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How to eat Pytorch in 20 days ?🔥🔥

一,本书📖面向读者 👼

本书假定读者有一定的机器学习和深度学习基础,使用过Keras或TensorFlow或Pytorch搭建训练过简单的模型。

🔥🔥号外号外,《20天吃掉那只Pytorch》视频版本登录BiliBili啦,吃货本货倾情掌勺,只为最纯正的乡土味道,欢迎新老朋友前来品尝 🍉🍉!

https://www.bilibili.com/video/BV1Ua411P7oe

二,本书写作风格 🍉

本书是一本对人类用户极其友善的Pytorch入门工具书,Don't let me think是本书的最高追求。

本书主要是在参考Pytorch官方文档和函数doc文档基础上整理写成的。

尽管Pytorch官方文档已经相当简明清晰,但本书在篇章结构和范例选取上做了大量的优化,在用户友好度方面更胜一筹。

本书按照内容难易程度、读者检索习惯和Pytorch自身的层次结构设计内容,循序渐进,层次清晰,方便按照功能查找相应范例。

本书在范例设计上尽可能简约化和结构化,增强范例易读性和通用性,大部分代码片段在实践中可即取即用。

如果说通过学习Pytorch官方文档掌握Pytorch的难度大概是5,那么通过本书学习掌握Pytorch的难度应该大概是2.

仅以下图对比Pytorch官方文档与本书《20天吃掉那只Pytorch》的差异。

三,本书学习方案 ⏰

1,学习计划

本书是作者利用工作之余大概3个月写成的,大部分读者应该在20天可以完全学会。

预计每天花费的学习时间在30分钟到2个小时之间。

当然,本书也非常适合作为Pytorch的工具手册在工程落地时作为范例库参考。

点击学习内容蓝色标题即可进入该章节。

日期 学习内容 内容难度 预计学习时间 更新状态
  一、Pytorch的建模流程 ⭐️ 0hour
day1 1-1,结构化数据建模流程范例 ⭐️⭐️⭐️ 1hour
day2 1-2,图片数据建模流程范例 ⭐️⭐️⭐️⭐️ 2hour
day3 1-3,文本数据建模流程范例 ⭐️⭐️⭐️⭐️⭐️ 2hour
day4 1-4,时间序列数据建模流程范例 ⭐️⭐️⭐️⭐️⭐️ 2hour
  二、Pytorch的核心概念 ⭐️ 0hour
day5 2-1,张量数据结构 ⭐️⭐️⭐️⭐️ 1hour
day6 2-2,自动微分机制 ⭐️⭐️⭐️ 1hour
day7 2-3,动态计算图 ⭐️⭐️⭐️⭐️⭐️ 2hour
  三、Pytorch的层次结构 ⭐️ 0hour
day8 3-1,低阶API示范 ⭐️⭐️⭐️⭐️ 1hour
day9 3-2,中阶API示范 ⭐️⭐️⭐️ 1hour
day10 3-3,高阶API示范 ⭐️⭐️⭐️ 1hour
  四、Pytorch的低阶API ⭐️ 0hour
day11 4-1,张量的结构操作 ⭐️⭐️⭐️⭐️⭐️ 2hour
day12 4-2,张量的数学运算 ⭐️⭐️⭐️⭐️ 1hour
day13 4-3,nn.functional和nn.Module ⭐️⭐️⭐️⭐️ 1hour
  五、Pytorch的中阶API ⭐️ 0hour
day14 5-1,Dataset和DataLoader ⭐️⭐️⭐️⭐️ 1hour
day15 5-2,模型层 ⭐️⭐️⭐️⭐️⭐️ 2hour
day16 5-3,损失函数 ⭐️⭐️⭐️⭐️ 1hour
day17 5-4,TensorBoard可视化 ⭐️⭐️⭐️ 1hour
  六、Pytorch的高阶API ⭐️ 0hour
day18 6-1,构建模型的3种方法 ⭐️⭐️ 0.5hour
day19 6-2,训练模型的3种方法 ⭐️⭐️⭐️ 1hour
day20 6-3,使用GPU训练模型 ⭐️⭐️⭐️⭐️ 1hour
* 后记:我的产品观 ⭐️ 0hour

