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Lecture 1: Algorithmic Thinking, Peak Finding
53:22
YouTubeMIT OpenCourseWare
Lecture 1: Algorithmic Thinking, Peak Finding
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Srini Devadas License: Creative Commons BY-NC-SA ...
5.9M viewsJan 14, 2013
Algorithm Design
The $1M Problem, Solved by a Student πŸ§‘β€πŸ’»πŸ’‘
1:00
The $1M Problem, Solved by a Student πŸ§‘β€πŸ’»πŸ’‘
YouTubemdscae
3.1M views1 month ago
How the TikTok algorithm works - Explained by the CEO
1:11
How the TikTok algorithm works - Explained by the CEO
YouTubeDuffin Whatever?
44.6K viewsApr 24, 2023
This comparison of two algorithms (Dijkstra’s and A*) was created by @madorskyart. ❗ Follow @science.mp4 for more science videos.❗ The second algorithm is Dijkstra’s algorithm, which is an algorithm for finding the shortest paths between nodes in a weighted graph, which may represent, for example, a road network. It was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later The second algorithm is A* (pronounced β€œA-star”), which is a graph traversal and pathfi
0:37
This comparison of two algorithms (Dijkstra’s and A*) was created by @madorskyart. ❗ Follow @science.mp4 for more science videos.❗ The second algorithm is Dijkstra’s algorithm, which is an algorithm for finding the shortest paths between nodes in a weighted graph, which may represent, for example, a road network. It was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later The second algorithm is A* (pronounced β€œA-star”), which is a graph traversal and pathfi
FacebookScience.mp4
427.4K views7 months ago
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πŸ“š 3 Core Math Skills You MUST Learn for Machine Learning! 🀯 Algebra πŸ’‘ | Statistics & Probability πŸ“Š | Calculus πŸ“ˆ Unlock the foundation behind AI, data science & ML fundamentals πŸš€ (Explained for beginners) πŸ‘‡ πŸ”‘ Why this matters: Every ML algorithm uses these math fields β€” from linear models to neural networks! πŸ” 🎯 Learn: ➀ Algebra – equations & vectors for models ➀ Stats & Probability – understanding data patterns ➀ Calculus – optimization & gradients πŸ‘¨β€πŸ« Perfect for students, beginners
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πŸ“š 3 Core Math Skills You MUST Learn for Machine Learning! 🀯 Algebra πŸ’‘ | Statistics & Probability πŸ“Š | Calculus πŸ“ˆ Unlock the foundation behind AI, data science & ML fundamentals πŸš€ (Explained for beginners) πŸ‘‡ πŸ”‘ Why this matters: Every ML algorithm uses these math fields β€” from linear models to neural networks! πŸ” 🎯 Learn: ➀ Algebra – equations & vectors for models ➀ Stats & Probability – understanding data patterns ➀ Calculus – optimization & gradients πŸ‘¨β€πŸ« Perfect for students, beginners
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