We are supposed to be working with PyTorch on this project. Getting into the headspace of programming in machine learning is a difficult task to accomplish. However, Neural Nets are given a strange degree of “deceptive complexity”. In reality, high level understanding of neural networks make them seem simple, by most standards. Mind, the calculus that makes up a Neural Network still requires a lot of background knowledge to understand. However, basically anyone with a limited understand of computer science can make a Neural Network with the tools that are currently provided.
Tensorflow, Keras and PyTorch are prime examples of this, with many of the most difficult to create features (back propogation and 2D kernel generation) being boiled down into one or two lines of code for the creator. This is the main reason why projects like these are even possible for students. Building a Densely Connected Neural Network from the ground up, even a very small one, takes a load of time and knowledge. This isn’t even entertaining the idea of training the models after the Network has been created. Instead, a student can simply reach a level of enough understanding to get at least a jist of how Neural Networks work and then let something like PyTorch do the rest of the work.
As for what has gone well and poorly with the project on this week, my teammates are very diligent when it comes to communication and work done already, which is always a good change of pace. Unfortunately, out client has not yet responded to us yet, which is quite worrying. Hopefully, it just a minor setback, as it is the weekend.
What could be done better in the course? I think more transparency on how the selection process works would help. I have heard that it was simply a random selection, which didn’t give me a large amount of confidence in the rest of the course. Of course, this is conjecture, as I don’t actually know whether any of that is true. What I enjoy about this coruse currently is the amount of leeway we get when it comes to oversight on the project. Most things are left to the client and the students, which is nice.
