ApacheCN 数据科学/人工智能/机器学习知识树 2019.2
时间:2022-06-17
本文章向大家介绍ApacheCN 数据科学/人工智能/机器学习知识树 2019.2,主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项,具有一定的参考价值,需要的朋友可以参考一下。
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+ [离散化](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%A6%BB%E6%95%A3%E5%8C%96&type=Code)
+ [等值分箱](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%AD%89%E5%80%BC%E5%88%86%E7%AE%B1&type=Code)
+ [等量分箱](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%AD%89%E9%87%8F%E5%88%86%E7%AE%B1&type=Code)
+ [独热 one-hot](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%8B%AC%E7%83%AD%20one-hot&type=Code)
+ [标准化](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%A0%87%E5%87%86%E5%8C%96&type=Code)
+ [最小最大 min-max](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%9C%80%E5%B0%8F%E6%9C%80%E5%A4%A7%20min-max&type=Code)
+ [z-score](https://github.com/search?l=Markdown&q=org%3Aapachecn+z-score&type=Code)
+ [l2 标准化](https://github.com/search?l=Markdown&q=org%3Aapachecn+l2%20%E6%A0%87%E5%87%86%E5%8C%96&type=Code)
+ [归一化](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%BD%92%E4%B8%80%E5%8C%96&type=Code)
+ [特征选择](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%89%B9%E5%BE%81%E9%80%89%E6%8B%A9&type=Code)
+ [ANOVA](https://github.com/search?l=Markdown&q=org%3Aapachecn+ANOVA&type=Code)
+ [信息增益/信息增益率](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E4%BF%A1%E6%81%AF%E5%A2%9E%E7%9B%8A%20%E4%BF%A1%E6%81%AF%E5%A2%9E%E7%9B%8A%E7%8E%87&type=Code)
+ [评价指标](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87&type=Code)
+ [回归](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%9B%9E%E5%BD%92&type=Code)
+ [MSE](https://github.com/search?l=Markdown&q=org%3Aapachecn+MSE&type=Code)
+ [R 方](https://github.com/search?l=Markdown&q=org%3Aapachecn+R%20%E6%96%B9&type=Code)
+ [分类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%88%86%E7%B1%BB&type=Code)
+ [准确率](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%87%86%E7%A1%AE%E7%8E%87&type=Code)
+ [精确率](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%B2%BE%E7%A1%AE%E7%8E%87&type=Code)
+ [召回率](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8F%AC%E5%9B%9E%E7%8E%87&type=Code)
+ [F1 得分](https://github.com/search?l=Markdown&q=org%3Aapachecn+F1%20%E5%BE%97%E5%88%86&type=Code)
+ [宏平均 F1](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%AE%8F%E5%B9%B3%E5%9D%87%20F1&type=Code)
+ [微平均 F1](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%BE%AE%E5%B9%B3%E5%9D%87%20F1&type=Code)
+ [聚类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%81%9A%E7%B1%BB&type=Code)
+ [互信息](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E4%BA%92%E4%BF%A1%E6%81%AF&type=Code)
+ [轮廓距离](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%BD%AE%E5%BB%93%E8%B7%9D%E7%A6%BB&type=Code)
+ [交叉验证](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81&type=Code)
+ [K 折](https://github.com/search?l=Markdown&q=org%3Aapachecn+K%20%E6%8A%98&type=Code)
+ [网格搜索](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%BD%91%E6%A0%BC%E6%90%9C%E7%B4%A2&type=Code)
+ [梯度下降](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D&type=Code)
+ [随机梯度下降 SGD](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%9A%8F%E6%9C%BA%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%20SGD&type=Code)
+ [牛顿法/拟牛顿法](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%89%9B%E9%A1%BF%E6%B3%95%20%E6%8B%9F%E7%89%9B%E9%A1%BF%E6%B3%95&type=Code)
+ [动量法](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8A%A8%E9%87%8F%E6%B3%95&type=Code)
+ [RMSProp](https://github.com/search?l=Markdown&q=org%3Aapachecn+RMSProp&type=Code)