2,学习环境

本书全部源码在jupyter中编写测试通过,建议通过git克隆到本地,并在jupyter中交互式运行学习。

step1: 克隆本书源码到本地,使用码云镜像仓库国内下载速度更快

git clone https://gitee.com/Python_Ai_Road/eat_pytorch_in_20_days

step2: 公众号 算法美食屋 回复关键词:pytorch, 获取本项目所用数据集汇总压缩包 eat_pytorch_datasets.zip百度云盘下载链接,下载解压并移动到eat_pytorch_in_20_days路径下,约160M。

import torch 
from torch import nn

print("torch version:", torch.__version__)

a = torch.tensor([[2,1]])
b = torch.tensor([[-1,2]])
c = a@b.t()
print("[[2,1]]@[[-1],[2]] =", c.item())
torch version: 2.0.1
[[2,1]]@[[-1],[2]] = 0

四,项目更新记录

1,2022-08🎈🎈更新 pytorch与广告推荐章节

适合对广告推荐领域感兴趣,且需要进阶的同学😋😋

日期 学习内容 内容难度 预计学习时间 更新状态
  七、Pytorch与广告推荐 ⭐️ 0hour
day1 7-1,推荐算法业务 ⭐️⭐️⭐️ 0.5hour
day2 7-2,广告算法业务 ⭐️⭐️⭐️ 0.5hour
day3 7-3,FM模型 ⭐️⭐️⭐️ 1hour
day4 7-4,DeepFM模型 ⭐️⭐️⭐️⭐️ 1hour
day5 7-5,FiBiNET模型 ⭐️⭐️⭐️⭐️ 2hour
day6 7-6,DeepCross模型 ⭐️⭐️⭐️⭐️⭐️ 2hour
day7 7-7,DIN网络 ⭐️⭐️⭐️⭐️⭐️ 2hour
day8 7-8,DIEN网络 ⭐️⭐️⭐️⭐️⭐️ 2hour

2,2023-03🎈🎈更新 彩蛋章节

介绍一些与pytorch相关的周边工具

日期 学习内容 内容难度 预计学习时间 更新状态
  彩蛋:Pytorch周边工具 ⭐️ 0hour
day1 A-1, Kaggle免费GPU使用攻略 ⭐️⭐️⭐️ 1hour
day2 A-2, Streamlit构建机器学习应用 ⭐️⭐️⭐️ 1hour
day3 A-3, 使用Mac M1芯片加速pytorch ⭐️⭐️⭐️ 1hour
day4 A-4, optuna可视化调参魔法指南 ⭐️⭐️⭐️⭐️ 1hour
day5 A-5, gradio让你的机器学习模型性感起来 ⭐️⭐️⭐️⭐️ 1hour
day6 A-6, wandb模型可视化分析 ⭐️⭐️⭐️⭐ 0.5hour
day7 A-7, wandb模型可视化自动调参 ⭐️⭐️⭐️⭐ 1hour

3, 2023-07🎈🎈更新pytorch模型训练工具库torchkeras

相关章节代码进行了对应优化调整。

功能 稳定支持起始版本 依赖或借鉴库
✅ 训练进度条 3.0.0 依赖tqdm,借鉴keras
✅ 训练评估指标 3.0.0 借鉴pytorch_lightning
✅ notebook中训练自带可视化 3.8.0 借鉴fastai
✅ early stopping 3.0.0 借鉴keras
✅ gpu training 3.0.0 依赖accelerate
✅ multi-gpus training(ddp) 3.6.0 依赖accelerate
✅ fp16/bf16 training 3.6.0 依赖accelerate
✅ tensorboard callback 3.7.0 依赖tensorboard
✅ wandb callback 3.7.0 依赖wandb

详情参考项目链接::https://github.com/lyhue1991/torchkeras

五,鼓励和联系作者 🎈🎈

如果本书对你有所帮助,想鼓励一下作者,记得给本项目加一颗星星star⭐️,并分享给你的朋友们喔😊!

如果对本书内容理解上有一些疑问或者建议,可以在公众号"算法美食屋"后台回复关键字:加群,加入读者交流群和大家讨论。

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Pytorch🍊🍉 is delicious, just eat it!😋😋 展开 收起
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