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.
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
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!
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!
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
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 😂
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
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, 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
by looking at the graph can someone tell me until what value of n should I use in each O(n)? for example if n=100000000000 it's better to acheive a solution that has o(1) but if n=1 it seems that O(n) is better
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?🤨
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?
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 @@noamrtdthesorcerer733
Those 6 minutes were more useful than 6 months of lectures. Thanks
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
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!
It's preposterous that you can make everything this simple and smoothly learnable. Thx a lot for real
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!
I kinda somewhat get Big O notation now on a high level. that graph helped so much. Google in 3 years here I come!
The guy needs to be seriously appreciated!
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
Please keep making more videos about this it helps for interviews thanks bro
That was just an amazing video. Keep up the hardwork and effort you put into your videos.
Thank you so much bro code, I'm watching your channel,it will grow bigger then your expected
Thank you! Great code examples to demonstrate the "steps" it takes. :D
bro is on the way to 100k 🥳
really looking forward for future vids
You are amazing, Bro!!
this man is the plug!
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
You are such a great man keep it going 💞🔥
Super clear and concise. Thanks bro!🎉
Thank you! This is a great foundation for me to learn more.
The easiness of this man's explanation is incredible
this is so easy to understand. thanks bro!
This right here is a great man
Amazing, thank you, bro!
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 😂
Many thanks! This video is really good for beginners!
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
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
fire explanation! thanks!
this video explain well the topic. Thank you alot for your time for making this tutorial video.
Underrated!
Awesome and simple, thanks a n!
i love this guy i stg
Great explanations! Thanks for share.
I have always enjoyed your humour, cheers and great vid
what humor?
Excellent amazing video. Thumbs up 👍 .
Thanks for these videos man
This was great!
You always rock it down bro!....huge admiration to yuh !
awesome explanation! Thanks
wow very good explaination thank you!
Thanks a lot for sharing all of this.
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.
Love your videos, brooo
Very useful. As a bonus I didn't know the sum of n is the same as n*(n+1)/2
Hey Bro!!!! Hope u are doing well. Thanks for such awesome content🔥🔥🔥
Love❤️
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
Nice explanation as usually 👍 🌸
Love you bro
Awesome overview
Great video!
PLZ MORE DSA. luv u
Thanks!
You're the bro
Great thanks!
Awesome bro
Bro code is different than other tutors xD. awesome
thanks bro!
After like ten videos, this is the best video by far. 0(1) for sure
Respect bro 👊
such a amazing explaination by the help of graph 🤩
Thanks for your efforts
Thanks my Bro!
You are the best
thanks for the short explanation
Needed this video bro
Nice explanation Bro!!!
Legend
cool!
Expelled from the school 😂. Excelente video hasta ahorita el mejor explicado
Lets go!
amaaazing
thanks habibi
Thank you Bro
Thx Bro!!!
so Good thanks Bro Code
Revision covered, my g
Like I always say, my python hero
by looking at the graph can someone tell me until what value of n should I use in each O(n)? for example if n=100000000000 it's better to acheive a solution that has o(1) but if n=1 it seems that O(n) is better
ty
Thx bro
Thanks bro
which programming language are you gonna use for this DSA course?
Nice and succinct
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?🤨
thx
Nice class
Thank you bro code
class video
can you do a tutorial on webpack 5 ?
Asante kwa maelekozo mazuri
Bro can you make tutorial for mips assembly?
Is this videos by order, should I watch the Playlist from the top?
this is huuge, well done man.
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?
n! getting expelled is crazyyyyy, but I agree lol
You could add the precise definition of Big O notation, not only the intuition behind it
Bro , is Python language can get us job or we have to learn cpp , Java like that. Plz tell
nice