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Lesson10神经网络的损失函数.ipynb
Lesson10神经网络的损失函数.ipynb
 
 
Lesson11神经网络的学习.ipynb
Lesson11神经网络的学习.ipynb
 
 
Lesson12.1深度学习创建基础函数与可视化.ipynb
Lesson12.1深度学习创建基础函数与可视化.ipynb
 
 
Lesson12.2线性回归建模.ipynb
Lesson12.2线性回归建模.ipynb
 
 
Lesson12.3softmax回归建模.ipynb
Lesson12.3softmax回归建模.ipynb
 
 
Lesson12.3逻辑回归建模.ipynb
Lesson12.3逻辑回归建模.ipynb
 
 
Lesson13.1建模目标与性能评估理论.ipynb
Lesson13.1建模目标与性能评估理论.ipynb
 
 
Lesson13.2拟合度与欠拟合.ipynb
Lesson13.2拟合度与欠拟合.ipynb
 
 
Lesson13.3梯度不平稳.ipynb
Lesson13.3梯度不平稳.ipynb
 
 
Lesson13.4DeadReLU与学习率优化.ipynb
Lesson13.4DeadReLU与学习率优化.ipynb
 
 
Lesson13.5Xavier方法和Kaiming方法.ipynb
Lesson13.5Xavier方法和Kaiming方法.ipynb
 
 
Lesson14.1数据归一化与BatchNormolization.ipynb
Lesson14.1数据归一化与BatchNormolization.ipynb
 
 
Lesson14.2BatchNormalization实现.ipynb
Lesson14.2BatchNormalization实现.ipynb
 
 
Lesson14.3BatcNormoliza实战.ipynb
Lesson14.3BatcNormoliza实战.ipynb
 
 
Lesson15.1学习率调度的基本概念.ipynb
Lesson15.1学习率调度的基本概念.ipynb
 
 
Lesson15.2学习率调度的实现.ipynb
Lesson15.2学习率调度的实现.ipynb
 
 
Lesson16深度视觉入门(下).ipynb
Lesson16深度视觉入门(下).ipynb
 
 
Lesson16深度视觉入门（上）.ipynb
Lesson16深度视觉入门（上）.ipynb
 
 
Lesson17深度视觉应用(上).ipynb
Lesson17深度视觉应用(上).ipynb
 
 
Lesson1张量的创建和常用方法.ipynb
Lesson1张量的创建和常用方法.ipynb
 
 
Lesson2张量的索引切片合并.ipynb
Lesson2张量的索引切片合并.ipynb
 
 
Lesson3张量的广播与科学计算.ipynb
Lesson3张量的广播与科学计算.ipynb
 
 
Lesson4张量的线性代数运算.ipynb
Lesson4张量的线性代数运算.ipynb
 
 
Lesson5基本优化思想与最小二乘法.ipynb
Lesson5基本优化思想与最小二乘法.ipynb
 
 
Lesson6动态计算图和梯度下降入门.ipynb
Lesson6动态计算图和梯度下降入门.ipynb
 
 
Lesson7.认识深度学习Pytorch.ipynb
Lesson7.认识深度学习Pytorch.ipynb
 
 
Lesson8单层神经网络.ipynb
Lesson8单层神经网络.ipynb
 
 
Lesson9深层神经网络.ipynb
Lesson9深层神经网络.ipynb
 
 
README.md
README.md
 
 

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Pytorch_DeepLearing

这是我在学习菜菜和九天老师的PyTorch&深度学习的课程中，所做的总结笔记~

会持续更新~ 预计在国庆假期后完结！

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Pytorch&DeepLearing

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