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By providing many solutions, DL finds its vast applications in changing the world. This section will discuss the applications of Deep Learning at the edge [33].

1 i) Computer vision - In computer vision, DL helps in image classification and object detection. These are computer vision tasks required in many fields e.g., video surveillance, object counting, and vehicle detection. Amazon uses DL in Edge for image detection in DeepLens. To reduce latency, image detection is performed locally. Important images of interest are uploaded to the cloud, which further saves bandwidth [33].

2 ii) Natural Language Processing - Speech synthesis, Named entity recognition, Machine translation are a few natural language processing fields where DL utilizes Edge. Alexa from Amazon and Siri from Apple are famous examples of voice assistants [33].

3 iii) Network Functions - Wireless scheduling and Intrusion detection, Network caching are common fields of an edge in network functions [33].

4 iv) Internet of Things - IoT finds its applications in many areas. In every field, analysis is required for communication between IoT devices, the cloud and the user and vice versa. Edge computing is the latest solution for implementing IoT and DL. From much research, DL algorithms are proven to be successful. Examples of IoT using edge include Human activity recognition, Health care monitoring, and Vehicle system [33].

5 v) AR and VR - Augmented Reality and Virtual Reality are the two models where edge provides applications with low latency and bandwidth. DL is considered to be the only pipeline of AR/VR. Object detection is an application of AR/VR [33].