The Lounge is rated Safe For Work. If you're about to post something inappropriate for a shared office environment, then don't post it. No ads, no abuse, and no programming questions. Trolling, (political, climate, religious or whatever) will result in your account being removed.
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.