coding tip: talk to other programmers

Share Ideas.

Hello,

Night Owls....

any one here ?

Hello, fellow coders and developers!

May your week be filled with productive coding sessions and bug-free deployments 😊

@EmmersiveLearning

Which one do you use 🤔

This is The Conversation that make Elon buy twitter 😊

Never Underestimate the power of talking with your commenters.😊

it may open another fortune.

Do you need this books ?

You will love it.

Here is their PDF version. 👇

https://news.1rj.ru/str/MuhaTeshome

Here are the known algorithm design techniques :

1. Divide and Conquer:
- Break a problem into smaller subproblems.
- Solve them independently.
- Combine the solutions.

2. Greedy:
- Make locally optimal choices.
- Aim for a global optimum.

3. Dynamic Programming:
- Break complex problems into simpler subproblems.
- Store solutions for efficiency.

4. Backtracking:
- Enumerate all possible candidates.
- Check if they satisfy the problem's statement.

These strategies are fundamental in computer science for solving complex problems efficiently.

@EmmersiveLearning

New Video: Algorithm.

i know most of the people wants me to make framework and High level videos.

but, it's better to know the foundation.

This series of computer Science core concepts will be very helpful for self thought developers.

GO and learn the gist.

https://www.youtube.com/watch?v=4ae6gujJqjI

which habit do you have ?

what ?

Different types of algorithms :

1. Brute Force Algorithm:
- Tries all possible solutions.
- Simple but can be inefficient.

2. Recursive Algorithm:
- Breaks problems into smaller subproblems.
- Often used in divide-and-conquer approaches.

3. Dynamic Programming Algorithm:
- Optimizes solutions by reusing results.
- Common for optimization problems.

4. Divide And Conquer Algorithm:
- Divides problems, solves, and combines results.
- Efficient for sorting and searching.

5. Greedy Algorithm:
- Makes locally optimal choices.
- Used in minimum spanning trees, Huffman coding.

6. Backtracking Algorithm:
- Explores all options systematically.
- Useful for problems with constraints.

7. Randomized Algorithm:
- Introduces randomness for efficiency.
- Examples: Monte Carlo simulations, randomized quicksort.

@EmmersiveLearning