# Deep-learning

**Repository Path**: zy121006/Deep-learning

## Basic Information

- **Project Name**: Deep-learning
- **Description**: 动手学深度学习_pytorch_Jupyter + 学习笔记
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-03-30
- **Last Updated**: 2023-05-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep-learning
动手学深度学习_pytorch_Jupyter + 学习笔记

## ch1-数据操作
### [数据操作](https://github.com/chenyu313/Deep-learning/blob/main/ch1/0-%E6%95%B0%E6%8D%AE%E6%93%8D%E4%BD%9C.ipynb)
### [自动求导](https://github.com/chenyu313/Deep-learning/blob/main/ch1/1-%E8%87%AA%E5%8A%A8%E6%B1%82%E5%AF%BC.ipynb)
### [神经网络](https://github.com/chenyu313/Deep-learning/blob/main/ch1/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.ipynb)

## ch2-线性神经网络
### [线性回归](https://github.com/chenyu313/Deep-learning/blob/main/ch2/2-%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92.ipynb)
### [线性回归代码实现](https://github.com/chenyu313/Deep-learning/blob/main/ch2/3-%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0.ipynb)
### [线性回归简洁实现](https://github.com/chenyu313/Deep-learning/blob/main/ch2/4-%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E7%AE%80%E6%B4%81%E5%AE%9E%E7%8E%B0.ipynb)
### [softmax回归](https://github.com/chenyu313/Deep-learning/blob/main/ch2/5-softmax%E5%9B%9E%E5%BD%92.ipynb)
### [图像分类数据集](https://github.com/chenyu313/Deep-learning/blob/main/ch2/6-%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%E6%95%B0%E6%8D%AE%E9%9B%86.ipynb)
### [softmax回归的从零开始实现](https://github.com/chenyu313/Deep-learning/blob/main/ch2/7-softmax%E5%9B%9E%E5%BD%92%E7%9A%84%E4%BB%8E%E9%9B%B6%E5%BC%80%E5%A7%8B%E5%AE%9E%E7%8E%B0.ipynb)
### [softmax回归的简洁实现](https://github.com/chenyu313/Deep-learning/blob/main/ch2/8-softmax%E5%9B%9E%E5%BD%92%E7%9A%84%E7%AE%80%E6%B4%81%E5%AE%9E%E7%8E%B0.ipynb)

## ch3-多层感知机
### [多层感知机介绍](https://github.com/chenyu313/Deep-learning/blob/main/ch3/9-%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA.ipynb)
### [多层感知机从零开始实现](https://github.com/chenyu313/Deep-learning/blob/main/ch3/10-%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA%E4%BB%8E%E9%9B%B6%E5%BC%80%E5%A7%8B%E5%AE%9E%E7%8E%B0.ipynb)
### [多层感知机简洁实现](https://github.com/chenyu313/Deep-learning/blob/main/ch3/11-%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA%E7%AE%80%E6%B4%81%E5%AE%9E%E7%8E%B0.ipynb)
### [模型选择、过拟合和欠拟合](https://github.com/chenyu313/Deep-learning/blob/main/ch3/12-%E6%A8%A1%E5%9E%8B%E9%80%89%E6%8B%A9%E3%80%81%E8%BF%87%E6%8B%9F%E5%90%88%E5%92%8C%E6%AC%A0%E6%8B%9F%E5%90%88.ipynb)
### [权重衰减](https://github.com/chenyu313/Deep-learning/blob/main/ch3/13-%E6%9D%83%E9%87%8D%E8%A1%B0%E5%87%8F.ipynb)
### [暂退法](https://github.com/chenyu313/Deep-learning/blob/main/ch3/14-%E6%9A%82%E9%80%80%E6%B3%95_Dropout.ipynb)
### [前向传播、反向传播和计算图](https://github.com/chenyu313/Deep-learning/blob/main/ch3/15-%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD%E3%80%81%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD%E5%92%8C%E8%AE%A1%E7%AE%97%E5%9B%BE.ipynb)
### [数值稳定性和模型初始化](https://github.com/chenyu313/Deep-learning/blob/main/ch3/16-%E6%95%B0%E5%80%BC%E7%A8%B3%E5%AE%9A%E6%80%A7%E5%92%8C%E6%A8%A1%E5%9E%8B%E5%88%9D%E5%A7%8B%E5%8C%96.ipynb)
### [环境和分布偏移](https://github.com/chenyu313/Deep-learning/blob/main/ch3/17-%E7%8E%AF%E5%A2%83%E5%92%8C%E5%88%86%E5%B8%83%E5%81%8F%E7%A7%BB.ipynb)
### [实战kaggle：预测房价](https://github.com/chenyu313/Deep-learning/blob/main/ch3/18-%E5%AE%9E%E6%88%98kaggle%E6%AF%94%E8%B5%9B-%E9%A2%84%E6%B5%8B%E6%88%BF%E4%BB%B7.ipynb)

