#August_25_LeetCode_Grid

🚀 Backtracking in DSA – Quick Recap

Backtracking is like exploring all paths in a maze. You try options, go forward, and undo your choices if they don’t work.

How it works:

Choose: Pick a possible option.

Explore: Move forward recursively with that choice.

Backtrack: Undo the choice to try other possibilities.

✅ Key idea: Explore all possibilities systematically, but prune paths that fail early.
✅ Use cases:

Subsets & permutations

Combination sum problems

Sudoku & N-Queens

Maze & pathfinding problems

💡 Tips:

Always have a base case to stop recursion.

Make sure to undo changes before returning to explore other options.

Example in LeetCode: Combination Sum, Subsets, N-Queens

@byte_philosopher

This days what I understand from solving leetcode is, you always have to think in the reverse way. (ገልብጦ ማሰብ😁 ) is the better way.

For ex: if your first thought for the solution was addition then use substraction boom it works😁

@byte_philosopher

#August_26_LeetCode_Grid

👑 Heap: The King of Efficiency 👑

Ever wondered how to always grab the biggest or smallest number FAST? ⚡
That’s where Heaps come in!

🔥 What’s a Heap?

A tree-like structure 📚

Min-Heap → smallest on top (root).

Max-Heap → largest on top (with a trick in Python 😉).

⚡ Why are they cool?

📍 Always gives you the top element in O(1) time.

➕ Insert / ❌ Remove in O(log n).

Used in priority queues, scheduling, Dijkstra’s algorithm, and finding kth largest/smallest element!

🐍 Python Example:

import heapq

nums = [5, 2, 8, 3, 1]

heapq.heapify(nums)   # min-heap
print(nums[0])        # 👉 1 (smallest)

# max-heap trick
nums = [-x for x in nums]
heapq.heapify(nums)
print(-nums[0])       # 👉 8 (largest)

💡 Remember:
Heaps don’t fully sort data, they just keep the top element ready at all times 🚀.

⚔️ Next time you need efficiency → Just call the Heap King 👑
@byte_philosopher

ሀሙስ የቀን ቅዱስ😉

@byte_philosopher

“Let us acquire reverence, dignity, and meekness towards all people, as well as precise knowledge of them. so that we may be able to avoid familiarity, which is the mother of all evils.”

+Abba Moses

#August_27_28_29_LeetCode_Grind

HashMaps (Python dict) Recap

A HashMap stores data in key–value pairs for super-fast access ⚡.
Think of it like a dictionary 📖: words = keys, meanings = values.

✅ Key Features:

Fast lookups, inserts, deletes → O(1) average

Keys are unique

Perfect for counting, mapping & caching

hashmap = {"apple": 2, "banana": 5}

print(hashmap["apple"])  # 2
hashmap["banana"] = 10   # update
hashmap["grape"] = 7     # insert
del hashmap["apple"]     # delete

🔥 Use Cases:
✔️ Word frequency counters
✔️ Caching results
✔️ Graph adjacency lists

@byte_philosopher

#August_30_LeetCode_Grid

📌 Graphs in DSA

A Graph = 🟢 Nodes (called vertices) + 🔗 Connections (called edges).
They’re everywhere — social networks, maps, recommendations, even computer networks!

Types of Graphs:

Directed vs Undirected ➡️ / ↔️

Weighted vs Unweighted ⚖️

Cyclic vs Acyclic 🔄 / 🚫

Ways to Store Graphs:

Adjacency List ✅ (efficient)

Adjacency Matrix 🔲 (easy but heavy)

Popular Graph Algorithms:

BFS (Breadth-First Search) 🔍 level by level

DFS (Depth-First Search) 🌊 go deep first

Dijkstra 🛣️ shortest path

Kruskal & Prim 🌐 minimum spanning tree

👉 Mastering graphs = mastering real-world problem solving.

@byte_philosopher

This is insane guys read it

Psalms 14:1
"The fool says in his heart, ‘There is no God.’"

2017 Reflections
2. I almost went insane...

https://telegra.ph/I-Almost-Went-Insane-08-31

Are you ready for tomorrow it's Monday plus sep 1?

@byte_philosopher

Happy Monday, have a productive week❤️

@byte_philosopher

#August_31_september_01_LeetCode_Grind

🚀 Dynamic Programming (DP) – The Brain of DSA 🧠

Dynamic Programming is one of the most powerful techniques in problem-solving. It’s all about breaking a complex problem into smaller overlapping subproblems, solving each subproblem once, and storing the results for reuse.

✨ Why DP matters?
Avoids repeated work ➝ makes algorithms efficient.

Solves problems that plain recursion or brute force cannot handle in time.

Forms the backbone of advanced algorithms in AI, bioinformatics, and optimization.

📌 Key Ideas in DP:
1. Overlapping Subproblems → Solve once, reuse results.

2. Optimal Substructure → The solution to a problem depends on solutions to smaller subproblems.

3. Memoization (Top-Down) → Store results of recursive calls.

4. Tabulation (Bottom-Up) → Build a table iteratively to reach the final answer.

⚡ Classic DP Problems:
Fibonacci Numbers 🔢
Longest Common Subsequence
Unique Paths in a Grid 🤖

Knapsack Problem 🎒

👉 Mastering DP = unlocking the ability to solve the toughest algorithmic problems efficiently.

💡 Remember: Think recursive, optimize with memory.

#DSA #DynamicProgramming #Coding
@byte_philosopher