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    "# 简介\n",
    "\n",
    "> 作者：hschen  \n",
    "> QQ：357033150  \n",
    "> 邮箱：hschen0712@gmail.com\n",
    "\n",
    "此笔记主要总结自一些论文、书籍以及公开课，由于本人水平有限，笔记中难免会出现各种错误，欢迎指正。  \n",
    "由于Github渲染`.ipynb`文件较慢，可以用nbviewer加快渲染：[点此加速](http://nbviewer.jupyter.org/github/hschen0712/machine-learning-notes/blob/master/README.ipynb)\n",
    "\n",
    "\n",
    "## 目录\n",
    "\n",
    "\n",
    "1.公开课/读书笔记  \n",
    "- [徐亦达机器学习笔记](YidaXu-ML/)\n",
    "  - [采样算法系列1——基本采样算法](YidaXu-ML/sampling-methods-part1.ipynb)\n",
    "  - [采样算法系列2——MCMC](YidaXu-ML/sampling-methods-part2.ipynb)\n",
    "  - [EM算法](YidaXu-ML/EM-review.ipynb)\n",
    "  - [变分推断](YidaXu-ML/variational-inference.ipynb)\n",
    "  - [高斯分布的变分推断](YidaXu-ML/variational-inference-for-gaussian-distribution.ipynb)\n",
    "  - [指数分布族](YidaXu-ML/exponential-family.ipynb)\n",
    "  - [指数分布族的变分推断](YidaXu-ML/exponential-family-variational-inference.ipynb)\n",
    "\n",
    "- [CS229课程笔记](CS229/)\n",
    "   - [广义线性模型](CS229/GLM.ipynb)\n",
    "   - [EM算法](CS229/EM.ipynb)\n",
    "   - [增强学习系列1](CS229/RL1.ipynb)\n",
    "   - [增强学习系列2](CS229/RL2.ipynb)\n",
    "\n",
    "- [台大机器学习基石笔记](ML-Foundation/)  \n",
    "   - [第一讲-学习问题](ML-Foundation/lecture-1.ipynb)\n",
    "- [PRML读书笔记](PRML/)\n",
    "   - [第一章 简介](PRML/Chap1-Introduction)\n",
    "      - [1.1 多项式曲线拟合](PRML/Chap1-Introduction/1.1-polynomial-curve-fitting.ipynb)\n",
    "      - [1.2 概率论回顾](PRML/Chap1-Introduction/1.2-probability-theory.ipynb)\n",
    "      - [总结-曲线拟合的三种参数估计方法](PRML/Chap1-Introduction/Summary-three-curve-fitting-approaches.ipynb)\n",
    "   - [第二章 概率分布](PRML/Chap2-Probability-Distributions)\n",
    "      - [2.1 二元变量](PRML/Chap2-Probability-Distributions/2.1-binary-variables.ipynb)\n",
    "      - [2.2 多元变量](PRML/Chap2-Probability-Distributions/2.2-multinomial-variables.ipynb)\n",
    "   - [第三章 线性回归模型](PRML/Chap3-Linear-Models-For-Regression)\n",
    "      - [3.1 线性基函数模型](PRML/Chap3-Linear-Models-For-Regression/3.1-linear-basis-function-models.ipynb)\n",
    "      - [总结-贝叶斯线性回归](PRML/Chap3-Linear-Models-For-Regression/summary-baysian-linear-regression.ipynb)\n",
    "\n",
    "2.[机器学习笔记](Machine-Learning/)\n",
    "- [xgboost笔记](Machine-Learning/xgboost-notes)\n",
    "  - [1. xgboost的安装](Machine-Learning/xgboost-notes/xgboost-note1.ipynb)\n",
    "- [softmax分类器](Machine-Learning/softmax-crossentropy-derivative.ipynb)\n",
    "- [用theano实现softmax分类器](Machine-Learning/implement-softmax-in-theano.ipynb)\n",
    "- [用SVD实现岭回归](Machine-Learning/svd-ridge-regression.ipynb)\n",
    "- [SVD系列1](Machine-Learning/svd1.ipynb)\n",
    "\n",
    "3.[NLP笔记](NLP/)\n",
    "- [LDA系列1——LDA简介](NLP/latent-dirichlet-allocation-1.ipynb)\n",
    "- [LDA系列2——Gibbs采样](NLP/latent-dirichlet-allocation-2.ipynb)\n",
    "- [朴素贝叶斯](NLP/naive-bayes.ipynb)\n",
    "\n",
    "\n",
    "4.[深度学习笔记](Deep-Learning/)\n",
    "- [theano笔记](Deep-Learning/theano-notes)\n",
    "  - [2. theano简单计算](Deep-Learning/theano-notes/part2-simple-computations.ipynb)\n",
    "  - [3. theano共享变量](Deep-Learning/theano-notes/part3-shared-variable.ipynb)\n",
    "  - [4. theano随机数](Deep-Learning/theano-notes/part4-random-number.ipynb)\n",
    "  - [6. theano的scan函数](Deep-Learning/theano-notes/part6-scan-function.ipynb)\n",
    "  - [7. theano的dimshuffle](Deep-Learning/theano-notes/part7-dimshuffle.ipynb)\n",
    "- [mxnet笔记](Deep-Learning/mxnet-notes)\n",
    "  - [1. Win10下安装MXNET](Deep-Learning/mxnet-notes/1-installation.ipynb)\n",
    "\n",
    "  - [2. MXNET符号API](Deep-Learning/mxnet-notes/2-mxnet-symbolic.ipynb)\n",
    "\n",
    "  - [mxnet中的运算符](Deep-Learning/mxnet-notes/operators-in-mxnet.ipynb)\n",
    "\n",
    "  - [mshadow表达式模板教程](Deep-Learning/mxnet-notes/mshadow-expression-template-tutorial.ipynb)\n",
    "\n",
    "- [keras笔记](Deep-Learning/keras-notes)\n",
    "  - [keras心得](Deep-Learning/keras-notes/keras-tips.ipynb)\n",
    "\n",
    "- [windows下安装caffe](Deep-Learning/install-caffe-in-windows.ipynb)\n",
    "- [BP算法矩阵形式推导](Deep-Learning/back-propagation-in-matrix-form.ipynb)\n",
    "- [随时间反向传播算法数学推导过程](Deep-Learning/back-propagation-through-time.ipynb)\n",
    "- [用numpy实现RNN](Deep-Learning/rnn-numpy.ipynb)\n",
    "- [随机矩阵的奇异值分析](Deep-Learning/singular-value-of-random-matrix.ipynb)\n"
   ]
  }
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