- Most Recommended Book
- YouTube
- Sites
- Code Training
- KhanAcademy
- Recursion 💀
- Data Structs Visualization
- Work Hard, Have Fun & Make Things 😄 ❤️ 👶
- Big O
- A beginner's guide to Big O notation
- A coffee-break introduction to time complexity of algorithms
- O(1) Constant Time
- Best Case Possible
- If an algorithm has a constant time, it means that it always takes the same amount time to produce the output.
- Example: array.pop()
- Logarithms O(log n)
- Preferible in most of times
- Logarithms are the inverse of exponentiation.
- Example: Classic Binary search algorithm -> divide-to-conquer
-
- Linear time O(n)
- Preferible in most of times
- If an algorithm has a linear time, it means that the running time of an algorithm grows as the input size grows.
- Example: array.forEach() sum all values
- Linear Logarithms O(n log n)
- Acceptable
x = n while ( x > 0 ) { y = x while ( y > 0 ) { y = y / 2 } x = x - 1 }- Example: classic merge sort -> divide-to-conquer
- Quadratic time O(n²) 💀
- Nice To Avoid
- The running time of an algorithm is directly proportional to the square of the size of the input.
- Example: sum of matrizes
for (var outer = 0; outer < elements.Count; outer++){ for (var inner = 0; inner < elements.Count; inner++){ ... } } - Exponential Time O(2^n) 💀 💀
- Worst Case Possible, always try to avoid
- 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
- Example: recursive calculation of Fibonacci numbers
int Fibonacci(int number){ if (number <= 1) return number; return Fibonacci(number - 2) + Fibonacci(number - 1); }
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- Medium - A LOT
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- Reddit - A LOT
- Rafael Falcon
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- WaitButWhy
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- Phil Calcado
- VidaDeSuporte
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- ProfissionaisTI
- O que é propósito de vida
- The Invented History of 'The Factory Model of Education'
- The Humble Programmer by Edsger W. Dijkstra
- Teach Yourself Programming in Ten Years - Peter Norvig
- Getting Real - the smarter, faster, easy way to build a sucesfull web application - BaseCamp
- Educação Clássica e Homeschooling
- Principles for Success - Ray Dalio
- The Twelve Factor App
- Write Code Every Day - John Resig
- Why Good Programmers Are Lazy and Dumb
- The Feynman Technique: The Best Way to Learn Anything - Easy to Understand | Hard To Master
- WaitButWhy - The Cook and the Chef: Musk’s Secret Sauce - Easy to Understand | Hard to Master
- Rob Pike's 5 Rules of Programming
- The Ten Commandments of Egoless Programming
- What makes a good programmer?
- Self-Education: Teach Yourself Anything with the Sandbox Method
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"I am always ready to learn although I do not always like being taught."
- Winston Churchill