RIT CIS Internship: Machine Learning

After coming in late due to a soccer practice I spent the morning updating my presentation based on changes that were previously suggested. After that later in the day I did a test presentation in front of kLab. I had some minor changes to make after that but the presentation is feeling much more polished. More importantly I feel confident to do the final presentation tomorrow.

I spent the morning going through my presentation and fixing any errors I saw. After I was satisfied with my presentation I practiced it in front of Tyler and Kushal. Later in the day all of the interns presented their projects. Hudson and Emerald presented on their document restoration shoeing some impressive results on some very old documents. Anoop and Emma presented on how their hockey analytics system worked. It showed some very promising results and their method of determine where a player was on the ice using only 4 reference points was very interesting. Tommorow I plan to add the finishing touches to my presentation.

I missed both of these days due to soccer starting up but I worked on my presentation from home.

While I was waiting for my experiments to run on the server I spent the day presenting in front of various members of kLab. I got some very good advice from Dr. Kanan in the morning which I used to update my presentation. Later in the day with the help of  Tyler, Dr. Kemker and Kushal I was able to get a presentation that I was happy with. I plan to update the presentation with the results from the experiments I ran.

I spent this morning working on my presentation. I tried to make my presentation more polished and explain computer vision in a concise way. After that I began making my lifelong programs ready to run on the server. When I tried to run the programs on the imac I am using it crashed so I needed something with more memory. After working with Tyler to convert my numpy code to Pytorch it was ready to run on the server. Pytorch is a library that runs large array calculations much quicker than numpy. I hope to extract data from my trials in order to evaluate their effectiveness.

I spent the majority of my day today working on my final presentation. I addressed some problems with my outline mainly the way I was over complicating things. Some of the topics of my presentation are difficult to explain but not as difficult to use. One example of this is neural networks. I was trying to go to in depth on how a neural network functions but all I really had to describe was the general use of them. I also added lots of visuals to my presentation in order to make it more interesting. I also made some minor changes to my code in an attempt to optimize it but its overall function stayed the same.