reading-notes

Pain vs. Suffering

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.
As you experience pain, seek the remedy! Ask questions that bridge the gap between what you know and what you need to be able to do.
You’re here because you chose to invest in a different life. A better life.

We use big-Θ notation to asymptotically bound the growth of a running time to within constant factors above and below.
Big-O-notation is used in Computer Science to describe the performance or complexity of an algorithm.
Can be used to describe the execution time required or the space used by an algorithm.
represents an algorithm whose performance is directly proportional to the square of the size of the input data set
O(2N) denotes an algorithm whose growth doubles with each additon to the input data set.
big-Θ notation bounds the running time from both above and below, rather than just from above.
This Chart will explain more:

Logarithms are slightly trickier to explain so I’ll use a common example:
Binary search is a technique used to search sorted data sets. It works by selecting the middle element of the data set, essentially the median, and compares it against a target value.

This talk is about some fundamental concepts in Python: names and values
Python is a very approachable language. Often it works just as you expect if you come to it from other languages. But you might suddenly encounter surprising behavior.
The underlying mechanisms of Python are often quite simple, but their combined effects might not be what you expect. By understanding the mechanisms, you can reason about their effects.
The local names in the function are drawn in a new frame. Calling the function assigned the actual values to the parameter names, just like any other assignment statement