This was a very good beginner-friendly problem that helped me understand matrix traversal and indexing concepts clearly. 🔹 I learned how to: ✔ Work with 2D arrays (matrices) ✔ Access primary and secondary diagonals ✔ Calculate sums efficiently ✔ Use absolute difference in a practical problem The problem required calculating the absolute difference between the sums of the two diagonals of a square matrix. It strengthened my logical thinking and confidence in handling matrix-based questions. As a beginner, solving problems like this gives me motivation to keep improving every day. 🚀 Consistency + Practice = Growth 💪 #HackerRank #Matrix #Python #ProblemSolving #CodingJourney #BeginnerProgrammer #Consistency
Matrix Diagonal Sums with Python
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Two-pointer technique in action! This solution finds the closest pair from two sorted arrays whose sum is nearest to a target x — all in O(n + m) time. 🔹 No nested loops 🔹 Optimal pointer movement 🔹 Simple logic, powerful result A great example of how understanding patterns beats brute force every time. Perfect for coding interviews and real-world problem solving 🚀 #Python #DSA #Algorithms #TwoPointers #CodingInterview #ProblemSolving #SoftwareEngineering
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Solved an interesting array problem using prefix sums + hash maps 🚀 This solution efficiently finds the longest subarray based on a comparison condition (arr[i] > k) by converting the problem into a prefix sum balance and tracking first occurrences with a hashmap. ✅ O(n) time complexity ✅ Smart use of prefix sums ✅ Great example of turning a complex condition into a simple math problem Always fun when problem-solving meets optimization! 💻📊 #Python #DataStructures #Algorithms #CodingInterview #ProblemSolving #100DaysOfCode
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🚀 Day-66 of #100DaysOfCode 📊 NumPy Practice – Image Matrix Manipulation Today I simulated a grayscale image using NumPy and performed a simple brightness adjustment. 🔹 Concepts Practiced ✔ Random matrix generation ✔ Array arithmetic operations ✔ Pixel value clipping using np.clip() ✔ Understanding image data as matrices 🔹 Key Learning Images in computer vision are essentially NumPy matrices, where each element represents a pixel intensity. NumPy makes it easy to manipulate these values efficiently. Exploring how NumPy connects with image processing and computer vision 📸✨ #Python #NumPy #DataScience #ComputerVision #MachineLearning #100DaysOfCode
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🚀 Day-68 of #100DaysOfCode 📊 NumPy Practice – Chessboard Pattern Today I created a chessboard pattern using NumPy. 🔹 Concepts Practiced ✔ Matrix creation using np.zeros() ✔ Advanced array slicing ✔ Pattern generation using NumPy 🔹 Key Learning NumPy slicing allows creating complex patterns efficiently without loops. This type of matrix manipulation is useful in image processing and grid-based computations. Building deeper understanding of NumPy array operations 🚀 #Python #NumPy #MatrixManipulation #PythonProgramming #100DaysOfCode #LearnPython
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🔁 Rotate Matrix by 90° — Clean In-Place Trick! Solved the classic Rotate Matrix by 90° problem using a simple observation: • Transpose the matrix • Reverse it column-wise This approach rotates the matrix in-place with O(n²) time and O(1) extra space. Sometimes the best solutions come from simple transformations on the matrix. 🚀 #DSA #ProblemSolving #Python #CodingInterview #Algorithms
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Floyd's Cycle Detection always felt like magic to me. Two pointers. One moves 1 step. The other moves 2. If they meet — there's a cycle. Then a second phase finds exactly where the cycle starts. But WHY does this work? Why does resetting slow to head and moving both at 1 step guarantee they meet at the cycle start? The proof is mathematical, but the intuition is visual. I built an animation that shows the tortoise and hare chasing through the cycle. You watch them converge, meet, and then trace back to the start — step by step, synced with Python code. Once you see it, you never forget it. https://lnkd.in/gHhQtANE #FloydAlgorithm #TwoPointers #LeetCode142 #CodingInterview #Algorithms #DataStructures #Python #FAANG #AlgoVision
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Just wrapped up an implementation that counts all square submatrices with a given sum using 2D prefix sums. 🧮 🔍 What’s happening here? Build a prefix sum matrix to enable O(1) submatrix sum queries Iterate over all possible square sizes and positions Check each square’s total efficiently without recomputing sums ⚡ This approach dramatically improves performance compared to brute force and is a powerful technique for grid-based problems in interviews and competitive programming. 📌 Key concepts: #Python #Algorithms #DataStructures #PrefixSum #ProblemSolving #CodingInterview
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week challenge ✅ Today: A Virtual Mouse that lets me control my PC with hand gestures. Turning hand gestures into system controls ✋💻 ☝️ Index finger → move cursor ✌️ Two fingers → click 🖐️ Thumb + Index → volume control Built with Python, OpenCV, PyAutoGUI and MediaPipe. The cursor smoothing alone took longer than the gesture detection. Check out the source code on GitHub : https://lnkd.in/gW7Fi3sX What should do next? Drop your ideas below👇. #ComputerVision #Python #BuildInPublic #OpenCV #7DayChallenge
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A clean and efficient approach to finding the longest span with equal sum in two binary arrays 🚀 This solution uses prefix sum differences and indexing optimization to achieve O(n) time and O(n) space, avoiding nested loops altogether. Always satisfying when math + data structures simplify a problem this much. 💡 #DataStructures #Algorithms #Python #ProblemSolving #CodingInterview
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The best side projects come from your kids asking, "How does this work?" Some time ago, I showed them that viral "squint to see Jesus" image. I could explain in rough strokes what happens and why this works. I wanted to try it out myself, so I sat down and wrote a Stable Diffusion pipeline with a control net in Python. I finally had the time to put some results into a small video, which also shows how you can turn stick-figure drawings into colorized images. Watch the Video here: https://lnkd.in/d5Kk6fvS
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