NeetCode

𝟮𝟰 𝗖𝗼𝗱𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗧𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 (𝟮𝟬𝟮𝟱) (Nail your interviews) You shouldn't have to guess your way to your dream tech job. Here are 24 coding interview patterns to ace your interviews (details in the attached document) 👇 𝟭) 𝗧𝘄𝗼 𝗣𝗼𝗶𝗻𝘁𝗲𝗿𝘀 𝟮) 𝗣𝗿𝗲𝗳𝗶𝘅 𝗦𝘂𝗺𝘀 𝟯) 𝗦𝗹𝗶𝗱𝗶𝗻𝗴 𝗪𝗶𝗻𝗱𝗼𝘄 𝟰) 𝗞𝗮𝗱𝗮𝗻𝗲’𝘀 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝟱) 𝗙𝗶𝗻𝗱 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝘀𝘂𝗯𝗮𝗿𝗿𝗮𝘆𝘀 𝟲) 𝗙𝗮𝘀𝘁 𝗮𝗻𝗱 𝗦𝗹𝗼𝘄 𝗣𝗼𝗶𝗻𝘁𝗲𝗿𝘀 𝟳) 𝗗𝗲𝗽𝘁𝗵-𝗙𝗶𝗿𝘀𝘁 𝗦𝗲𝗮𝗿𝗰𝗵 𝟴) 𝗕𝗿𝗲𝗮𝗱𝘁𝗵-𝗙𝗶𝗿𝘀𝘁 𝗦𝗲𝗮𝗿𝗰𝗵 𝟵) 𝗠𝗮𝘁𝗿𝗶𝘅 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹 𝟭𝟬) 𝗔𝗱𝗷𝗮𝗰𝗲𝗻𝗰𝘆 𝗟𝗶𝘀𝘁 𝗕𝗙𝗦 / 𝗗𝗙𝗦 𝟭𝟭) 𝗧𝘄𝗼 𝗛𝗲𝗮𝗽𝘀 𝟭𝟮) 𝗕𝗶𝗻𝗮𝗿𝘆 𝗦𝗲𝗮𝗿𝗰𝗵 (𝗠𝗼𝗱𝗶𝗳𝗶𝗲𝗱) 𝟭𝟯) 𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗦𝗼𝗿𝘁 𝟭𝟰) 𝗧𝗼𝗽 𝗞 𝟭𝟱) 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁 𝗥𝗲𝘃𝗲𝗿𝘀𝗮𝗹 𝟭𝟲) 𝗣𝗲𝗿𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝟭𝟳) 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 𝟭𝟴) 𝗧𝗿𝗲𝗲 𝗠𝗮𝘇𝗲 (𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴) 𝟭𝟵) 𝗟𝗼𝗻𝗴𝗲𝘀𝘁 𝗖𝗼𝗺𝗺𝗼𝗻 𝗦𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝟮𝟬) 𝗠𝗼𝗻𝗼𝘁𝗼𝗻𝗶𝗰 𝗦𝘁𝗮𝗰𝗸 𝟮𝟭) 𝗠𝗲𝗺𝗼𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝟮𝟮) 𝗧𝗮𝗯𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝟮𝟯) 𝗠𝘂𝗹𝘁𝗶-𝗦𝗼𝘂𝗿𝗰𝗲 𝗕𝗙𝗦 𝟮𝟰) 𝗠𝗲𝗿𝗴𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗮𝗹𝘀 --- Start practicing with NeetCode 250-> https://lnkd.in/eA5EqprJ

LeetCode has 3,715 problems but you only need 18 topics to get good: 1) Arrays & Hashing 2) Two Pointers 3) Stack 4) Binary Search 5) Sliding Window 6) Linked List 7) Trees 8) Tries 9) Backtracking 10) Heaps/Priority Queues 11) Intervals 12) Greedy 13) Graphs 14) 1-D DP 15) Advanced Graphs 16) 2-D DP 17) Bit Manipulation 18) Math and Geometry Most people think that if they memorize enough questions, they'll have a better chance at passing their technical interviews. But in reality, you may just be gambling away a real shot at getting the tech job of your dreams. Focus on patterns, and understanding WHY they work. --- Start solving these problems today -> https://lnkd.in/dyqwXaZr

𝟲 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗜𝗻 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 It may seem like algorithms involved in dynamic programming and sorting are only useful when passing Online Assessments and on-sites. Well... let's prove that these algorithms are actually useful in the real world: 1) Dijkstra's Algorithm Google Maps evaluates all possible paths and calculates the shortest one based on distance, traffic, and road conditions. It uses a modified version of the Dijkstra's algorithm. 2) Binary Search The git bisect command uses binary search to find which commit in your project’s history introduced a bug. 3) Sorting Algorithms Programming languages like Java, Python and C++ use a hybrid of sorting algorithms. 4) Dynamic Programming Autocorrect on your phone calculates the "edit distance" between what you typed and dictionary words to suggest corrections, using dynamic programming under the hood. 5) Breadth-First Search (BFS) LinkedIn's "People You May Know" feature explores your network level by level to suggest relevant connections. 6) Topological Sort Getting dressed is a perfect example of topological sorting. For example, you must wear socks before you can wear your shoes. --- Become A NeetCoder → neetcode.io

