Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Human activity recognition using LSTM model


The Human Activity Recognition (HAR) database was built by taking measurements from 30 participants who performed activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify their activities into one of the six categories mentioned previously.

Dataset description

The experiments were carried out on a group of 30 volunteers within an age range of 19-48 years. Each person accomplished six activities (walking, walking upstairs, walking downstairs, sitting, standing, and laying) while wearing a Samsung Galaxy S II smartphone on their waist. Using an accelerometer and a gyroscope, the author captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50 Hz.

Only two sensors, an accelerometer, and gyroscope, were used. The sensor signals were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec with a 50...