Learn Big O notation in 6 minutes 📈

Big O notation tutorial example explained
#big #O #notation

[@CallMeAce153]

Good thing our professor needed 5 hours to explain that graph...

[@Swift_Media]

College is a scam but unfortunately we gotta do it lmfao

[@noamrtdthesorcerer733]

Mine explained it in 5 minutes so no one understood it (lol) At least he came to a conclusion at the end

[@jasonwang-wg8wu]

Cold, crushing grip of academia got you too?

[@philippemts88]

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.

[@ditz3nfitness]

Here too, lol. I didn't understand a single thing, and nobody else did either @@noamrtdthesorcerer733

[@redaboukdir5678]

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

[@Evoyde_]

Thank you bro !!!!

[@thanosmylonas9781]

Thank you so much Bro

[@DMPLAYER1000]

Thank you bro! I am in love with you for this

[@ninoe9124]

such a goat fr bro

[@CountDracula-vq8zn]

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

[@saurabhbasak9545]

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!

[@berkan5018]

It's preposterous that you can make everything this simple and smoothly learnable. Thx a lot for real

[@mactenssheelal1840]

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

[@rishabhverma7064]

I just discovered this channel and goes through the python course I must say...... U deserve 🙏🙏🙏🙏🙏

[@wrathofainz]

"Prays" lmao

[@mohammedrangoonwala9328]

That was just an amazing video. Keep up the hardwork and effort you put into your videos.

[@ditz3nfitness]

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!

[@omardumet120]

Please keep making more videos about this it helps for interviews thanks bro

[@youvegotmail9385]

I kinda somewhat get Big O notation now on a high level. that graph helped so much. Google in 3 years here I come!

[@andresroca9736]

Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙

[@flashbao1922]

The guy needs to be seriously appreciated!

[@lemapperium5344]

The easiness of this man's explanation is incredible

[@mahendranath2504]

Thank you so much bro code, I'm watching your channel,it will grow bigger then your expected

[@AbhijeetKumar-cm3jh]

bro is on the way to 100k 🥳 really looking forward for future vids

[@somethingwine]

Thank you! Great code examples to demonstrate the "steps" it takes. :D

[@AnthonyR007]

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

[@CoffeeXmusic]

Super clear and concise. Thanks bro!🎉

[@jackvianey1630]

You are amazing, Bro!!

[@zohirboshi2245]

You are such a great man keep it going 💞🔥

[@hariprasaanth3115]

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 😂

[@ThapeloMachaka256]

this man is the plug!

[@estelle9819]

this is so easy to understand. thanks bro!

[@hermitmobile1742]

This right here is a great man

[@deepdaddy1208]

I have always enjoyed your humour, cheers and great vid

[@rewardx]

what humor?

[@BPEKSupraInteractive]

Wow. Thanks for helping me understand Big O here than the 3 weeks we spent on in class lol

[@ChrisAthanas]

Universities are about to go bye-bye

[@danser_theplayer01]

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.

[@PythonDS]

Great explanations! Thanks for share.

[@Snowmanver2]

Amazing, thank you, bro!

[@itsashhuby6781]

i love this guy i stg

[@jeremiahgatwechpeter4222]

You always rock it down bro!....huge admiration to yuh !

[@alonasmatko9670]

Many thanks! This video is really good for beginners!

[@MellowJ2000]

Thanks for these videos man

[@dribrahimel-nahhal2477]

Excellent amazing video. Thumbs up 👍 .

[@leoniaklebanov2502]

Awesome and simple, thanks a n!

[@nikitos2442]

Underrated!

[@CSstudent_1001]

Nice explanation as usually 👍 🌸

[@Garrison86]

awesome explanation! Thanks

[@Diamond_Hanz]

Love your videos, brooo

[@adityarajsrivastava6580]

This was great!

[@walrexx_2370]

this video explain well the topic. Thank you alot for your time for making this tutorial video.

[@moglimogify]

Very useful. As a bonus I didn't know the sum of n is the same as n*(n+1)/2

[@skolan4834]

wow very good explaination thank you!

[@trntrinh79]

Thanks a lot for sharing all of this.

[@ProgamerEU]

Awesome overview

[@melonolandsorryformybadeng2815]

Bro code is different than other tutors xD. awesome

[@bruno10505]

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

[@wolanus]

Great video!

[@MultimediaCizzy]

PLZ MORE DSA. luv u

[@numiinsoles627]

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

[@ultimategr2363]

Love you bro

[@charitharanasingha]

Nice explanation Bro!!!

[@keerthansj845]

Awesome bro

[@user-dc5pf2vf1p]

Thanks for your efforts

[@vedantdeshmukh4949]

Hey Bro!!!! Hope u are doing well. Thanks for such awesome content🔥🔥🔥 Love❤️

[@gerardsk8ordie]

thanks for the short explanation

[@PythonArms]

Great thanks!

[@shiftshaddow6487]

After like ten videos, this is the best video by far. 0(1) for sure

[@quotablecode]

Respect bro 👊

[@cjrozor918]

Thanks my Bro!

[@yasserj3693]

You are the best

[@natifun9877]

Thanks!

[@raajroy5931]

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?🤨

[@JJavierCueto]

Expelled from the school 😂. Excelente video hasta ahorita el mejor explicado

[@kunalsoni7681]

such a amazing explaination by the help of graph 🤩

[@Maros554]

You're the bro

[@ddam712]

thanks bro!

[@ty6512]

Needed this video bro

[@awwtawnoo]

which programming language are you gonna use for this DSA course?

[@mondlistyleman9389]

Like I always say, my python hero

[@notblal6669]

so Good thanks Bro Code

[@Extrify_]

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?

[@iRandroid]

Is this videos by order, should I watch the Playlist from the top?

[@kristijanlazarev]

amaaazing

[@SizarieldoR]

Nice and succinct

[@jvg6877]

thanks habibi

[@orazovdidar]

Thank you Bro

[@ashyvlogs1132]

Revision covered, my g

[@Helmansonable]

You could add the precise definition of Big O notation, not only the intuition behind it

[@user-ek8uw1rt6z]

cool!

[@tomaszkarolak6179]

Thx Bro!!!

[@victorrezende6002]

Nice class

[@Infinity540]

Legend

[@Arsy1999]

class video

[@snapreal9814]

Asante kwa maelekozo mazuri

[@oguzhantopaloglu9442]

can you do a tutorial on webpack 5 ?

[@minhtriettruong9217]

Lets go!

[@suspiciousgoose7904]

Thanks bro

[@c.d.premkumar6867]

1.05 Isn't (Log n) less than (n) ? Then why is it not in ascending order ?

[@flychy3268]

this is huuge, well done man.

[@derboss66]

Thx bro

[@mariostheophanous2300]

Bro can you make tutorial for mips assembly?

[@letsthinkforamoment8904]

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.

[@Hellbending]

Order of operations

[@larissakenneh]

Thank you bro code

[@theencryptedpartition4633]

n! getting expelled is crazyyyyy, but I agree lol

[@rockstareditz4690]

Bro , is Python language can get us job or we have to learn cpp , Java like that. Plz tell

[@nomadybb1-66er7]

nice

[@martinbabala6719]

1:09 shouldn't it be exponential time?

[@oussemaelmzergui7055]

ty

[@Heyjo300]

thx