https://mitpress.mit.edu/9780262046305/introduction-to-algorithms/
This one is pretty hardcore. I bought the 2nd edition of it over 20 years ago when I started my career as a developer due to not doing a CS degree.
Ha ha came here to suggest exactly this book!
It has it all, from bubble sort up to parallel algos and isn't it just a killer name, introduction to algorithms...
There are YT courses available to support the book. Or rather, the book exists to support the courses:
Don't mind the ages of these series - I watched them in full, and they're generally still relevant. I say generally because I'm not sure if I'll ever use a Tango Tree, but who knows!
PS: If you're not sure if you don't know the required Math, I created a graph of all MIT courses with YT videos here. The courses on the left are dependencies for those to the right.
If you want to improve your problem solving skills, I'd suggest solving actual problems. Data structures and algorithms can be very satisfying in their own right, but the real value is in taking a real-world problem and translating it into code.
It also depends what you want to do with your knowledge. There are domains that are deeply technical and require a lot of the things you've mentioned, but they also tend to be pretty hard to break into. A lot of software is not so deep. Any software project will have need for good domain modeling, architecture, and maintainability. Again, these are things best learned through practice.
I agree, and here's a few different avenues of examples:
If trying to get past interviews, Leet code and hacker rank can be great. They're not so great for real world problems, but not bad.
Advent of code is a good middle ground between theory and practice in my opinion.
To really learn real world problem solving, I'd recommend implement a specification, without looking at existing implementations. For example, make a basic regex engine (formal Regular Expressions not PCRE expressions), or try to implement the C Preprocessor, or the JS event loop.
The current version is in C++
But if you check the link above, the older C and Fortran editions are free to read. This is the gold standard for numerical algorithms and scientific computing, without getting into things like GPUs and such.
"Algorithms to live by" https://www.amazon.com/Algorithms-Live-Computer-Science-Decisions/dp/1627790365
Less technical than you probably want, but it is useful for mapping real world problems to known algorithms.
If you're not in any hurry, The Art of Computer Programming by Knuth.
Although technically that's several books, not a book.
There are plenty of good resources online. Here are some topics you probably wouldn't see in an intro algos course (which I've actually used in my career). And I highly recommend finding the motivation for each of these in application rather than just learning them abstractly.
Ignore the name (neural networks might as well be a footnote). A more appropriate title would be "generic problems and algorithms".
(Artificial Intelligence: A modern approach by Russell and Norvig)
Honestly I would take a look through a good standard library that provides a lot of algorithms (e.g. C++ or Rust). That has the basics, especially for data structures.
Also have a go at some hacker rank tests. Especially if you want to learn dynamic programming (abysmal name), they absolutely love that.
What have you built? What larger projects have you contributed to?
One of the biggest things I have built was a experimental 3D physics engine, it did require some memory allocation optimizations, I built a lot of stuff I cannot really list them all in here, An honorable mention would be an FPS game from scratch without a game engine.
That's more than most SWE graduates have done, which is great! But it makes it difficult to judge what you might benefit from based on what you've shared.
To answer the title question, the suggestions provided by others here are all good resources for studying algorithms, but no one mentioned Algorithms Illuminated which is of a similar quality. Choose one of these suggested resources that vibes with your learning style.
But don't discount the suggestions to work on new projects that are outside of your current experience and requires more than your current knowledge base to complete. Trying things you haven't tried before really is the only way to do things you couldn't do previously.
Good luck!
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