+ [Adam](https://github.com/search?l=Markdown&q=org%3Aapachecn+Adam&type=Code)
+ [基本概念](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5&type=Code)
+ [欠拟合/过拟合](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%AC%A0%E6%8B%9F%E5%90%88%20%E8%BF%87%E6%8B%9F%E5%90%88&type=Code)
+ [距离](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%B7%9D%E7%A6%BB&type=Code)
+ [汉明距离](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%B1%89%E6%98%8E%E8%B7%9D%E7%A6%BB&type=Code)
+ [曼哈顿距离](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%9B%BC%E5%93%88%E9%A1%BF%E8%B7%9D%E7%A6%BB&type=Code)
+ [欧几里得距离](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%AC%A7%E5%87%A0%E9%87%8C%E5%BE%97%E8%B7%9D%E7%A6%BB&type=Code)
+ [切比雪夫距离](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%88%87%E6%AF%94%E9%9B%AA%E5%A4%AB%E8%B7%9D%E7%A6%BB&type=Code)
+ [余弦相似度](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E4%BD%99%E5%BC%A6%E7%9B%B8%E4%BC%BC%E5%BA%A6&type=Code)
+ [pearson 相似度](https://github.com/search?l=Markdown&q=org%3Aapachecn+pearson%20%E7%9B%B8%E4%BC%BC%E5%BA%A6&type=Code)
+ [损失函数](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0&type=Code)
+ [MSE](https://github.com/search?l=Markdown&q=org%3Aapachecn+MSE&type=Code)
+ [交叉熵](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E4%BA%A4%E5%8F%89%E7%86%B5&type=Code)
+ [Hinge](https://github.com/search?l=Markdown&q=org%3Aapachecn+Hinge&type=Code)
+ [线性模型](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B&type=Code)
+ [线性回归](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92&type=Code)
+ [Lasso/岭回归](https://github.com/search?l=Markdown&q=org%3Aapachecn+Lasso%20%E5%B2%AD%E5%9B%9E%E5%BD%92&type=Code)
+ [正则化](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%AD%A3%E5%88%99%E5%8C%96&type=Code)
+ [逻辑回归](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92&type=Code)
+ [softmax 回归](https://github.com/search?l=Markdown&q=org%3Aapachecn+softmax%20%E5%9B%9E%E5%BD%92&type=Code)
+ [支持向量机](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA&type=Code)
+ [拉格朗日对偶](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%8B%89%E6%A0%BC%E6%9C%97%E6%97%A5%E5%AF%B9%E5%81%B6&type=Code)
+ [软边界支持向量机](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%BD%AF%E8%BE%B9%E7%95%8C%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA&type=Code)
+ [核方法](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%A0%B8%E6%96%B9%E6%B3%95&type=Code)
+ [树和森林](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%A0%91%E5%92%8C%E6%A3%AE%E6%9E%97&type=Code)
+ [决策树](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%86%B3%E7%AD%96%E6%A0%91&type=Code)
+ [随机森林](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97&type=Code)
+ [GDBT/XGBoost](https://github.com/search?l=Markdown&q=org%3Aapachecn+GDBT%20XGBoost&type=Code)
+ [LightGBM](https://github.com/search?l=Markdown&q=org%3Aapachecn+LightGBM&type=Code)
+ [集成学习](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0&type=Code)
+ [Bagging](https://github.com/search?l=Markdown&q=org%3Aapachecn+Bagging&type=Code)
+ [Boosting](https://github.com/search?l=Markdown&q=org%3Aapachecn+Boosting&type=Code)
+ [Adaboost](https://github.com/search?l=Markdown&q=org%3Aapachecn+Adaboost&type=Code)
+ [Blending/Stacking](https://github.com/search?l=Markdown&q=org%3Aapachecn+Blending%20Stacking&type=Code)
+ [KNN](https://github.com/search?l=Markdown&q=org%3Aapachecn+KNN&type=Code)
+ [聚类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%81%9A%E7%B1%BB&type=Code)
+ [KMenas](https://github.com/search?l=Markdown&q=org%3Aapachecn+KMenas&type=Code)
+ [层次聚类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%B1%82%E6%AC%A1%E8%81%9A%E7%B1%BB&type=Code)
