reading-notes

This Repo required for Asac labs class 2

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Pain vs. Suffering

The pain that you’ll experience during this course is no exception; most times it won’t feel good. You won’t feel good.

That price is pain, the pain of growth

All growth comes with some degree of pain, as it pulls you out of your comfort zone. The greater the growth, the greater the pain. But pain in the service of growth is a good thing, as long as that pain is what’s necessary to achieve the growth that you’re aiming for. And even better than that, this pain is only temporary. It’s what will launch you forward into the next phase of your life.

The greater the growth, the greater the pain.

• suffering is pain without purpose. Pain with no higher goal. Pain with no dreams, no ambition, no aspiration.

Big O Notation

A beginner’s guide to Big O notation(Summary)

• Big O notation : describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.
• ` O(1)` : describes an algorithm that will always execute in the same time (or space) regardless of the size of the input data set.
• O(N) : describes an algorithm whose performance will grow linearly and in direct proportion to the size of the input data set
• O(N2) : represents an algorithm whose performance is directly proportional to the square of the size of the input data set. This is common with algorithms that involve nested iterations over the data set. Deeper nested iterations will result in O(N3), O(N4) etc.
• O(2N) : denotes an algorithm whose growth doubles with each additon to the input data set. The growth curve of an O(2N) function is exponential - starting off very shallow, then rising meteorically
• O(log N) : The iterative halving of data sets produces a growth curve that peaks at the beginning and slowly flattens out as the size of the data sets increase

names and values in Python

• Assignment never copies data.

• Python is neither “Call By value” nor “Call By Reference”, it’s “Call by Assignment”! Epic!
• There is no way in python where a name can refer to another name.
• names refers to values in python
• many names can refer to one value
• if we have a list ,so if we modify it then all names that referring to that list will change
• so the mechanisms almost the same between python and other languages ,but the effects can be surprising in python