Learning Algorithms(paedubucher.ch) |
Learning Algorithms(paedubucher.ch) |
CLRS isn't bad. It is meant for a reference, and it also assumes that you are taking the course in college with an instructor teaching you.
There are other great resources out there.
Two I recommend are:
- Algorithms, Coursera (Sedgewick, Wayne)
- The Algorithm Design Manual (Skiena)
I have recommended self-taught programmers in the past, which all of them liked:
- A Common Sense Guide to Data Structures and Algorithms, PragProg (Jay Wengrow)
- Algorithms Illumited, book or Coursera MOOC (Tim Rougharden)
My usual learn-a-new-language routine is speed-running the first 25ish Project Euler problems. They cover a lot of three usual language basics.
Going through them at 1.25-1.5x speed, a lecture per night or every few days, is extremely helpful.
The latter is great for algorithms because it groups everything by concepts, ie greedy, DP and then network flows.
I've tried CLRS, but oh boy it's a) a slog to go through, b) very sparse with explanations. You might say that the whole book is one explanation after another, and you'd be technically right, but there's nothing more frustrating than an overly complex explanation that might be absolutely precise yet complex to understand. I also often feel like the language is needlessly complex.
On top of that, for example, the mathematical explanations often skip steps that are obvious to authors. You might think it's reasonable, but to a struggling newcomer, this is a huge drag.
Unrelated to algorithms (but not totally), I am now studying Discrete Mathematics for Comp Sci [0] since I felt like my math knowledge (or lack thereof) is holding me back in reasoning about algorithms. Even those courses have a fair share of skipping obvious steps in mathematic explanations, but it's doable so far (especially with tools like ChatGPT and Wolfram Alpha to check my understanding).
I'm just trying to be a better engineer, and I'm having a really hard time trying to find a suitable resource to learn from.
[0] https://www.coursera.org/specializations/discrete-mathematic...
My data structures course mainly was graded from Zybooks assignments. I think it's actually a pretty good and understandable resource. It was this: https://www.zybooks.com/catalog/data-structures-essentials/
Anyway, maybe after you study discrete math from Coursera, that Zybook could help. It has some basics about big-O notation and determining the big-O of an algorithm (and calculating the number of operations it requires), but after that, it's all explanations of data structures accompanied by many animations and pictures. It may be tedious for someone who already knows how e.g. red black trees works, but it helped me understand them, as well as unbalanced BSTs, AVL trees, B-trees, heaps, and graphs. The stacks and queues were tedious for me, because I already know them.
I do think having taken discrete math helped me; I still flipped through CRLS during the course on the side, and their explanations were less impenetrable because I know the basics of mathematical proofs now.
There's also a book on data structures that went over hash functions (it's out of print, but I found a PDF online). It was also helpful for me. I'll reply to this comment with it when I get home.
Your time would be better spent on accumulating domain knowledge in whatever industry you are working in - finance, healthcare, government, etc. Especially if you want career advancement or make yourself look more attractive to prospective employers. Unless you are directly involved in writing algorithms or academic research, you really won't be proving time and space complexity of algorithms in your job. A general understanding is sufficient for 99% of the jobs out there.
"be balanced". (Do not disregard the other sides, work on all dimensions.)
Perhaps there is a book for algorithms that does it the functional way? Probably a lot of it comes down to naive versions of algorithms using functional data structures and then some changes in the algorithms due to optimizing for functional data structures?
Would really love to know to see different and various use cases.
That's why I wrote: 'Unless you are directly involved in writing algorithms or academic research, you really won't be proving time and space complexity of algorithms in your job. A general understanding is sufficient for 99% of the jobs out there.' If you are developing a kernel, then you should have a solid understanding of algorithms, data structures, etc. But 99% of programmers aren't developing kernels. 99% of programmers are working in industry - finance, healthcare, government, etc.