Convolutional Neural Networks - Seminar

In this edition of the JVL blog, I want to present yet another small literature review, but this time on Convolutional Neural Networks (CNN) in the area of machine learning for image processing.

Similar to the previous post about Nanophotonics Interconnects, this write-up was also an obligatory part of my current masters program. This literature review on the topic of Convolutional Neural Networks gives an introductory knowledge about what neural networks are, how they are extended to be convolutional networks, and how they can be used for image processing and other applications.

Again, the requirements were similar to those of the document presented in the previous post: a brief 4 pages literature review about the topic. This serves as an extension to and further practice on understanding and writing scientific papers especially about a foreign topic. Writing a technical paper on a topic foreign to me was demanding as it required the extra effort to get acquainted with the fundamentals. Nonetheless, it was quite an interesting topic and a worthwhile task, and so I will like to share it here.

The Literature Review

The following document introduces fundamental concepts on and recollect my understanding of Convolutional Neural Networks. This is in NO WAY an officially published paper nor does it contain any new contributions from my part. It serves as a technical literature review and writing exercise.

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Lastly...

As was briefly mentioned, working on a topic that's different from one's own technical area of expertise does require more effort. I reckon CNN wasn't an easy topic to grasp at an instant, but once the fundamentals are there, the rest is basically just feeding to that curiosity. There are a plethora of resources out there, and there is much more being worked on as far as CNN is concerned (different applications, architecture, parameters, etc...).

If you are interested in the topic, I encourage you to look around for more resources and projects about CNN...Even try and play with some of the widely-known Machine Learning frameworks such as Caffe, Torch, Theano, mxNet, TensorFlow, DSSTNE...

As usual, leave a comment if you have one!