise as more people will need to heat up their homes and offices to stay warm. This seasonal change in the weather also accounts for seasonality in energy utilization. Again, we could also witness an unusual surge in energy consumption due to a major sporting event, leading to a large influx of guests during the period. This causes noise in the data as such events are one-offs or occur at irregular intervals.

In the following sections, let us explore the characteristics of time series, types, applications, and techniques for modeling...