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Title: DSA
Description: I will provide lecture wise notes one after the other in this there r notes of 3 lectures

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because you will be able to understand the C program better and be able to ask
more questions on the C program
...
I 'm talking about C program that 's
why I say that you get a good picture of memory with the help of C programming
therefore , data structures and algorithms is best learned from C and C++ Now
here I am dividing it into segments so there is thing called code segment let 's say
this is my code let 's say there is a code with the name `` harry
...
fun2 ( )
is calling inside fun1 ( ) now the variables that I had created insi de it will be created
here let 's say I have created `` k '' & `` l '' let me write here : initialize k and l and
after that I 'm calling fun2 ( ) so as soon as fun2 ( ) will be called it will say to fun1 (
) that you wait for a while , I call fun2 ( ) and be back by fetching the value
whatever it will return fun1 ( ) replied that you go and get it call fun2 ( ) and get its
value
...
it
will say to fun1 ( ) that you 're coming back and now you can start your execution
again
...
So that thing is done here with the help of dynamic memory because
when does the stack of a function end it ends when the function is returned
...
You go and get i t and then what will
happen, fun function will be called
...

Time Complexity and Big O Notation (with notes)
CodeWithHarry
So the input size didn’t increase and the runtime of the algorithms didn’t increase either
...
When we ask questions like as the input will increase, Then the runtime
will change as per what? And after that Now you will go to aunty’s house You will be treated
...


Is the algorithm that runs in constant time
...
The sentence is: Run time of it, there are some things that we will recite
...
Now, come here and listen to another
story
...
If the game is of N kb then how much time will you need? The
sentence is: Run time of it, there are some things that we will recite
...
There are also algorithms that are not linear in time
...


Asymptotic Notations: Big O, Big Omega and Big Theta Explained (With Notes)
CodeWithHarry
We ‘ll talk a little bit about asymptotic notation
...
We talked about ordering
...
Big O is set to be O ( g ( n ) ) if and only if there exist a constant ( c )
and a constant n -node such that 0 ≤ f ( n) ≤ cg (n) is O (g (N) If you watch this video completely then I
guarantee that you will understand these three notations
...
When we do analysis of algorithms comparing any 2 algorithms then f ( n ) will be time
and what is n , it ‘s input ok , size of input
...
O ( n²)
is Anything Can Be Algorithm it is g (n) that will be here and which is your algorithm
...
If you ca n’t find it then its is not f (n ) is O
...



Title: DSA
Description: I will provide lecture wise notes one after the other in this there r notes of 3 lectures