If you want to truly understand data structures, you must implement these 9 from scratch: 1) Design Dynamic Array (Resizable Array) https://lnkd.in/ez6WxN-k 2) Design Singly Linked List https://lnkd.in/eDUdkd5r 3) Design Double-ended Queue https://lnkd.in/eVfAr6aV 4) Design Binary Search Tree https://lnkd.in/eBMvXrBJ 5) Design Hash Table https://lnkd.in/efH5kyJ2 6) Design Heap https://lnkd.in/eEYJ_bP7 7) Design Graph https://lnkd.in/eNrbQiG8 8) Design Disjoint Set (Union-Find) https://lnkd.in/eXZugJea 9) Design Segment Tree https://lnkd.in/e6anMBFn --- Become a NeetCoder → neetcode.io

If you want to truly understand data structures, you must implement these 9 from scratch: 1) Design Dynamic Array (Resizable Array) https://lnkd.in/ez6WxN-k 2) Design Singly Linked List https://lnkd.in/eDUdkd5r 3) Design Double-ended Queue https://lnkd.in/eVfAr6aV 4) Design Binary Search Tree https://lnkd.in/eBMvXrBJ 5) Design Hash Table https://lnkd.in/efH5kyJ2 6) Design Heap https://lnkd.in/eEYJ_bP7 7) Design Graph https://lnkd.in/eNrbQiG8 8) Design Disjoint Set (Union-Find) https://lnkd.in/eXZugJea 9) Design Segment Tree https://lnkd.in/e6anMBFn --- Become a NeetCoder → neetcode.io

𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 - 𝗣𝗮𝘁𝗵 𝗙𝗶𝗻𝗱𝗲𝗿 Backtracking is a brute force way of exploring all potential solutions to a problem. It builds paths incrementally and discards invalid ones, making it perfect for solving pathfinding problems in trees. Using DFS, we can count valid paths to leaf nodes based on specific conditions. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Track Path: Use a list to store the current path's node values 2. Add Nodes: Include the current node if it satisfies the condition (e.g., non-null, non-zero) 3. Recursive Exploration: Search left and right subtrees for valid paths 4. Backtrack: Remove the current node from the path list when returning from a branch, avoiding overlap between paths 5. Validation: Finally, return true or false indicating whether a valid path exists 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 1. Path Sum: Check if a path meets a sum condition 🟢 2. Subsets: Generate all subsets of an input array 🟡 https://lnkd.in/e9-fi6SB 3. Combination Sum: Find combinations that achieve a target sum 🟡 https://lnkd.in/e42mAKmz 𝗧𝗶𝗺𝗲 𝗮𝗻𝗱 𝗦𝗽𝗮𝗰𝗲 Time: O(n) Space: O(h), where h is the height of the tree --- Become a NeetCoder -> neetcode.io

𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 - 𝗣𝗮𝘁𝗵 𝗙𝗶𝗻𝗱𝗲𝗿 Backtracking is a brute force way of exploring all potential solutions to a problem. It builds paths incrementally and discards invalid ones, making it perfect for solving pathfinding problems in trees. Using DFS, we can count valid paths to leaf nodes based on specific conditions. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Track Path: Use a list to store the current path's node values 2. Add Nodes: Include the current node if it satisfies the condition (e.g., non-null, non-zero) 3. Recursive Exploration: Search left and right subtrees for valid paths 4. Backtrack: Remove the current node from the path list when returning from a branch, avoiding overlap between paths 5. Validation: Finally, return true or false indicating whether a valid path exists 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 1. Path Sum: Check if a path meets a sum condition 🟢 2. Subsets: Generate all subsets of an input array 🟡 https://lnkd.in/e9-fi6SB 3. Combination Sum: Find combinations that achieve a target sum 🟡 https://lnkd.in/e42mAKmz 𝗧𝗶𝗺𝗲 𝗮𝗻𝗱 𝗦𝗽𝗮𝗰𝗲 Time: O(n) Space: O(h), where h is the height of the tree --- Become a NeetCoder -> neetcode.io

𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗧𝗼𝗽-𝗗𝗼𝘄𝗻 𝗩𝗦. 𝗕𝗼𝘁𝘁𝗼𝗺-𝗨𝗽 What's the difference between the top-down and the bottom-up approach when it comes to dynamic programming? (Spoiler: It's not the direction in which the grid is traversed) 𝗧𝗼𝗽-𝗗𝗼𝘄𝗻: Starts with the main problem and breaks it down into smaller subproblems recursively, storing results in a cache. 𝗕𝗼𝘁𝘁𝗼𝗺-𝗨𝗽: Builds the solution iteratively from the base case up to the final result. Let's see how these two approaches work when we are asked to find the number of unique paths from the top left to the bottom right of the grid. 1/ Top Down Approach (Memoization) We can avoid recalculating the same subproblem by storing the calculated values inside of a cache. This way, we can just check the cache at each calculation to see if we've already calculated the value. This gives us a time and space of O(n * m), where n = number of rows and m = number of columns. But we can do better. 2/ Bottom Up Approach (Tabulation) Because we are only ever using values from the current and the previous row, we actually don't need to store the entire grid. All we need are two arrays, one for prevRow and one for curRow. This iterative solution helps us bring down the complexity to O(m) space, where m = number of columns. 💡The direction in which we traverse the grid can be similar, but the process of how and when you store intermediate results (via recursion or iteration) is where the distinction lies. Code Yourself 👇 1/ Unique Paths 🟡 https://lnkd.in/eexp_xJ2 2/ Longest Common Subsequence (LCS) 🟡 https://lnkd.in/er5PB-Hc --- Preparing for coding interviews? Check out neetcode.io