khannate

I'm a little confused about what the business model here is. It sounds like they are selling labeled data to companies, and doing this by "label[ing] most of the objects automatically" and then having humans review these labels. So does this mean they are using some unsupervised method to label data, and then selling that to people who want to train supervised models? Why aren't they instead just beating out the people they sell to by solving the same problems without labeled data?

I'm surprised that no one in this thread has mentioned TikZ yet. I've always found it easy to use and sufficient for my needs.

UChicago's honors introductory sequence uses it: http://cmsc-16100.cs.uchicago.edu/

Based on my experiences using this as a reference while taking a class on functional programming, I think that while many of the explanations and examples are helpful, the ordering is a bit weird. For example, pushing off the explanation of higher-order functions for so long seems questionable, since they're a fundamental feature of the language.

I am actually planning on doing a PhD right after my undergrad. My issue is with what to do before then.

Nothing. What does prevent me is the timeline on which I'm applying and other things I have to do in that same stretch. I'm hoping to do exactly this sometime in the next two or three years, but I don't think it'll be an option for this application cycle.

Sorry for the confusion - it's not that I'm learning machine learning without programming, but rather that most of the programming I know I learned in the context of machine learning. In particular, I'm only familiar with the small portion of the standard undergrad cs curriculum that's relevant to those things.