1.4M
INGraham scan algorithm animated!
Full video in the YouTube channel
#algorithms #computerscience #programming
@inside.code
#Lll Algorithm Examples
世界中の人々によるLll Algorithm Examplesに関する件のリール動画を視聴。
ログインせずに匿名で視聴。
トレンドリール
(12)
5.0K
CO⚡️DSA in Action → Greedy Algorithm
Watch how this strategy builds the minimum spanning tree step-by-step!
Instead of looking at all possibilities, it picks the locally best option at every step…
…and still ends up with an optimal solution 💡
Would you use Greedy or Dynamic Programming for this problem? 🤔
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#DSA #GreedyAlgorithm #Algorithms #DataStructures #CodingLife #ProgrammerLife #CodeLoopa #TechCreators #LearnToCode #ComputerScience #ProblemSolving #CompetitiveProgramming #CodingCommunity #TechContent #CodeDaily
#codeloopa #trending #viral #meme #programminghumor #techreels #computerscience #programmingmemes
@codeloopaa
25.7K
SAWhat is an algorithm? 🤯
It’s not magic. It’s just step-by-step logic.
Search, sort, recommend… this is what runs everything.
Wether you are CS student, a junior or a senior engineer, learning algorithms + DSA is how you learn to actually solve real-world problems. 🚀
@sayed.developer
3.7K
CAerror diffusion algorithm on a grayscale image with increasing resolutions
this visualizes how the Floyd Steinberg dithering algorithm redistributes the brightness of the image so that each pixel is either black or white
#generativeart #mathart #creativecoding #algorithmicart #algorithm #codevisual #codeart #math #datavisual #imageprocessing #monochrome
@canvas.51
9.7K
LASorting Algorithm. #sorting #pythonprogramming #learnpython #coding #dataanalytics
@laskentatechltd
44.1K
ONBrute Force Algorithm Visualized
@onjsdev
39.8K
KRSorting algorithms at work. Comment your favorite
#sortinganimation #Sorting #python #coding #programming #codinglife #coding
@kreggscode
6.4K
COSelection sort algorithms
#fyp #foryou #foryoupage #programming #coding #dsa #datastructures #algorithm #javascript #codinglife #codingpics #100daysofcode #python
@code_within
13.3K
GEThe Langevin algorithm is used in Diffusion models, which are generative models that are used for image, video, and audio generation. Diffusion models work by learning to reverse a gradual noise-corruption process that transforms data into pure random noise.
During training, these models observe clean data (like images) being progressively corrupted by noise, then learn to perform the reverse process—starting from pure noise and gradually removing it step by step to recover realistic data samples.
This denoising process is where the Langevin algorithm becomes crucial: each reverse step combines a learned prediction of how to reduce the noise (the drift term pointing toward higher probability regions of real data) with additional controlled randomness (the stochastic term) that prevents the sampling process from getting stuck in local optima or producing the same outputs every time.
Even though we’re following gradients toward high-probability data regions, we maintain enough randomness to have enough diversity of the learned distribution. This is exactly why diffusion models can generate varied, high-quality samples.
C: deepia
@getintoai
39.6K
ADWhat this 👀
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Follow @advicefromtraders for daily trading & finance insights!
#MIT #Algorithm #TechSimplified
#SmartThinking #CodeGenius
@advicefromtraders
930.7K
COLeetCode Daily - Day 36 : Trapping Rain Water (Part 1)
We are given an array that represents the height of bars.
Our task is to calculate how much water can be trapped between these bars after it rains.
But before solving this question, we need to know when does water actually get trapped?
Let’s break it down.
Water can only get trapped if a shorter bar is surrounded by taller bars on both sides.
And to calculate the water trapped at each index, we need to know two things:
👉 The tallest bar to the left of that index, call it leftMax
👉 The tallest bar to the right of that index, call it rightMax
Once we know both, the formula is simple:
water[i] = min(leftMax, rightMax) - height[i]
Let’s say:
leftMax = 4, rightMax = 6, and height[i] = 2
Then water trapped at index i will be:
min(4, 6) - 2 = 2 units
Pretty intuitive, right?
But here’s the catch…
How do we efficiently find leftMax and rightMax for every index?
In the next part, we’ll explore two powerful approaches:
✅ One brute-force way (just to understand the logic clearly)
✅ And one optimized using prefix arrays
So make sure to save this post, and stay tuned for Part 2 where we dive into code and logic.
#dsa #java #leetcode #datastructure #javaprogramming #logicbuilding #codingwithyash #algorithms #leetcodesolution #interviewprep
@codingwithyash
2.7K
ACAlgorithm - one of the most mispronounced words in tech and business.
Clear pronunciation builds credibility.
Want to sound clearer and more confident at work?
🎯 Book your free Clarity Call - link in bio.
@accentarrow
✨ #Lll Algorithm Examples発見ガイド
Instagramには#Lll Algorithm Examplesの下にthousands of件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
#Lll Algorithm Examplesは現在、Instagram で最も注目を集めているトレンドの1つです。このカテゴリーにはthousands of以上の投稿があり、@inside.code, @codingwithyash and @onjsdevのようなクリエイターがバイラルコンテンツでリードしています。Pictameでこれらの人気動画を匿名で閲覧できます。
#Lll Algorithm Examplesで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
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#Lll Algorithm Examplesについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Lll Algorithm Examplesのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
パフォーマンス分析
12リールの分析
💡 トップ投稿は平均594.8K回の再生（平均の2.9倍）
週3-5回、活動時間に定期的に投稿
コンテンツ作成のヒントと戦略
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