CS373 Fall 2022: Bruno Fazzani
What did you do this past week?
I finished an NLP assignment that I thought was really interesting, as I’ve never worked with neural networks before, but got experience using PyTorch to train and test a network. I definitely recommend the class if NNs are something you are interested in. I also got to meet with my SWE project group and we did some brainstorming about our project proposal.
What’s in your way?
My group has not finalized our proposal yet, which I suppose is temporarily blocking our progress, but I’m not concerned about it. I’m nervous that it’ll be difficult to learn all of the new tools we need to complete the project, but I felt better after seeing all of the resources that the TAs have shared with us.
What will you do next week?
I have an exam on Monday, but after that I’m definitely going to be putting a lot of time into finishing Phase 1, especially since I’m visiting Boston next weekend, so I’ll try to get it finished before then.
What did you think of Paper #5: The Single Responsibility Principle?
Making sure to separate units that work on different tasks seems like it’s one of the concepts in OOP that has been drilled into me over and over again since taking my first CS class, and this paper seemed like a more formal and in depth explanation of that same concept.
What was your experience of reduce and methods vs. functions? (this question will vary, week to week)
I was using a functional programming language at my internship this last summer, so I got really used to thinking in terms of map/reduce for any data processing tasks, and while I knew Python had map, I actually wasn’t aware of reduce, so that was really cool to see. One of the things I really enjoy about Python is the flexibility with which you can iterate over and modify data structures. I think the way Python approaches methods and functions with the double underbars is an interesting and somewhat novel way to provide additional functionality, but the syntax sometimes seems somewhat strange to me.
What made you happy this week?
I started watching a tv show that I think is pretty interesting, so hopefully I’ll have some more time next week to keep watching it.
What’s your pick-of-the-week or tip-of-the-week?
kaggle.com has lots of tutorials, competitions, and datasets for machine learning, so if you’re interested in ML, NN, NLP, or any other data intensive tasks I highly recommend this resource.