The way the camera moves and the speaker is acting makes it feel like someone is about to bust in and erase all his whiteboards at any moment 😂 Great video!
I think this is one of those videos that will really keep you up rather than making you want to go to sleep. It's not boring. And the explanations are so lit.
Simply the best explanation on the whole internet. I really like how he points out what he has done wrong in the past so we don't do the same. Thank you. :)
Table Of Contents: (I want to redo this video, the video is overexposed in lighting) Messing Around 0:00 - 0:17 Big O Introduction 0:17 - 0:43 O(n^2 )Bounding Example 0:43 - 1:33 Upper Bounding 1:33 - 1:51 The O(n) Mistake 1:51 - 2:38 Notating min() & max() 2:38 - 2:56 O(1) "Constant time" 3:07 - 3:46 O(log(n)) 3:46 - 5:43 O(n) "Linear time" 5:43 - 6:30 O(n * log(n)) 6:32 - 8:09 O(n^2) 8:09 - 9:09 O(2^n) "Exponential time" 9:09 - 9:40 O(n!) "n-factorial" 9:40 - 10:55 Considering Tradeoffs 11:01 - 11:34 Why To Optimize Time 11:34 - 11:58 Space Complexity 12:02 - 14:38 DO. NOT. GUESS. 14:44 - 15:40 Leveraging Our Complexities 15:40 - 16:30 Wrap Up 16:30 - 16:59 HUGE IDEA. Time complexity must be at least the space complexity. If you deduce a complexity and this does not happen then something is wrong. This is because to use space we must use time (space is tightly bound to the time that it takes to use it). Due to this relationship, space ALWAYS has at least a loose lower bound on time if not very close. I will make a part 2 to this video to expand with nuances like this in complexity theory.
This is the kind of video by which people like me, who are introducing to this world, will buy access to your platform, never stop making them, excellent explanation bro, I finally understood this concept.
THE BEST, CLEAREST BIG-O VIDEO BREAKDOWN YOU WILL FIND ON RU-vid!!! Thank you so much for making this, it has really helped me understand things better!
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Ben, you are the MAN. I wish I could attend a Bootcamp taught entirely by you. I go to Columbia and it's extremely difficult to keep up with the speed at which the curriculum is taught. Your videos really help create a baseline of understanding.
Great video❣️ Helped me alot I have watched many videos on big O notation but no1 has ever explained it in this easy and simple manner Great job sir👍❣️
Thanks for the tutorial - thumbs-up given. The most important comment you made was toward the end of the video: it is impressive to state the space-time complexity. For interviews, it always helps to create an air of authority, regardless of how practical it would be to write production code that relies on the what you are being tested, which in all likelihood you never will :)
To be honest, this is by far the best one! now please make a vid where you go through actual code, I am talking about actual code, not some rubbish nested for loops just to print the name or number. That will help in practically applying these concepts.
Studying the behavior and complexity of algorithm can be super confusing, especially considering mathematics is involved and not all developers are well schooled beyond basic algebra. This is a pretty good introduction in my opinion, to get your head around the basics.
@@SaiKumar-vo2ek I think it depends on your goal, if you want to get into competitive programming then C++ might be a good option. otherwise use a high-level language like python, ruby, or javascript where you don't have to worry about the lengthy syntax, and implement your own DS on top the existing one. NOTE: I'm not an expert so take with a grain of salt and do your own research.
So very proud of this video. I love it! I was just thinking today after about 6 years of higher education in the US (undergrad and grad), I've never had a black lecturer/instructor. Never! Not even outside of my CS classes. Thank you for this :)
Talking about time complexity, it's great to watch your video and do some further study on it because it's almost a basic skill during interviews. At least from my previous interview experience, both facebook and bloomberg interviewers were willing to know if I could accurately state time & space complexity. Highly recommend Cracking the Code Interview Chapter VI. Big O, where you would deeply understand the practical ways of solving this issue. The answer here clearly explained how we should calculate time complexity for the recursion calls: stackoverflow.com/questions/43298938/space-complexity-of-recursive-function
Sir, can we say O(log(n)) means "half of n or half of the common runtime" of the algorithm? So, if anyone asks that what is O(n*log(n)), we can say its O(n*(n*1/2)) which is O(1/2*n^2) and drop the constant which becomes O(n^2)?? Means O(n*log(n)) = O(n^2)???
Is the triangular work @ 8:45 a graph of number of comparisons (y) vs index (x) ? That's how you would get a triangle imo. Also from what i know about work (from physics) work is calculates as the area under the graph. Since it is a right angle triangle, we can calculate the area as 1/2 * base * height which equals n^2/2 which then gives us the complexity of n^2.
This is my question besides all this, plain English plain Math. int x = 3; right? O.K. what "operators" create more complexity? Do 2 operators on 1 line square the time? Each variable has a size in memory, int, char, float. so in PEMDAS notation per operator what creates complexity? I think I will make my own channel. thanks anyway for the try.
Oh gads, I want to watch this, this instructor seems very good but all the camera moving is making me feel ill. :( I hope you redo it sometime with the camera still on a base or something.
