Well, no one can understand it in a 6 min. video. It doesn't even show the formal definition of Big-O neither how to prove its theorems and properties.
Idk man there's something about your presentation and the colors you use that grabs my attention and now I'm actually understanding these concepts. Thanks Bro!
I made a summary for this lesson in the same way that Bro uses and I would like to share it with you, bros public class BigONotation {
/** * Big O Notation (how code slows as data grows): * it describes the performance of an algorithm as the amount of data * increases. * * it is machine independent but we are focusing on the "number of steps" to * complete an algorithm. * * examples of Big O notations: * O(1) * O(n) (n = amount of data) * O(log n) * O(n^2) * ... */
/** * concrete example: * addUp1() method will add up to a certain number (n). * * ex: * if n = 3 -> sum = 0 + 1 + 2 + 3 -> sum = 6. * here, the number of steps is 4 because we have one operation * (sum + i) repeated 4 times (n
Just as I'm getting introduced to this topic on the third semester of my Software Engineering degree in a course called Algorithms & Data Structures, I get recommended this video! Thanks, Bro Code!
Man, really thank you!!! I'm just learning for my Data Structures and Algorithms exam next week on my Uni, and Big-O was one of a few things, that I couldn't fully understand. Thanks to you now I understand it clearly
Great video Bro, just a quick note. When you said (at minute 5:15) that an algorithm that has O(n**2) can be faster than a O(n) algorithm if n is very small showing the graph, the part in which n**2 < n is only when 0 < n < 1 and since we're talking about data size then n is a positive integer and it is at least n = 1. The only way an O(n**2) algorithm can be faster than an O(n) algorithm is if there are hidden constants that have been omitted (since time complexity is asymptotic), an O(n) algorithm might actually be O(c1*n +c2) where c1 and c2 are constants. And depending on how large are these constants then you can find n > 1 such that c1*n + c2 > n**2 and therefore n > 1 such that the O(n**2) is faster than the O(c1*n +c2) ~ O(n) algorithm.
This was a GREAT explanation! I struggle with this as I learned to code in a bootcamp that did not study it and I don't have a CS degree. Thanks so much for the examples, helped a bunch! I will be rewatching :)
For anyone wondering, O(√n) is between O(n) and O(log n). It also has a cousin O(√(n)/2) which is literally 2 times smaller even in the worst case scenario, it's important to read non simplified O notation when calculating total time (not general complexity) for your specific algorithm.
The things I've always adored about computer science is how I struggle understand a topic until something snaps in my head, and it all becomes child's play.
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
I jus wanna let u know that I'm highly addicted to your channel (after java beginner playlist)and I badly want u to complete DSA asap before facing placements Keep up the good work broman 😂
Man, thank you. I watched about 2 hours of my teacher talking about it, and in the end, I didn't even know how to tell the big O of my own algorithm, now, 10 minutes later, I understood it with a 6 min yt video
Yoooo, My favorite comp sci. channel is back at it again Have you looked into rust at all? I’ve just started diving into the documentation, and I gotta say, it’s so much better than anything else I’ve used previously
bro i was a hater for learning bigO notation before watching your video. 😡 cause i cant understand that much.😬 you made me understand this bro. 😘 have you uploaded the "travelling salesman problem" video?🤨
So we can also say that the shorter the data, the faster the supposed slow big O notations? Like 0(n^2) is faster than 0(log n). I'm no cs student so I'm bad at math 😞
Hold up, how are the two functions at the start the same??? first one adds up n, the second one performs exponentiation and divides it by 2? Two different results.
im learning this thing but i have no idea about anything in CS my major doesnt have CS - what should i study before this so i get a basic understanding?
Those 6 minutes were more useful than 6 months of lectures. Thanks
Good thing our professor needed 5 hours to explain that graph...
College is a scam but unfortunately we gotta do it lmfao
Mine explained it in 5 minutes so no one understood it (lol)
At least he came to a conclusion at the end
Cold, crushing grip of academia got you too?
Well, no one can understand it in a 6 min. video.
It doesn't even show the formal definition of Big-O neither how to prove its theorems and properties.
