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folder:人工智能实战与应用课程
folder:53.RNN深度理解
folder:50.RNN
folder:18.让神经网络看懂图象
folder:69.人工智能金融应用
folder:59.计算机视觉深度学习入门结构篇
folder:3.高等数学—导数
folder:40.卷积神经网络
folder:9.线性代数—矩阵、等价类和行列式
folder:46.图像识别综述
folder:31.加入概率更健康之逻辑斯蒂回归
folder:19.模拟人类大脑：神经网络的故事
folder:65.数据呈现进阶
folder:70.人工智能与设计
folder:43.CNN进化
folder:8.线性代数—线性空间和线性变换
folder:36.升维大法之SVM
folder:41.PyTorch（上）
folder:49.时间序列分析
folder:45.Resnet残差网络
folder:47.迁移学习
folder:72.个性化推荐算法
folder:68.自然语言处理导入
folder:17.人工智能简史
folder:33.集群模型
folder:63.Scikit-Learn
folder:23让机器来思考之强化学习简介
folder:10.线性代数—特征值与特征向量
folder:29.机器学习背后的数学（上）
folder:1.高等数学—元素和极限
folder:16.人工智能如何改变我们的未来生活
folder:37.多层感知机DNN
folder:38.梯度下降法
folder:56.强化学习（下）
folder:58.计算机视觉深度学习入门目的篇
folder:44.BatchNormalization
folder:67.监督学习－分类
folder:39.BP算法
folder:52.RNN时间序列预测
folder:66.监督学习－回归
folder:54.课程总结
folder:5.高等数学—偏导数
folder:28.KNN算法
folder:2.高等数学—两个重要的极限定理
folder:30.机器学习背后的数学（下）
folder:4.高等数学—泰勒展开
folder:6.高等数学—积分
folder:71.第四范式分享
folder:32.模拟人类理性的决策树
folder:21.模拟人类思维的机器学习工具
folder:26.机器学习引入