+ [凝聚聚类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%87%9D%E8%81%9A%E8%81%9A%E7%B1%BB&type=Code)
+ [分裂聚类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%88%86%E8%A3%82%E8%81%9A%E7%B1%BB&type=Code)
+ [DBSCAN](https://github.com/search?l=Markdown&q=org%3Aapachecn+DBSCAN&type=Code)
+ [谱聚类](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%B0%B1%E8%81%9A%E7%B1%BB&type=Code)
+ [高斯混合模型 GMM](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%AB%98%E6%96%AF%E6%B7%B7%E5%90%88%E6%A8%A1%E5%9E%8B%20GMM&type=Code)
+ [概率图](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%A6%82%E7%8E%87%E5%9B%BE&type=Code)
+ [朴素贝叶斯](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF&type=Code)
+ [隐马尔科夫 HMM](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%9A%90%E9%A9%AC%E5%B0%94%E7%A7%91%E5%A4%AB%20HMM&type=Code)
+ [降维](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%99%8D%E7%BB%B4&type=Code)
+ [PCA/SVD](https://github.com/search?l=Markdown&q=org%3Aapachecn+PCA%20SVD&type=Code)
+ [T-SNE](https://github.com/search?l=Markdown&q=org%3Aapachecn+T-SNE&type=Code)
+ [基本概念](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5&type=Code)
+ [正向传播](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%AD%A3%E5%90%91%E4%BC%A0%E6%92%AD&type=Code)
+ [反向传播](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD&type=Code)
+ [激活函数](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0&type=Code)
+ [sigmoid](https://github.com/search?l=Markdown&q=org%3Aapachecn+sigmoid&type=Code)
+ [softmax](https://github.com/search?l=Markdown&q=org%3Aapachecn+softmax&type=Code)
+ [tanh](https://github.com/search?l=Markdown&q=org%3Aapachecn+tanh&type=Code)
+ [ReLU](https://github.com/search?l=Markdown&q=org%3Aapachecn+ReLU&type=Code)
+ [ELU](https://github.com/search?l=Markdown&q=org%3Aapachecn+ELU&type=Code)
+ [Leaky ReLU](https://github.com/search?l=Markdown&q=org%3Aapachecn+Leaky%20ReLU&type=Code)
+ [丢弃 Dropout](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E4%B8%A2%E5%BC%83%20Dropout&type=Code)
+ [微调 Fine-Tune](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%BE%AE%E8%B0%83%20Fine-Tune&type=Code)
+ [批量归一化 BatchNorm](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%89%B9%E9%87%8F%E5%BD%92%E4%B8%80%E5%8C%96%20BatchNorm&type=Code)
+ [前馈神经网络 DNN/多层感知机 MLP](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%89%8D%E9%A6%88%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%20DNN%20%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA%20MLP&type=Code)
+ [输入层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%BE%93%E5%85%A5%E5%B1%82&type=Code)
+ [隐层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E9%9A%90%E5%B1%82&type=Code)
+ [输出层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%BE%93%E5%87%BA%E5%B1%82&type=Code)
+ [卷积神经网络 CNN](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%20CNN&type=Code)
+ [层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%B1%82&type=Code)
+ [卷积层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8D%B7%E7%A7%AF%E5%B1%82&type=Code)
+ [池化层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%B1%A0%E5%8C%96%E5%B1%82&type=Code)
+ [全连接层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%85%A8%E8%BF%9E%E6%8E%A5%E5%B1%82&type=Code)
+ [经典结构](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%BB%8F%E5%85%B8%E7%BB%93%E6%9E%84&type=Code)
+ [LeNet](https://github.com/search?l=Markdown&q=org%3Aapachecn+LeNet&type=Code)
+ [AlexNet](https://github.com/search?l=Markdown&q=org%3Aapachecn+AlexNet&type=Code)
+ [ZFNet](https://github.com/search?l=Markdown&q=org%3Aapachecn+ZFNet&type=Code)
+ [GoogLeNet](https://github.com/search?l=Markdown&q=org%3Aapachecn+GoogLeNet&type=Code)
