Programming

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.

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.

https://numerical.recipes/

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.

If you're not in any hurry, The Art of Computer Programming by Knuth.

Although technically that's several books, not a book.

"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.

Introduction to algorithms or The Art of Computer Programming.

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.

• bloom filter
• btree
• b+ tree
• consensus algos (PAXOS, RAFT, VSR, etc)
• error correction codes (Hamming, Reed Solomon, etc)
• garbage collection (mark+sweep, generational, etc)
• generational arena allocator
• lease (i.e. distributed lock)
• log-structured merge trees
• min-cost + max-flow
• request caching and coalescing
• reservoir sampling
• spatial partition (BVH, kd-tree, etc)
• trie
• write-ahead log

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)

https://github.com/Youquan1/Artificial-intelligence-books/blob/master/Artificial%20Intelligence%20A%20Modern%20Approach%20-%20Stuart%20J.%20Russell%20%2C%20Peter%20Norvig.pdf

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?