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- neural network
- Jacobian Matrix
- 김성훈 교수님
- convex optimization
- 데이터 분석
- unity
- 판다스
- Deep Learning
- list
- statistics
- 모두를 위한 RL
- 사이킷런
- 논문
- 유니티
- ML-Agent
- Linear algebra
- paper
- 강화학습
- rl
- optimization
- reinforcement learning
- 딥러닝
- 리스트
- machine learning
- Python Programming
- Laplacian
- Series
- David Silver
- pandas
- Hessian Matrix
목록Optimization/Stanford Lecture (3)
RL Researcher
Convex sets affine and convex sets some important examples operations that preserve convexity generalized inequalities separating and supporting hyperplanes dual cones and generalized inequalities Affine set 아래에 그림의 $x_{1}$과 $x_{2}$를 통하는 모든 점들을 Affine set이라고 부름. $$x = \theta x_{1}+(1-\theta)x_{2}\ \ \ \ \ (\theta \in R)$$ affine set : set에서 두 개의 다른 점을 통과하는 선을 포함함. example : linear equations $\le..
Question 1 The symbol x⋆ usually denotes a feasible point the optimal value of the problem a solution Question 2 Least squares is a special case of convex optimization. true false Question 3 Almost any problem you'd like to solve in practice is convex. true false Question 4 Convex optimization problems are attractive because they always have a unique solution. true false Question 5 In device siz..
Introduction mathematical optimization (수학적 최적화) least-squares and linear programming (최소 제곱과 선형 계획법) convex optimization (볼록 최적화) example (예시) course goals and topics (코스 목표 및 주제) nonlinear optimization (비선형 최적화) brief history of convex optimization (볼록 최적화의 간략한 역사) Mathematical Optimization Optimization Problem의 정의 : $x = (x_{1}, ..., x_{n})$ : Optimization variables(최적화 변수), Decision variable..