franztao

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Linear_Regression_02

2022年10月

数据集$D={(x_1, y_1), (x_2, y_2), \cdots, (x_N, y_N)}$，其中$x_i\in\mathbb{R}^{p}$，$y_i\in\mathbb{R}$，$i=1, \ 2,\cdots,\ N$。 数据矩阵为：(这样可以保证每一行为一个数据点) \[\begin{equation} X=(x_1, x_2, \cdots, x_N)^T= ...

Posted by franztao on October 14, 2019

Linear_Regression_01

2022年10月

数据集$D={(x_1, y_1), (x_2, y_2), \cdots, (x_N, y_N)}$，其中$x_i\in\mathbb{R}^{p}$，$y_i\in\mathbb{R}$，$i=1, \ 2,\cdots,\ N$。 数据矩阵为：(这样可以保证每一行为一个数据点) \[\begin{equation} X=(x_1, x_2, \cdots, x_N)^T= ...

Posted by franztao on October 12, 2019

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Linear Regression Math Basis Exponential Family Distribution Classification Bayes Background Inference Conclusion Feedforward Neural Network Support Vector Machine Margin and Solution Weak Duality Kernel Method of Function Probability Graph Markov Variational Algorithm Belief Process Introduction Gaussian Expectation Maximization Mixture Model Chain Monte Carlo Sampling Hidden Learning Kalman Filter Conditional Random Field Boltzmann Deep Generative Adversarial Reinforcement Blog docker cuda
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