The evolution of Deep Learning Models

Scope and approach

This paper is a part of a series covering Deep Learning applications for Smart cities/IoT with an emphasis on Security (human activity detection, surveillance etc). It also relates to my teaching at Oxford and UPM (Madrid) on Data Science and Internet of Things. The content is also a part of a personalized Data Science course I teach (online and offline) Personalized Data Science for Internet of Things course. I am also looking for academic collaborators to jointly publish similar work. If you want to be a part of the personalized Data Science course or collaborate academically, please contact me at ajit.jaokar at futuretext.com or connect with me on Linkedin Ajit Jaokar

No taxonomy of Deep learning models exists. And I do not attempt to create one here either. Instead, I explore the evolution of Deep learning models by loosely classifying them into Classical Deep learning models and Emerging Deep Learning models. This is not an exact classification. Also, we embark on this exercise keeping our goal in mind i.e. the application of Deep learning models to Smart cities from the perspective of Security (Safety, Surveillance). From the standpoint of Deep learning models, we are interested in ‘Human activity recognition’ and its evolution. This will be explored in subsequent papers.

In this paper, we list the evolution of Deep Learning models and recent innovations. Deep Learning is a fast moving topic and we see innovation in many areas such as Time series, hardware innovations, RNNs etc. Where possible, I have included links to excellent materials / papers which can be used to explore further. Any comments and feedback welcome and I am happy to cross reference you if you can add to specific areas.  Finally, I would like to thanks Lee Omar, Xi Sizhe and Ben Blackmore all of Red Ninja Labs for their feedback

Rest of the paper can be downloaded here Evolution of Deep Learning models