Day 2 (7/9/19): Mathematics and Data Science Review

After a brief meeting with the other interns in the morning (and receiving our prizes from the team building exercise yesterday), I started working in the Machine Vision Lab to establish a solid foundation of the math and programming packages that will be essential for this internship. After finishing setting up my workplace and resolving a few operating system issues, we successfully installed all the necessary software, such as anaconda, Spyder, Numpy, and Pytorch, for me to begin experimenting and utilizing the learning resources.

I started off reading a few articles and completing an edX course titled "Essential Math for Machine Learning- Python Edition". These materials reviewed the fundamentals of linear algebra (vectors, matrices, tensors, and their operations), calculus (multivariate differentiation, integration, etc.), and statistics/probability (measures of central tendency and variance, confidence intervals, sampling distributions, and hypothesis testing).

After becoming confident in the math upon which deep learning is founded, I transitioned into a Microsoft course titled "Introduction to Python for Data Science". This course was a great review of python programming practices which i haven't used in awhile and an easy introduction to the packages useful in python as well. These include Numpy (numeric python), Matplotlib (for data visualization), and Pandas (for dataframes storing data of different type).

Finally, I finished the day with a few more articles on the python packages Numpy and Pytorch. These tutorials gave me more in-depth knowledge around the uses of the packages and allowed me to start experimenting on my own in the Spyder IDE.

Overall, my second day was very informative, and I learned/reviewed a lot of material which I believe will be very useful in the future.

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Day 29 (8/15/19): Final Day Before Presentations

Most of today was also spent practicing and editing my presentation to make it as professional as I can. I'm really looking forward to the opportunity to present my work to faculty and friends tomorrow. Here is a link to the slides for my final presentation: Novelty Detection in Streaming Learning using Neural Networks

Day 28 (7/14/19): Presentation Dry Run

In the morning, all of us interns got the chance to practice our presentations in front of each other in the auditorium. I was pretty happy with how mine went overall but the experience was definitely valuable in identifying typos or slight adjustments that should be made. Throughout the rest of the day, I tried to implement these changes and clean up a few plots that I want to include for Friday.

Day 9 (7/18/19): Incrementally Learning CUB200

Today I continued my work learning about incremental learning models by testing out different strategies on the CUB200 dataset. From what I understand from reading various articles, there seem to be five different approaches to mitigating catastrophic forgetting in lifelong learning models. These are regularization methods (adding constraints to a network's weights), ensemble methods (train multiple classifiers and combine them), rehearsal methods (mix old data with data from the current session), dual-memory methods (based off the human brain, includes a fast learner and a slow learner), and sparse-coding methods (reducing the interference with previously learned representations). All of these methods have their constraints and I don't believe it is yet clear what method (or what combination of different methods) is best. Full rehearsal obviously seems to be the most effective at making the model remember what it had previously learned but given that all training exam...

RIT CIS Summer Internship

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