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MIT 6.006 Introduction to Algorithms. nodes, pointers, and parent relationships in binary trees.

49,706 просмотров • 5 месяцев назад •via X (Twitter)

Комментарии: 11

Фото профиля Andrea Hatfield🩷
Andrea Hatfield🩷5 месяцев назад

@AmX_ZaxTr Binary trees in MIT 6.006: where nodes, pointers, and parent relationships are still the DNA of efficient computing — and the silent foundation of everything AI does today. Timeless class. 🌳

Фото профиля Angel
Angel5 месяцев назад

Remember when people had to do those to land any software job?. Reversing binary trees and responding to trick questions for a FAANG job

Фото профиля ZaxtR
ZaxtR5 месяцев назад

Where did everyone go? 🤨🤷‍♂️

Фото профиля David🚀
David🚀5 месяцев назад

Truth, goodness, and beauty

Фото профиля jansel
jansel5 месяцев назад

BM3RYbkZZfC7KPPGxdhC681N4a41fZmrDp2vLzggpump

Фото профиля Mkaey🔺
Mkaey🔺5 месяцев назад

Binary trees looked confusing at first, but once the parent and pointer relationships start clicking, everything makes way more sense

Фото профиля Mario
Mario5 месяцев назад

I remember good at university, C++ by Borland. Exciting.

Фото профиля i need YHWH
i need YHWH5 месяцев назад

Alpha

Фото профиля desiVoidStar
desiVoidStar5 месяцев назад

I will stick my pointer in mighty b00b anyday 😂

Фото профиля Navi
Navi5 месяцев назад

💗

Фото профиля Caustic Dave 🇺🇸
Caustic Dave 🇺🇸5 месяцев назад

Brings back memories…

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Holy shit… someone just made DSA finally click. Not static notes Not boring pseudocode Not guessing what happens in memory Real data structures — animating step-by-step — visually. It’s called Data Structure Visualizations and it lets you watch algorithms run in real time. Here’s why this is different: Instead of dumping theory, it shows execution live • nodes getting inserted • trees rotating • pointers moving • queues filling • stacks popping • graphs traversing • heaps rebalancing You literally see algorithms think. Everything is interactive: • Binary Search Trees • AVL Trees (with rotations) • Red-Black Trees • Heaps & Priority Queues • Graph BFS / DFS • Dijkstra & MST • Hash Tables • Tries • Sorting (Quick, Merge, Heap…) • Dynamic Programming No black box. Just input → steps → result Watch in real time: • AVL rotations balancing themselves • BFS exploring layer by layer • DFS diving deep then backtracking • Dijkstra relaxing edges step-by-step • Quick sort partition visually • Heap forming after each insert • Hash collisions resolving live This solves the biggest DSA problem: Most resources teach code → memorize → hope it works This shows input → execution → visualization → understanding Which means you finally understand: • why AVL rotates • how heap property maintains • how BFS differs from DFS • how Dijkstra actually updates distances • what happens during rehashing • how quicksort partitions • how trees rebalance Even better: You control everything Change values Insert nodes Run step-by-step Pause execution Replay algorithms Learning DSA becomes interactive Not passive Not confusing Not theoretical Just… visible. Perfect for: • DSA beginners • interview prep • visual learners • CS students • LeetCode prep • teaching algorithms • debugging understanding This is the kind of resource that makes trees, graphs, and sorting finally click. Link: We’re moving from reading DSA → watching DSA execute And once you can see algorithms… you stop memorizing and start understanding.

Suryansh Tiwari

14,627 просмотров • 7 месяцев назад

25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on a sorted list 3. Bubble sort to sort a list 4. Merge sort will also sort lists 5. Quicksort to sort the list and do it fast 6. Dijkstra to find the shortest path in a graph 7. Breadth-first Search (BFS) for trees or graphs 8. Depth-first search (DFS) for trees or graphs 9. Huffman for doing data compression 10. Anything related to dynamic programming Learning about algorithms is like getting tattoos: you never have enough. Here are another 5 algorithms that will help you go beyond the basics: 11. Kruskal for the finding minimum spanning tree 12. Floyd Warshall, shortest paths in a graph 13. Union Find to detect cycles in a graph 14. Bellman-Ford, shortest path in a graph 15. Lee for finding the shortest path in a maze If you are serious about this topic, I recommend learning about algorithms' space and time complexity. People usually refer to this topic as "Big O" notation. You should build a good intuition about the performance of different algorithms and learn how to evaluate them. Machine Learning will rule the next 50 years, so the next 10 algorithms you can't ignore are the following: 16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Bayes' theorem 20. k-Nearest Neighbors (kNN) 21. Every algorithm related to neural networks 22. K-means 23. Random forest 24. Gradient boosting algorithms 25. Any dimensionality reduction algorithm (PCA, for instance) There are many more mind-blowing algorithms! I haven't found a better way to understand how computers work from a first-principles point of view than reading about different algorithms. Take a look at the attached video.

Santiago

274,092 просмотров • 2 лет назад