贝叶斯网络结构学习1
Baysian Network Structure Learning
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基本的蒙特卡洛采样方法
Basic Monte Carlo sampling method
基本的采样方法
Basic sampling method
beta分布,dirichlet分布与贝叶斯参数估计简介
beta, dirichlet dirichlet and baysian estimation introduction
二项分布,负二项分布简介
binomial pascal poisson introduction
EM 算法简介
EM algorithm introduction
吉布斯采样方法
gibbs sampling method
Gradient Boosting Decision Tree 简介
gradient-boosting introduction
马尔科夫蒙特卡洛采样方法
mcmc sampling method
无模型信息控制
model free control
无模型信息预测
Monte Carlo learning and Temporal Difference Learning
动差生成函数
moment generating function
NNLM introduction
NNLM introduction
主成分分析简介
PCA introduction
泊松分布,指数分布简介
poisson and exponential distribution introduction
策略梯度
policy gradient in reinforcement learning
随机森林与Adaptive Boosted决策树简介
random-forest-and-adaboost-decision-tree introduction
Stochastic Neighbor Embedding非线性降维简介
An introduction to SNE
softmax 简介
softmax introduction
t-sne简介
t-sne introduction
价值函数的近似表示
value function approxmation in reinforcement learning
word2vec简介
word2vec introduction
pets
portfolio
Foo Bar Identity
Foo Bar design system including logo mark, website design, and branding applications.