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!
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.
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 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 😂
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
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
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
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.
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 😞
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
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
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!
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!
Please keep making more videos about this it helps for interviews thanks bro
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.
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
The guy needs to be seriously appreciated!
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 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 😂
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
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
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
I'm really grateful for this channel, it save me in a lot of time.
Honestly, the best explanation of Big O, thanks you!
Very useful. As a bonus I didn't know the sum of n is the same as n*(n+1)/2
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
That was just an amazing video. Keep up the hardwork and effort you put into your videos.
The easiness of this man's explanation is incredible
Thank you! Great code examples to demonstrate the "steps" it takes. :D
You are amazing, Bro!!
I have always enjoyed your humour, cheers and great vid
what humor?
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.
Simple and covered. Thank you
From Sri Lanka
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
Extremely straightforward
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.
good to hear you in a non-coding video lesson, bro!
I learned more in 6 minutes , then I did going to class and tutoring for the past couple of weeks
Thank you! This is a great foundation for me to learn more.
this man is the plug!
This right here is a great man
You are such a great man keep it going 💞🔥
Many thanks! This video is really good for beginners!
wow very good explaination thank you!
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?🤨
this is so easy to understand. thanks bro!
Super clear and concise. Thanks bro!🎉
Bro code is different than other tutors xD. awesome
Thanks, it blew my mind
Great explanations! Thanks for share.
Awesome and simple, thanks a n!
You could add the precise definition of Big O notation, not only the intuition behind it
Nice explanation as usually 👍 🌸
You always rock it down bro!....huge admiration to yuh !
Excellent amazing video. Thumbs up 👍 .
Bro, Thanks! Appreciate it!
Amazing, thank you, bro!
fire explanation! thanks!
Great video bro, subbed.
“you get expelled”
imagine getting expelled over bogosort lmao
Thanks!
You're the bro
i love this guy i stg
clear explanation 👌👌
After like ten videos, this is the best video by far. 0(1) for sure
Underrated!
Thanks a lot for sharing all of this.
Love your videos, brooo
Great video man!
which programming language are you gonna use for this DSA course?
Thanks for the help
Asante kwa maelekozo mazuri
Amazing ❤
awesome explanation! Thanks
Hey Bro!!!! Hope u are doing well. Thanks for such awesome content🔥🔥🔥
Love❤️
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 😞
Great thanks!
Expelled from the school 😂. Excelente video hasta ahorita el mejor explicado
thanks for the short explanation
Is this videos by order, should I watch the Playlist from the top?
Thanks for your efforts
thanks habibi
PLZ MORE DSA. luv u
My professor's explanation that took me 4 months but still didn't get it, until I saw this video.
Awesome overview
such a amazing explaination by the help of graph 🤩
Thanks for these videos man
Nice explanation Bro!!!
Awesome bro
You are the best
Great video!
amaaazing
This was great!
Like I always say, my python hero
n! getting expelled is crazyyyyy, but I agree lol
Go to heaven!
so Good thanks Bro Code
Nice class
Thanks my Bro!
Bro , is Python language can get us job or we have to learn cpp , Java like that. Plz tell
thanks bro!
Revision covered, my g
class video