Here too, lol. I didn't understand a single thing, and nobody else did either @@noamrtd-g4f
It's preposterous that you can make everything this simple and smoothly learnable. Thx a lot for real
Idk man there's something about your presentation and the colors you use that grabs my attention and now I'm actually understanding these concepts. Thanks Bro!
I made a summary for this lesson in the same way that Bro uses and I would like to share it with you, bros
public class BigONotation {
/**
* Big O Notation (how code slows as data grows):
* it describes the performance of an algorithm as the amount of data
* increases.
*
* it is machine independent but we are focusing on the "number of steps" to
* complete an algorithm.
*
* examples of Big O notations:
* O(1)
* O(n) (n = amount of data)
* O(log n)
* O(n^2)
* ...
*/
/**
* concrete example:
* addUp1() method will add up to a certain number (n).
*
* ex:
* if n = 3 -> sum = 0 + 1 + 2 + 3 -> sum = 6.
* here, the number of steps is 4 because we have one operation
* (sum + i) repeated 4 times (n
Thank you bro !!!!
Thank you so much Bro
Thank you bro! I am in love with you for this
such a goat fr bro
Tried it, addUp1 is faster compare to addUp2.
addUp2 is only fast if there are more numbers/steps whilst
addUp1 is fast if it is less numbers/steps
Please keep making more videos about this it helps for interviews thanks bro
Just as I'm getting introduced to this topic on the third semester of my Software Engineering degree in a course called Algorithms & Data Structures, I get recommended this video! Thanks, Bro Code!
The guy needs to be seriously appreciated!
Man, really thank you!!! I'm just learning for my Data Structures and Algorithms exam next week on my Uni, and Big-O was one of a few things, that I couldn't fully understand. Thanks to you now I understand it clearly
Great video Bro, just a quick note. When you said (at minute 5:15) that an algorithm that has O(n**2) can be faster than a O(n) algorithm if n is very small showing the graph, the part in which n**2 < n is only when 0 < n < 1 and since we're talking about data size then n is a positive integer and it is at least n = 1. The only way an O(n**2) algorithm can be faster than an O(n) algorithm is if there are hidden constants that have been omitted (since time complexity is asymptotic), an O(n) algorithm might actually be O(c1*n +c2) where c1 and c2 are constants. And depending on how large are these constants then you can find n > 1 such that c1*n + c2 > n**2 and therefore n > 1 such that the O(n**2) is faster than the O(c1*n +c2) ~ O(n) algorithm.
I just discovered this channel and goes through the python course I must say...... U deserve 🙏🙏🙏🙏🙏
"Prays" lmao
Dumbo that emoji is also used to express gratitude. @@wrathofainz
This was a GREAT explanation! I struggle with this as I learned to code in a bootcamp that did not study it and I don't have a CS degree. Thanks so much for the examples, helped a bunch! I will be rewatching :)
I kinda somewhat get Big O notation now on a high level. that graph helped so much. Google in 3 years here I come!
hope u got the position u wanted now!
For anyone wondering, O(√n) is between O(n) and O(log n). It also has a cousin O(√(n)/2) which is literally 2 times smaller even in the worst case scenario, it's important to read non simplified O notation when calculating total time (not general complexity) for your specific algorithm.
Isnt O(sqrtn) polynomial time? O(n^x) since its O(n^(1/2))
The things I've always adored about computer science is how I struggle understand a topic until something snaps in my head, and it all becomes child's play.
Wow. Thanks for helping me understand Big O here than the 3 weeks we spent on in class lol
Universities are about to go bye-bye
That was the best explanation of the topic i found on the whole internet. Thank you very much
Thank you so much bro code, I'm watching your channel,it will grow bigger then your expected
The easiness of this man's explanation is incredible
That was just an amazing video. Keep up the hardwork and effort you put into your videos.
Honestly, the best explanation of Big O, thanks you!
I'm really grateful for this channel, it save me in a lot of time.
Thank you! Great code examples to demonstrate the "steps" it takes. :D
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
I jus wanna let u know that I'm highly addicted to your channel (after java beginner playlist)and I badly want u to complete DSA asap before facing placements
Keep up the good work broman 😂
You are amazing, Bro!!
