Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications

Razzaque, PhD Mohammad Abdur

ISBN 10: 1789616131 / ISBN 13: 9781789616132

Edité par Packt Publishing, 2019

A propos de cet article

Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

N° de réf. du vendeur M01789616131-V

Détails bibliographiques

• Titre: Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications
• Éditeur: Packt Publishing
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 1789616131
• ISBN à 13 chiffres: 9781789616132
• Reliure: Couverture souple
• État de l'article: very good

A propos de ce titre

Synopsis

This book will provide you an overview of Deep Learning techniques to facilitate the analytics and learning in various IoT apps. We will take you through each process - from data collection, analysis, modeling, statistics, and monitoring. We will make IoT data speak with a set of popular frameworks, like TensorFlow, TensorFlow Lite, and Chainer.

À propos des auteurs

Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.