├── Part1.预修知识 ├── 1.概率论.md ├── 微积分.xmind ├── 概率论.xmind └── 线性代数.xmind ├── Part2.总概 ├── 常用特征工程方法.xmind └── 概论.xmind ├── Part3.常用模型 ├── 1.监督学习SupervisedLearning │ ├── 0.广义线性模型GLM │ │ ├── 广义线性模型.md │ │ └── 广义线性模型.xmind │ ├── 1.线性回归（回归）LinearRegression │ │ ├── 1.线性回归.xmind │ │ └── 2.局部加权线性回归.xmind │ ├── 11.概率图模型ProbabilityGrapyModel │ │ ├── 总概.xmind │ │ └── 条件随机场.xmind │ ├── 12.集成学习EnsembleLearning │ │ ├── Adaboost.xmind │ │ ├── Boosting Tree.xmind │ │ ├── GBDT.xmind │ │ └── 随机森林.xmind │ ├── 13.深度学习DeepLearning │ │ ├── 1.前馈神经网络.xmind │ │ ├── 2.防止过拟合.xmind │ │ ├── 3.卷积神经网络 │ │ │ ├── 1.概述.xmind │ │ │ ├── 2.LeNet[Lecun,1998].xmind │ │ │ ├── 3.AlexNet[Hinton,NIPS,2012].xmind │ │ │ ├── 4.Clarifai[ECCV,2014].xmind │ │ │ ├── 5.VGGNet[ICLR,2015].xmind │ │ │ ├── 6.GoogleNet[CVPR,2015].xmind │ │ │ └── 7.ResNet[CVPR,2016].xmind │ │ ├── 4.循环神经网络 │ │ │ ├── LSTM.md │ │ │ ├── 循环神经网络RNN.xmind │ │ │ ├── 注意力机制Attention.xmind │ │ │ ├── 长短期记忆网络LSTM.xmind │ │ │ └── 门控循环单元GRU.xmind │ │ └── 5.深度模型中的优化.xmind │ ├── 2.线性判别（分类）LinearDiscriminant │ │ ├── 1.感知机判别.xmind │ │ ├── 2.fisher判别.xmind │ │ └── 3.最小平方误差判别.xmind │ ├── 3.非线性判别（分类）NonlinearDiscriminant │ │ ├── 1.广义线性判别函数.xmind │ │ ├── 2.分段线性判别函数.xmind │ │ └── 3.势函数法.xmind │ ├── 4.逻辑斯蒂回归LogisticRegression │ │ ├── 1.逻辑斯蒂回归.xmind │ │ ├── 2.SoftmaxRegression.xmind │ │ └── SoftmaxRegression.md │ ├── 6.贝叶斯分类NaiveBayes │ │ ├── 贝叶斯分类-new.xmind │ │ └── 贝叶斯分类.xmind │ ├── 7.决策树DecisionTree │ │ └── 决策树.xmind │ ├── 8.支持向量机SVM │ │ ├── 支持向量回归SVR.xmind │ │ └── 支持向量机SVM.xmind │ └── 9.高斯判别模型GaussDiscriminantModel │ │ └── 高斯判别模型.xmind └── 2.无监督学习UnsupervisedLearning │ └── 2.维归约DimensionReduction │ └── 维归约.xmind ├── Part4.优化算法 ├── A.EM算法 │ └── EM算法.xmind ├── B.梯度下降法 │ └── 梯度下降.xmind └── C.牛顿法 │ └── 牛顿法.xmind ├── Part6.特点领域应用 ├── 1.nlp │ ├── 1.词的向量表示.md │ └── 2.命名实体识别.md ├── 2.知识图谱 │ ├── 1.知识图谱简介.xmind │ ├── 2.知识表示方法.xmind │ ├── 3.知识框架学习.xmind │ ├── 4.实体识别.xmind │ ├── 5.实体消歧.xmind │ └── 6.关系抽取.xmind └── 3.CV │ └── 常见数据集.md └── README.md /Part1.预修知识/1.概率论.md: 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