Highlights
Learning Outcome:
Critique and contextualise emerging research in the area of computational intelligence.
Task
Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Deep learning architectures such as deep neural networks and recurrent networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, medical image analysis where they have produced results comparable to and in some cases superior to human experts. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The deeper learning algorithms learn, the better they perform. For example, Facebook has developed a strange ability to recognize one’s friends in their photographs. In the old days, Facebook used to make user to tag the friends in photos by clicking on them and typing in their name. Now as soon as the user uploads a photo, Facebook tags everyone for the user. Modern Facebook uses deep learning for face recognition. Deep learning is revolutionizing the face recognition field since last few years.
Please choose at least 3 reputable research papers and write a research report about the use of different Deep learning architectures in face recognition and your view on the success of using different architectures.
Your report should at least cover the following aspects.
• Introduction to the concept of deep leaning
• A brief explanation of different deep learning techniques used in face recognition domain
• Summary of how Deep learning architectures have been used in your chosen research papers
• Conclusion on the success of using deep learning techniques in facial recognition in your own words
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