GitHub - zhengq01/PyTorch_Tutorial: Tutorial for deep learning and its PyTorch realizations

第一讲（2h 40min）

Python File	Summary
1.Introduction to PyTorch.ipynb	介绍深度学习及其编程框架，介绍 PyTorch 的动态图
2.PyTorch Basics.ipynb	介绍 PyTorch 中最基础的 Tensor, Variable 以及 autograd 机制
3.Simple Linear Regression Model.ipynb	介绍用于回归问题的线性回归模型，以及梯度下降方法
4.Simple Logistic Regression Model.ipynb	介绍用于分类问题的 Logistic 回归模型，以及用 PyTorch 自带函数实现损失计算，参数优化
5.Fully-Connected Neural Network.ipynb	介绍简单的全连接神经网络，以及 PyTorch 提供的两种构建网络模型的工具

Python File	Summary
6.Introduction to CNNs - Stanford CS231.pdf	介绍CNNs的基本概念
7.CNN Basics.ipynb	介绍怎样在PyTorch中实现简单的CNNs
7_S1.Edge Detection.pdf	补充材料，介绍卷积运算与图像梯度求解之间的联系

Name		Name	Last commit message	Last commit date
Latest commit History 60 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Data		Data
Images		Images
Python_Basics		Python_Basics
.gitattributes		.gitattributes
1. Introduction to PyTorch.ipynb		1. Introduction to PyTorch.ipynb
2. PyTorch Basics.ipynb		2. PyTorch Basics.ipynb
3. Simple Linear Regression Model.ipynb		3. Simple Linear Regression Model.ipynb
4. Simple Logistic Regression Model.ipynb		4. Simple Logistic Regression Model.ipynb
5. Fully-Connected Neural Network.ipynb		5. Fully-Connected Neural Network.ipynb
6. Introduction to CNNs - Stanford CS231.pdf		6. Introduction to CNNs - Stanford CS231.pdf
7. CNN Basics.ipynb		7. CNN Basics.ipynb
7_S1. Edge Detection.pdf		7_S1. Edge Detection.pdf
README.md		README.md