## ch4-深度学习计算
### [层与块](https://github.com/chenyu313/Deep-learning/blob/main/ch4/19-%E5%B1%82%E4%B8%8E%E5%9D%97.ipynb)
### [参数管理](https://github.com/chenyu313/Deep-learning/blob/main/ch4/20-%E5%8F%82%E6%95%B0%E7%AE%A1%E7%90%86.ipynb)
### [自定义层](https://github.com/chenyu313/Deep-learning/blob/main/ch4/21-%E8%87%AA%E5%AE%9A%E4%B9%89%E5%B1%82.ipynb)
### [读写文件](https://github.com/chenyu313/Deep-learning/blob/main/ch4/22-%E8%AF%BB%E5%86%99%E6%96%87%E4%BB%B6.ipynb)
### [GPU](https://github.com/chenyu313/Deep-learning/blob/main/ch4/23-GPU.ipynb)

## ch5-卷积神经网络
### [从全连接层到卷积](https://github.com/chenyu313/Deep-learning/blob/main/ch5/24-%E4%BB%8E%E5%85%A8%E8%BF%9E%E6%8E%A5%E5%B1%82%E5%88%B0%E5%8D%B7%E7%A7%AF.ipynb)
### [图像卷积](https://github.com/chenyu313/Deep-learning/blob/main/ch5/25-%E5%9B%BE%E5%83%8F%E5%8D%B7%E7%A7%AF.ipynb)
### [填充和步幅](https://github.com/chenyu313/Deep-learning/blob/main/ch5/26-%E5%A1%AB%E5%85%85%E5%92%8C%E6%AD%A5%E5%B9%85.ipynb)
### [多输入多输出通道](https://github.com/chenyu313/Deep-learning/blob/main/ch5/27-%E5%A4%9A%E8%BE%93%E5%85%A5%E5%A4%9A%E8%BE%93%E5%87%BA%E9%80%9A%E9%81%93.ipynb)
### [汇聚层](https://github.com/chenyu313/Deep-learning/blob/main/ch5/28-%E6%B1%87%E8%81%9A%E5%B1%82.ipynb)
### [卷积神经网络(LeNet)](https://github.com/chenyu313/Deep-learning/blob/main/ch5/29-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9CLeNet.ipynb)

## ch6-循环神经网络
### [序列模型](https://github.com/chenyu313/Deep-learning/blob/main/ch6/30-%E5%BA%8F%E5%88%97%E6%A8%A1%E5%9E%8B.ipynb)
### [文本预处理](https://github.com/chenyu313/Deep-learning/blob/main/ch6/31-%E6%96%87%E6%9C%AC%E9%A2%84%E5%A4%84%E7%90%86.ipynb)
### [语言模型与数据集](https://github.com/chenyu313/Deep-learning/blob/main/ch6/32-%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E4%B8%8E%E6%95%B0%E6%8D%AE%E9%9B%86.ipynb)
### [循环神经网络](https://github.com/chenyu313/Deep-learning/blob/main/ch6/33-%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.ipynb)
### [循环神经网络从零开始](https://github.com/chenyu313/Deep-learning/blob/main/ch6/34-%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E4%BB%8E%E9%9B%B6%E5%BC%80%E5%A7%8B.ipynb)
### [循环神经网络简洁实现](https://github.com/chenyu313/Deep-learning/blob/main/ch6/35-%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E7%AE%80%E6%B4%81%E5%AE%9E%E7%8E%B0.ipynb)
### [通过时间反向传播](https://github.com/chenyu313/Deep-learning/blob/main/ch6/36-%E9%80%9A%E8%BF%87%E6%97%B6%E9%97%B4%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD.ipynb)

