Category: Malcolm’s Status Report

This week was not a heavily productive week, as I had many assignments due, an interview and it was Thanksgiving. However I was still able to get work done on the Amazon database. I made progress on moving more of our testing and training data to the cloud as well as working on developing architectures for the neural network. We need to start fully implementing it soon, so will meet as a group and discuss the details of this.

This week i have been playing a lot of catch-up with my part of the data management of the project. One of our big challenges is efficient learning of the neural network, so having the data in a place to do this is of major importance. As such, we are using AWS to host both this and the network. So with this in mind I have made progress towards building up the repositories and systems we need in order to host both of these services. There are still parts of it I am unclear on and need to figure out. Otherwise I need to take time to keep understanding possible architectures for our neural network, as this is still a large part of the project that needs to be implemented. I believe our plan is to work together over the coming weeks to put our individual parts of the project together and then use them to configure and finish designing the network, as well as completely train it.

This week I spent time working on our report document, learning about AWS services and how we can potentially host a website through there rather than building a Django web-app, and continuing work on finding and using the publicly available datasets. One of the pieces of feedback we picked up on during the design reviews was using AWS to host a website rather that writing a web-app from scratch. Since our project focuses most heavily on the machine learning and signal processing portions of our solution, we believe it is still within scope to use AWS to host a webpage. And since we are using their services to train the neural network, it will be much easier to pipe our results to the website if we use AWS. Otherwise, the report document has been focused on formalizing many parts of our design as well as incorporating feedback from our design review.

This week I spent time on an initial edge detection schema and managing the public datasets that we will be leveraging. This edge detection is fairly basic; it essentially uses Canny edge detection. The big issue with our edge detection is that we have no formal way of testing the efficiency of the edge detection. We would need to observe changes in the neural net in order to better tune the edge detection, so our approach to it is best-effort. Otherwise, I have made progress in identifying and creating frameworks for standardizing the data we need. Since our neural net is learning from three different data sets, it’s important to standardize them as much as possible. My goal for the coming week is to have the data standardized and to start experimenting with initial architectures for the neural net.