Man, thank you. I watched about 2 hours of my teacher talking about it, and in the end, I didn't even know how to tell the big O of my own algorithm, now, 10 minutes later, I understood it with a 6 min yt video
I have always enjoyed your humour, cheers and great vid
what humor?
good to hear you in a non-coding video lesson, bro!
Very useful. As a bonus I didn't know the sum of n is the same as n*(n+1)/2
Simple and covered. Thank you
From Sri Lanka
Yoooo, My favorite comp sci. channel is back at it again
Have you looked into rust at all? I’ve just started diving into the documentation, and I gotta say, it’s so much better than anything else I’ve used previously
I learned more in 6 minutes , then I did going to class and tutoring for the past couple of weeks
bro is on the way to 100k 🥳
really looking forward for future vids
this video explain well the topic. Thank you alot for your time for making this tutorial video.
this man is the plug!
this is so easy to understand. thanks bro!
This right here is a great man
Super clear and concise. Thanks bro!🎉
Great explanations! Thanks for share.
You are such a great man keep it going 💞🔥
Bro code is different than other tutors xD. awesome
Excellent amazing video. Thumbs up 👍 .
You always rock it down bro!....huge admiration to yuh !
Extremely straightforward
This is amazing summary, many thanks!
Your video was o(1) for my mind ❤ thank you
Nice explanation as usually 👍 🌸
Awesome and simple, thanks a n!
Thank you! This is a great foundation for me to learn more.
“you get expelled”
imagine getting expelled over bogosort lmao
After like ten videos, this is the best video by far. 0(1) for sure
Great video bro, subbed.
Amazing, thank you, bro!
Expelled from the school 😂. Excelente video hasta ahorita el mejor explicado
Many thanks! This video is really good for beginners!
Great explanation, thanks Bro!
bro i was a hater for learning bigO
notation before watching your video. 😡
cause i cant understand that much.😬
you made me understand this bro. 😘
have you uploaded the "travelling salesman problem" video?🤨
Useful video! Thanks bro
wow very good explaination thank you!
Love your videos, brooo
Thanks, it blew my mind
such a amazing explaination by the help of graph 🤩
Sir, I am very, very sorry not mentioning you title , you are great teacher,you just target what we learner need thanks a lot 😂❤❤❤❤❤
clear explanation 👌👌
You could add the precise definition of Big O notation, not only the intuition behind it
So we can also say that the shorter the data, the faster the supposed slow big O notations? Like 0(n^2) is faster than 0(log n). I'm no cs student so I'm bad at math 😞
so good explanation bro
fire explanation! thanks!
4:32 Quadratic time
Bro, Thanks! Appreciate it!
Great video man!
My professor's explanation that took me 4 months but still didn't get it, until I saw this video.
i love this guy i stg
Underrated!
which programming language are you gonna use for this DSA course?
Nice explanation Bro!!!
Asante kwa maelekozo mazuri
Hey Bro!!!! Hope u are doing well. Thanks for such awesome content🔥🔥🔥
Love❤️
awesome explanation! Thanks
Amazing ❤
Awesome overview
Thanks for these videos man
Thanks a lot for sharing all of this.
school took 3 months to teach this and i had no idea what it meant. got it in 6 minutes from Bro
Hold up, how are the two functions at the start the same??? first one adds up n, the second one performs exponentiation and divides it by 2? Two different results.
Order of operations
PLZ MORE DSA. luv u
im learning this thing but i have no idea about anything in CS
my major doesnt have CS - what should i study before this so i get a basic understanding?
Awesome bro
Like I always say, my python hero
Great video!
thanks for the short explanation
You're the bro
This was great!
Revision covered, my g
n! getting expelled is crazyyyyy, but I agree lol
Is this videos by order, should I watch the Playlist from the top?
Learn Big O notation in 6 minutes 📈
1:09 shouldn't it be exponential time?
exponential is for nested loop
@@ryuzoraa but when we increase the amount of data by one byte we get twice the computation time
Thanks my Bro!