Minimum Distance Between Three Equal Elements Solved with HashMap

Day 248 of #365DaysOfCode Solved a problem involving mirror pairs and index distance minimization using a hashmap-based approach. For each element, computed its digit-reversed counterpart and tracked indices to identify valid mirror pairs. The minimum distance was updated by comparing current positions with previously stored indices. The solution runs in O(n · d) time, where d is the number of digits, and efficiently leverages hashing for constant-time lookups. Continuing to build intuition around number manipulation and indexing strategies. #365DaysOfCode #Day248 #DSA #LeetCode #Python #Algorithms #HashMap #ProblemSolving #Consistency

Day 250 of #365DaysOfCode Solved Intervals Between Identical Elements using a hashmap with prefix sum optimization. Grouped indices of identical values and computed distances by leveraging prefix sums to avoid redundant pairwise calculations. This reduces the complexity from quadratic to linear over grouped indices. The approach efficiently calculates contributions from both left and right sides for each index. Continuing to strengthen optimization techniques using prefix sums and grouping strategies. #365DaysOfCode #Day250 #DSA #LeetCode #Python #Algorithms #PrefixSum #HashMap #ProblemSolving #Consistency

Group Anagrams: Frequency Signature Beats Sorting for Keying Sorting each string as key costs O(k log k) per string. Character frequency array is O(k) and creates same signature for anagrams. Fixed 26-element array converted to tuple serves as hashable HashMap key — cleaner, faster grouping. Frequency as Key: Character counts uniquely identify anagram groups. Tuple conversion enables using array as dict key (lists aren't hashable). This pattern applies to document clustering, duplicate detection. Time: O(n × k) vs O(n × k log k) with sorting | Space: O(n × k) #FrequencySignature #HashMap #Anagrams #KeyOptimization #Python #AlgorithmDesign #SoftwareEngineering

Subsets: Backtracking with Include/Exclude Decision Tree Generate all 2^n subsets via recursive binary choices — include current element or skip. Base case: processed all elements, save current subset. Backtracking pattern: modify state, recurse, undo modification. Backtracking Pattern: Modify shared state, explore branch, restore state before exploring alternate branch. This template applies to permutations, combinations, constraint satisfaction problems. Time: O(2^n) | Space: O(n) recursion depth #Backtracking #Subsets #DecisionTree #StateRestoration #Recursion #Python #AlgorithmDesign #SoftwareEngineering

Day 254 of #365DaysOfCode Solved Check if There is a Valid Path in a Grid using a DFS-based traversal with directional constraints. Each cell type defines allowed movement directions, and transitions are validated by ensuring bidirectional connectivity between adjacent cells. The traversal explores only feasible paths while maintaining a visited structure to avoid cycles. The solution runs in O(n × m) time and emphasizes careful handling of constrained graph traversal. Continuing to strengthen understanding of grid-based graphs and state validation. #365DaysOfCode #Day254 #DSA #LeetCode #Python #Algorithms #DFS #Graphs #ProblemSolving #Consistency

✅ Day 19 of #DSAPrep > Problem: Quick Sort > Concept: Divide and Conquer (Pivot & Partition) Learned Quick Sort, where an element is chosen as a pivot and the array is partitioned such that smaller elements are placed on the left and larger elements on the right. > Key Idea: - Choose a pivot element - Partition the array around the pivot - Recursively apply the same process on left and right subarrays > Time Complexity: O(n log n) > Space Complexity: O(log n) #DSAPrep #Algorithms #Python #Sorting #ProblemSolving #CodingJourney #DivideAndConquer

🚗 Built an Automatic Number Plate Recognition (ANPR) System! Excited to share my recent project where I developed a real-time ANPR system using YOLOv8 and EasyOCR. 🔍 Key Features: • Real-time number plate detection • OCR-based text extraction • Custom dataset creation This project helped me strengthen my skills in Computer Vision and Machine Learning. 🔗 GitHub Repository: https://lnkd.in/g_5r5wcW #MachineLearning #ComputerVision #YOLO #Python #DataScience

How can you evaluate an AI model's robustness before real-world failures occur? In this webinar, we’ll demonstrate how to use the open source Natural Robustness Toolkit (NRTK) to create reproducible workflows for testing model performance. You’ll learn how to: ✅ Install and configure NRTK in Python ✅ Apply perturbations to expand existing datasets ✅ Design parameter sweeps to measure performance degradation ✅ Evaluate models under simulated operational conditions 📅 April 15, 2026 | 12–1 PM 👉 Register here: https://ow.ly/Ncnr50YBmK7 #AIResearch #MachineLearning #ModelValidation #NRTK #Python

📊 Day 6 | K-Nearest Neighbors (KNN) 🤝📍 Today, I learned about K-Nearest Neighbors (KNN), a simple and intuitive Machine Learning algorithm. KNN works on the idea of distance — it classifies a data point based on the majority class of its nearest neighbors. 📌 In simple terms: “Similar data points are close to each other.” Example: ✔ Recommending products ✔ Classifying customers To understand this, I implemented KNN using Python and observed how it predicts based on nearby data points 💻 KNN is simple but powerful for many classification problems. #MachineLearning #KNN #DataScience #LearningInPublic #Python

I was building the 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 and I encountered compatibility issue for installing the libraries. Here I got to know the importance of the 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 in 𝗣𝘆𝘁𝗵𝗼𝗻 to isolate the code/libraries for a new Project to avoid the compatibility issues. Also I had to install the libraries from the backend on Terminal which were not working from the 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸 Please refer my 𝐁𝐥𝐨𝐠 for details of issues encountered and the steps I followed to solve the same :-  https://lnkd.in/drmifuGp #Python #MachineLearning #AI #VirtualEnviornments #LinearRegression