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But that is way too mathy for me. I like math but I like it to be applied. Anyways, I wanted to really see Big O explained and tied to specific algorithms. I wanted to start out relative simply but not treat me like I'm totally ignorant. I stumbled upon this book from the Pragmatic Programmer publishers...
I devoured the first 4 chapters and it is amazing. Definitely check this one out. I'm so excited about it. I feel like I'm really putting the concepts together. This one is going into the Top 5 All-time Best books on programming. Seriously up there with Petzold Windows programming.
After reading those first 4 chapters I actually understand what O(1), O(N), O(log N) and O(N^2) mean. I even understand them in relation to algorithms. The author connects them to the algos and shows graphs of time / efficiency and it is absolutely clear and interesting.
(I'm sure it's like this many places) Algorithms was one of my Campus' Crush-the-soul-of-the-CS-student classes, second to x86 assembly. The Professor was fantastic though, and was passionate about the topic.
Now you've got me wanting to dust of my Alg book... and maybe read it within a salt circle.
If you really want to learn just post some articles or questions and show your code and there will be plenty of people pointing out what you did wrong. I mean pointing out how you can write better code.
Social Media - A platform that makes it easier for the crazies to find each other.
Everyone is born right handed. Only the strongest overcome it.
Fight for left-handed rights and hand equality.
Self-taught myself, I learned a lot from reading Joel Spolsky and Raymond Chen. They don't care all too much about theory of algorithms, but highlight the essentially same topics from a practical, pragmatical perspective which I personally find way easier to learn.
I found The Algorithm Design manual to be a really useful survey of all sorts of algorithms and 'war stories' about how they apply to real-life situations. The intent is to equip you, the programmer-in-the-field, to think about your high-n data problems in terms of complexity and adapt and apply algorithms to them. (As opposed to, say, solving abstract puzzles by inventing and implementing algorithms).
Be sure to follow the links through to the 'Algorithm Repository' and even what seems to be the complete videos and slides of Prof Skiena's introductory algorithms class if you're interested in that rabbit hole. But the lovely hardback book will never be out of reach for ideas and inspiration.
I started off programming self taught, then went to college to study computer science and continued self-learning. It seems that computer science has become the embodiment of Super Tramp's "The Logical" song. Programming is supposed to be creative and artistic... Yet they want to teach you to do things a certain way, document your code, etc. "Zen and the Art of Programming" had a passage where the pupils asks the Master when he will know he has become a Master... The Master answered you will know when you no longer need to ask that question.
Learn techniques but don't let the crush your soul...
This may be a bit dated as I got my Computer Science degree way back in 1983, but as far as I am concerned, Knuth's "The Art of Computer Programming" series of books are Computer Science's bible.
Those books are amazing tomes.
I have seen those since I started back in '93 or so.
Each year or two I pick up Volume 1 Fundamental Algorithms and read the first half page and get maybe two pages in and then I give up again.
Maybe after I complete this other book I'll go back and see if I can get thru 4 or 5 pages now.
I hold an academic M.Comp.Sci. degree, yet I have no problem recognizing your situation.
I've got another "Holy Scriptures" book: The Anatomy of Lisp. Procuced with an early pre-release version (1978) of the first TeX/Metafont typesetting system. Maybe the text content is good, but I have tried again and again to focus on thoose Metafont characters; it is impossible. I cannot keep my eyes fixed on the text for as much as a single line. I have made so many attempts, over many years, to read this book that now I have given up. Also, I have given up Lisp. Maybe, if the book had been reprinted in a readable typeface, I would have been a Lisp fan now (I did a little Lisp in the 1990s), but it was just too hard to learn its anatomy.