├── Code ├── DNN │ ├── DNN_binary_classification_example.py │ ├── DNN_multi_classification_example.py │ ├── DNN_multivariable_regression_example.py │ └── hans_on_feedforward_neural_network.py └── RNN │ ├── RNN_text_example.py │ ├── Test_dynamic_programming.py │ ├── dynamic_programming_accelerate_RNN_back_propagation.py │ ├── hans_on_recurrent_neural_network.py │ └── rnn_demo.py ├── LICENSE ├── README.md ├── 反向传播算法.md ├── 反向传播算法 ├── one_1.png ├── one_2.png ├── one_3.png ├── one_4.png ├── one_5.png ├── one_6.png ├── one_7.png └── one_8.png ├── 用动态规划算法加速循环神经网络梯度的反向传播.md ├── 用动态规划算法加速循环神经网络梯度的反向传播 ├── one_1.png ├── one_2.png ├── one_3.png ├── one_4.png ├── one_5.png └── one_6.png ├── 用机器学习解决典型分类问题 └── one_1.png ├── 矩阵形式的Sigmoid多标签分类模型.md ├── 矩阵形式的Sigmoid多标签分类模型 └── 1.png ├── 矩阵形式的Softmax多分类模型.md ├── 矩阵形式的Softmax多分类模型 ├── 0.png └── 1.png ├── 矩阵形式的前馈神经网络.md ├── 矩阵形式的前馈神经网络 ├── one_1.png ├── one_2.png ├── one_3.png ├── one_4.png ├── one_5.png ├── one_6.png ├── one_7.png ├── one_8.png ├── one_9.png ├── two_1.png └── two_2.png ├── 矩阵形式的循环神经网络.md ├── 矩阵形式的循环神经网络 ├── one_1.png └── one_2.png ├── 矩阵形式的感知机.md ├── 矩阵形式的支持向量机.md ├── 矩阵形式的支持向量机 └── 1.png ├── 矩阵形式的线性回归模型.md ├── 矩阵形式的逻辑回归模型.md └── 矩阵形式的逻辑回归模型 └── 1.png /Code/DNN/DNN_binary_classification_example.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yuanxiaosc/Theoretical-Proof-of-Neural-Network-Model-and-Implementation-Based-on-Numpy/HEAD/Code/DNN/DNN_binary_classification_example.py -------------------------------------------------------------------------------- /Code/DNN/DNN_multi_classification_example.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yuanxiaosc/Theoretical-Proof-of-Neural-Network-Model-and-Implementation-Based-on-Numpy/HEAD/Code/DNN/DNN_multi_classification_example.py -------------------------------------------------------------------------------- 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