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Math for ML
Math / Math for ML
Math / Math for ML
Mathematics for Machine Learning 6.3장: 합/곱의 규칙, 베이즈 정리
2023. 02. 04
Probability and Distributions - Sum Rule, Product Rule, and Bayes' Theorem
Math / Math for ML
Mathematics for Machine Learning 6.2장: 확률과 분포 - 이산/연속 확률
2023. 01. 31
Probability and Distributions - Discrete and Continuous Probabilities
Math / Math for ML
Mathematics for Machine Learning 7.2장: 최적화 - 제한된 최적화와 라그랑주 승수
2023. 01. 30
Continuous Optimization - Constrained Optimization and Lagrange Multipliers
Math / Math for ML
Mathematics for Machine Learning 7.1장: 최적화 - 경사하강법을 이용한 최적화
2023. 01. 29
Continuous Optimization - Optimization Using Gradient Descent
Math / Math for ML
Mathematics for Machine Learning 5.8장: 벡터 연산 - 선형화(선형근사)와 다변수 테일러 급수
2023. 01. 28
Vector Calculus - Linearization and Multivariate Taylor Series
Math / Math for ML
Mathematics for Machine Learning 5.7장: 벡터 연산 - 고차 미분 (헤시안 행렬)
2023. 01. 28
Vector Calculus - Higher-Order Derivatives (Hessian)
Math / Math for ML
Mathematics for Machine Learning 5.6장: 벡터 연산 - 오차역전파와 자동 미분
2023. 01. 27
Vector Calculus - Backpropagation and Automatic Differentiation
Math / Math for ML
Mathematics for Machine Learning 5.4장: 벡터 연산 - 행렬의 그래디언트
2023. 01. 26
Vector Calculus - Gradients of Matrices
Math / Math for ML
Mathematics for Machine Learning 5.3장: 벡터 연산 - 벡터값 함수의 그래디언트
2023. 01. 18
Vector Calculus - Gradients of Vector-Valued Functions
Math / Math for ML
Mathematics for Machine Learning 5.2장: 벡터 연산 - 편미분과 그래디언트
2023. 01. 16
Vector Calculus - Partial Differentiation and Gradients
Math / Math for ML
Mathematics for Machine Learning 5.1장: 벡터 연산 - 미분
2023. 01. 16
Vector Calculus - Differentiation of Univariate Functions
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