Applications of IOT using Deep Learning

Abstract

Deep learning, a branch of machine learning and a branch of artificial intelligence, focuses on simulating the human brain in settings involving data collecting and processing. Because the neural networks used by deep learning to learn have many deep layers, the term "deep learning" has been coined. It employs a programmable neural network, which enables machines to decide correctly without the assistance of humans. The network of physical objects (things) that are integrated with sensors, software, and other technologies for the purpose of communicating and exchanging data with other devices and systems over the internet is described by the term "Internet of Things" (IoT). IoT devices are pieces of hardware, such as sensors, actuators, gadgets, appliances, or machines, that may communicate data over the internet or over other networks and are designed for particular uses. Artificial intelligence includes deep learning, whereas IoT refers to internet-connected technologies that link and exchange data with other systems and devices. IoT is rapidly expanding in the fields of science and engineering. On IOT devices, deep learning applications usually have strict real-time requirements. In order to support a new realm of interactions between people and their physical surroundings, this article offers a survey of deploying deep neural networks to Internet of Things (IoT) devices.