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How to Find the Angle Between Two Planes Given by Their Equations | MOA Lesson 25
Hey math fans!
Welcome to Math Olympiad Academy,
Lesson 25 is now live on MOA:
By the end of this video, students will be able to:
Identify the normal vector for each plane from its equation
Use the dot product to compute angles between plane
Determine when planes are perpendicular or form acute angles
Apply vector reasoning systematically in 3D geometry
Approach advanced spatial problems with confidence and structure
Watch the complete step-by-step lesson for free on YouTube by searching “MOA Lesson 25”.
or access it directly via the link below:
We sincerely thank our subscribers and all viewers for your continued support.
The MOA Team
AI는 숫자로 세상을 이해한다! ChatGPT가 '커피숍 추천해줘'라고 물으면 왜 "아메리카노가 맛있는 카페"를 척척 찾아낼까? 비밀은 바로 벡터 데이터베이스(Vector DB)이다. 전통 데이터베이스가 '정확한 일치'를 찾는다면, 벡터 DB는 '의미가 비슷한' 데이터를 찾아주는 혁신적인 기술이다. 벡터 검색, 임베딩(Embedding), 코사인 유사도, 유클리드 거리, 그리고 HNSW, IVF 같은 인덱싱 알고리즘까지! 관리형 Pinecone부터 대규모 Milvus, 고성능 Qdrant까지 다양한 벡터 DB의 차이도 소개한다. AI의 필수 도구인 벡터 DB가 어떻게 추천 시스템, 이상 탐지, RAG(검색 증강 생성)에 활용되는지 알아보자. 기술의 미래에 한 걸음 더 가까워지는 기회! 자세한 내용은 여기를 확인해보자: https://doyouknow.kr/436/vector-database-guide-embedding-similarity-search-hnsw-ivf-pinecone-milvus/ 여러분은 AI가 추천하는 커피숍, 어떤 기준으로 선택하고 있나요? 의견 공유도 환영합니다! ☕✨ #벡터데이터베이스 #AI #임베딩 #추천시스템 #유사도검색 Read the full article
Vectors - Explained
A simple introduction to vectors covering: vector definition and representation, vector magnitude and direction, addition and subtraction of vectors, components of a vector, unit vectors, and the dot product of two vectors.
An introduction to vectors convering vector definition and representation, vector magnitude, scalar multiplication, vectors addition and sub
한 점이 삼각형 내부에 있는지를 체크하기
한 점이 삼각형 내부에 있는지를 체크하기
평면좌표상의 임의의 점 P(px, py) 가 세 점 A(ax, ay), B(bx, by), C(cx, cy)로 이루어진 삼각형 내부에 있는지 외부에 있는지를 판단하는 코드를 작성해보자. 이거 마치 무슨 고등학교 수학문제 같은데, 사실 고등학교 수학 문제 맞다…
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MIT has a new chip to make AI faster and more efficient on smartphones
MIT has a new chip to make AI faster and more efficient on smartphones
Just one day after MIT revealed that some of its researchers had created a super low-power chip to handle encryption, the institute is back with a neural network chip that reduces power consumption by 95 percent. This feature makes them ideal for battery-powered gadgets like mobile phones and tablets to take advantage of more complex neural networking systems. Neural networks are made up of lots…
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