A detailed analysis of Deep Learning for Internet of Things (IoT) Applications
Internet of things (IoT) is changing the world around us, as IoT-enabled devices are creating wonders day by day. Be it healthcare, industrial manufacturing, or smart home solutions, IoT devices are making a positive impact and a lot of businesses are quickly adopting these devices to surmount challenges.
IoT, which was found in the early 1980s, was first experimented on a Coca-Cola machine. The founders connected their computers with the machine to confirm the machine was stocked before they went on a trek. It was the world’s first internet-connected device.
The key to developing a “smart” IoT application lies in intelligent processing of and analysis of the gathered data generated by the sensing or the embedding components. These devices usually run on simple computer chips which do not have an operating system. So the pattern recognition tasks like deep learning are difficult to run locally on IoT devices.
This paper provides an overview of Deep Learning methodologies and the potential of emerging deep learning techniques in the internet of things data analytics. It also gives references to different types of data generated by IoT applications and how specific deep learning models provide better results for specific applications.
Major components of this whitepaper:-
- Characteristics of IoT data and its analytic requirements.
- The available computing frameworks for processing data.
- The various frameworks, which are available for DNN inference and training.
- The primary services of IoT, which use deep learning as their intelligence engine.
- How does an IoT application generate different data?
PathPartner, with over a decade of relevant experience, provides IoT services across the entire IoT lifecycle. This document contains details regarding product information, standards, and technical specifications.
To know more download this whitepaper.