+ [VGG](https://github.com/search?l=Markdown&q=org%3Aapachecn+VGG&type=Code)
+ [ResNet](https://github.com/search?l=Markdown&q=org%3Aapachecn+ResNet&type=Code)
+ [DenseNet](https://github.com/search?l=Markdown&q=org%3Aapachecn+DenseNet&type=Code)
+ [循环神经网络 RNN](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%20RNN&type=Code)
+ [循环层](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%BE%AA%E7%8E%AF%E5%B1%82&type=Code)
+ [经典结构](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%BB%8F%E5%85%B8%E7%BB%93%E6%9E%84&type=Code)
+ [LSTM](https://github.com/search?l=Markdown&q=org%3Aapachecn+LSTM&type=Code)
+ [GRU](https://github.com/search?l=Markdown&q=org%3Aapachecn+GRU&type=Code)
+ [BiLSTM](https://github.com/search?l=Markdown&q=org%3Aapachecn+BiLSTM&type=Code)
+ [注意力](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%B3%A8%E6%84%8F%E5%8A%9B&type=Code)
+ [Seq2Seq](https://github.com/search?l=Markdown&q=org%3Aapachecn+Seq2Seq&type=Code)
+ [自编码器](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8&type=Code)
+ [栈式自编码器](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%A0%88%E5%BC%8F%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8&type=Code)
+ [稀疏自编码器](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%A8%80%E7%96%8F%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8&type=Code)
+ [去噪自编码器](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8E%BB%E5%99%AA%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8&type=Code)
+ [变分自编码器](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8&type=Code)
+ [生成对抗网络 GAN](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9C%20GAN&type=Code)
+ [DCGAN](https://github.com/search?l=Markdown&q=org%3Aapachecn+DCGAN&type=Code)
+ [推荐系统](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F&type=Code)
+ [机器视觉 CV](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%9C%BA%E5%99%A8%E8%A7%86%E8%A7%89%20CV&type=Code)
+ [自然语言处理 NLP](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%20NLP&type=Code)
+ [生物信息](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%94%9F%E7%89%A9%E4%BF%A1%E6%81%AF&type=Code)
+ [数据分析](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90&type=Code)
+ [NumPy](https://github.com/search?l=Markdown&q=org%3Aapachecn+NumPy&type=Code)
+ [Pandas](https://github.com/search?l=Markdown&q=org%3Aapachecn+Pandas&type=Code)
+ [科学计算](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E7%A7%91%E5%AD%A6%E8%AE%A1%E7%AE%97&type=Code)
+ [SciPy](https://github.com/search?l=Markdown&q=org%3Aapachecn+SciPy&type=Code)
+ [可视化](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E5%8F%AF%E8%A7%86%E5%8C%96&type=Code)
+ [Matplotlib](https://github.com/search?l=Markdown&q=org%3Aapachecn+Matplotlib&type=Code)
+ [Seaborn](https://github.com/search?l=Markdown&q=org%3Aapachecn+Seaborn&type=Code)
+ [机器学习](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0&type=Code)
+ [scikit-learn/sklearn](https://github.com/search?l=Markdown&q=org%3Aapachecn+scikit-learn%20sklearn&type=Code)
+ [XGBoost](https://github.com/search?l=Markdown&q=org%3Aapachecn+XGBoost&type=Code)
+ [LightGBM](https://github.com/search?l=Markdown&q=org%3Aapachecn+LightGBM&type=Code)
+ [深度学习](https://github.com/search?l=Markdown&q=org%3Aapachecn+%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0&type=Code)
+ [Keras](https://github.com/search?l=Markdown&q=org%3Aapachecn+Keras&type=Code)
+ [TensorFlow](https://github.com/search?l=Markdown&q=org%3Aapachecn+TensorFlow&type=Code)
+ [PyTorch](https://github.com/search?l=Markdown&q=org%3Aapachecn+PyTorch&type=Code)
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