Temporary pause in licensing from Germany not 'shut down'. Germany has no choice but to buy Russian gas where else are they going to get energy from to run their huge GDP country
Became too partisan/political
People forget this is not twitter
Anyway Ukraine will be broke for a long time after this, there's no jobs during a war except fighting we can help there is tons of developers there who will be jobless probably tomorrow
Nobody cared when Russia put military there last year. Then after Afghanistan disaster in the summer he doubled the amount of military, and still nothing happened in response. It's a highly corrupt country where opposition is in prison, and various foreign leaders launder money there. There is no appetite for investing in Ukraine, meaning multi-billions into real air defences and training military to use it instead we gave some pocket change and wished them the best while using their banks and oil companies as personal ATM.
Research is done because you are compelled to do so, it chooses you, usually something you never thought you'd be good at or interested in, you just can't stop, for me anyway.
This workshop https://learnaifromscratch.github.io/algorithms.html is designed for passing job interviews, doing competitive programming and ucsd's design & analysis course. Do you have 3 months? You can do this.
You are an english teacher so I assume you are familiar with grammar and possibly Latin syntax, you're good to go esp when you learn PL theory
Wildberger's channel for me has the best 'explainers', he reinterprets math and in the process you end up learning about both the standard definition and a different model he comes up with for example in his FMP playlist is a totally wild reinterpretation of the complex numbers using Dihedron algebra https://www.youtube.com/playlist?list=PLIljB45xT85Bfc-S4WHvT...
I always like seeing standard math curriculum cast to some other interpretation for example E.T. Jaynes book 'Probability Theory the Logic of Science' the first few chapters he builds up from scratch probability theory deriving it entirely from quantitative logic using a running example of a robot being programmed using sampling theory and hypothesis testing, parameter estimation and performing random experiments. No Venn diagrams, the Bernoulli urn rule comes popping out as a logical consequence, the central limit theorem reveals itself as a special case when you discover a phenomenon over the chapters where all other distributions seem to gravitate towards a gaussian/normal distribution which he proposes renaming the central distribution then there's even an entire chapter on where the names for these distributions come from and how they are misleading. No measure theory either, you expand a continuous function to a finite orthogonal function, you assign probabilities in a finite dimensional space, do the probability calculation then pass to the limit at the end where you end up using the Lebesgue integral in what he claims is 'it's original meaning'.
Anyone who knows of more books or channels that do this I'd be interested
I self study machine learning here https://learnaifromscratch.github.io/ai.html it's an early and shitty draft and proof of concept that you can do self-directed learning for these topics while looking up the background you need to know, which for me is much more interesting than taking a generalized math curriculum of absolutely everything. The courses so far we haven't escaped the content of Wasserman's 'All of Statistics' book yet on classification or probabilistic graphs, so you could if you wanted watch the lectures and only do Wasserman's book.
If you want to try the OCW linear route of taking everything for whatever reasons, you will have to get up to MIT student levels trying to unravel the algebra done in the early calculus courses and later where they just assume you possess this background. One way to do that is those problem solving books like this one which 'bridges the gap between highschool math and university' https://bookstore.ams.org/mcl-25 at least you then get worked out solutions. Another way is Poh-Shen Loh's Discrete Math course he opened up on YouTube which is done the same way he holds the CMU Putnam seminar, working through a bunch of combinatorics and algebra will more than prepare you to understand those continuous math OCW courses https://youtu.be/0K540qqyJJU
Like everybody else there is of course the issue of: who is going to check my work. For me I went with the time tested tradition of hiring a tutor, a local grad student and paid them once a week to go on chat/zoom or meet at a coffee shop before the pandemic and spend a few minutes going over everything I'm doing wrong. In the early days however I used constructive logic ie: 'proof theory', to audit my own work: https://symbolaris.com/course/constlog-schedule.html and read a huge amount of Per-Martin Lof papers on the justifications of logical operators like implies, disjunction, conjunction, etc. Of all the math I've ever taken I would say that proof theory was the most useful for somebody by themselves who isn't sure of what they are doing (I'm still not 100% sure.. hence why I hire people now).
If you want a great Calculus text that explains those nasty looking Euler's e nested statistics distributions try Mathematical Modeling and Applied Calculus by Joel Kilty everything from partial derivatives, gradients, x^n, e^x, trig, integrals, limits is explained in terms of parameters to modeling functions, if you write software it will be easy to understand. I haven't posted it yet but I tried going through Allan Gut's probability book using only that math modeling calc text and have not run into anything applied, as in concepts about limits or integrals, that wasn't already covered. Of course the concepts are much more abstract measuring a bunch of intervals and a different method of integration and I don't pretend I'll be making any advances in this area beyond applied usage but it can be done, jump in and pick up the background as you go as opposed to doing all the background at once, losing interest and giving up.