## ch7-现代循环神经网络
### [门控循环单元（GRU）](https://github.com/chenyu313/Deep-learning/blob/main/ch7/37-%E9%97%A8%E6%8E%A7%E5%BE%AA%E7%8E%AF%E5%8D%95%E5%85%83.ipynb)
### [长短期记忆网络（LSTM）](https://github.com/chenyu313/Deep-learning/blob/main/ch7/38-%E9%95%BF%E7%9F%AD%E6%9C%9F%E8%AE%B0%E5%BF%86%E7%BD%91%E7%BB%9C.ipynb)
### [深度循环神经网络](https://github.com/chenyu313/Deep-learning/blob/main/ch7/39-%E6%B7%B1%E5%BA%A6%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.ipynb)
### [双向循环神经网络](https://github.com/chenyu313/Deep-learning/blob/main/ch7/40-%E5%8F%8C%E5%90%91%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.ipynb)
### [机器翻译与数据集](https://github.com/chenyu313/Deep-learning/blob/main/ch7/41-%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91%E4%B8%8E%E6%95%B0%E6%8D%AE%E9%9B%86.ipynb)
### [编码器-解码器架构](https://github.com/chenyu313/Deep-learning/blob/main/ch7/42-%E7%BC%96%E7%A0%81%E5%99%A8-%E8%A7%A3%E7%A0%81%E5%99%A8%E6%9E%B6%E6%9E%84.ipynb)
### [序列到序列学习(seq2seq)](https://github.com/chenyu313/Deep-learning/blob/main/ch7/43-seq2seq.ipynb)
### [束搜索](https://github.com/chenyu313/Deep-learning/blob/main/ch7/44-%E6%9D%9F%E6%90%9C%E7%B4%A2.ipynb)

## ch8-注意力机制
### [注意力提示](https://github.com/chenyu313/Deep-learning/blob/main/ch8/45-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%8F%90%E7%A4%BA.ipynb)
### [注意⼒汇聚：Nadaraya-Watson 核回归](https://github.com/chenyu313/Deep-learning/blob/main/ch8/46-%E6%B3%A8%E6%84%8F%E2%BC%92%E6%B1%87%E8%81%9A%EF%BC%9ANadaraya-Watson%20%E6%A0%B8%E5%9B%9E%E5%BD%92.ipynb)
### [注意力评分函数](https://github.com/chenyu313/Deep-learning/blob/main/ch8/47-%E6%B3%A8%E6%84%8F%E5%8A%9B%E8%AF%84%E5%88%86%E5%87%BD%E6%95%B0.ipynb)
### [Bahdanau 注意⼒](https://github.com/chenyu313/Deep-learning/blob/main/ch8/48-Bahdanau%20%E6%B3%A8%E6%84%8F%E2%BC%92.ipynb)
### [多头注意力](https://github.com/chenyu313/Deep-learning/blob/main/ch8/49-%E5%A4%9A%E5%A4%B4%E6%B3%A8%E6%84%8F%E5%8A%9B.ipynb)
### [自注意力和位置编码](https://github.com/chenyu313/Deep-learning/blob/main/ch8/50-%E8%87%AA%E6%B3%A8%E6%84%8F%E5%8A%9B%E5%92%8C%E4%BD%8D%E7%BD%AE%E7%BC%96%E7%A0%81.ipynb)
### [Transformer](https://github.com/chenyu313/Deep-learning/blob/main/ch8/51-Transformer.ipynb)

## ch9-优化算法
### [优化与深度学习](https://github.com/chenyu313/Deep-learning/blob/main/ch9/52-%E4%BC%98%E5%8C%96%E4%B8%8E%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0.ipynb)
### [凸性](https://github.com/chenyu313/Deep-learning/blob/main/ch9/53-%E5%87%B8%E6%80%A7.ipynb)
### [梯度下降](https://github.com/chenyu313/Deep-learning/blob/main/ch9/54-%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D.ipynb)
### [随机梯度下降](https://github.com/chenyu313/Deep-learning/blob/main/ch9/55-%E9%9A%8F%E6%9C%BA%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D.ipynb)
### [⼩批量随机梯度下降](https://github.com/chenyu313/Deep-learning/blob/main/ch9/56-%E2%BC%A9%E6%89%B9%E9%87%8F%E9%9A%8F%E6%9C%BA%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D.ipynb)
### [动量法](https://github.com/chenyu313/Deep-learning/blob/main/ch9/57-%E5%8A%A8%E9%87%8F%E6%B3%95.ipynb)

## ch10-自然语言处理：预训练
### [词嵌入]()
### [近似计算]()
### [用于预训练词嵌入的数据集]()
### [预训练word2vec]()
### [全局向量词嵌入]()
### [子词嵌入]()
### [词的相似性和类比任务]()
### [来⾃Transformers的双向编码器表⽰（BERT）]()
### [用于预训练BERT的数据集]()
### [预训练BRET]()

## Ex-pytorch_lightning
### [pytorch-lightning 学习笔记](https://github.com/chenyu313/Deep-learning/blob/main/ex/pytorch-lightning%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0.ipynb)

## End-参考
### [References-https://zh.d2l.ai/chapter_preface/index.html](https://zh.d2l.ai/chapter_preface/index.html)