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# Learn PyTorch for Deep Learning
<!-- WEHUB_ZH_README -->
> [!NOTE]
> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
> [English](./README.en.md) · [原始项目](https://github.com/mrdbourke/pytorch-deep-learning) · [上游 README](https://github.com/mrdbourke/pytorch-deep-learning/blob/HEAD/README.md)
> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
Welcome to the [Zero to Mastery Learn PyTorch for Deep Learning course](https://dbourke.link/ZTMPyTorch), the second best place to learn PyTorch on the internet (the first being the [PyTorch documentation](https://pytorch.org/docs/stable/index.html)).
# 学习 PyTorch 深度学习
* **Update April 2023:** New [tutorial for PyTorch 2.0](https://www.learnpytorch.io/pytorch_2_intro/) is live! And because PyTorch 2.0 is an additive (new features) and backward-compatible release, all previous course materials will *still* work with PyTorch 2.0.
欢迎来到 [Zero to Mastery Learn PyTorch for Deep Learning 课程](https://dbourke.link/ZTMPyTorch),——互联网上学习 PyTorch 的第二好去处（第一好是 [PyTorch 文档](https://pytorch.org/docs/stable/index.html)).
* **2023 年 4 月更新：** 全新的 [PyTorch 2.0 教程](https://www.learnpytorch.io/pytorch_2_intro/) 已上线！由于 PyTorch 2.0 是增量式（新功能）且向后兼容的发布，所有先前的课程材料在 PyTorch 2.0 下*仍然*可用。
<div align="center">
<a href="https://learnpytorch.io">
<img src="https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/misc-pytorch-course-launch-cover-white-text-black-background.jpg" width=750 alt="pytorch deep learning by zero to mastery cover photo with different sections of the course">
<img src="https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/misc-pytorch-course-launch-cover-white-text-black-background.jpg" width=750 alt="Zero to Mastery PyTorch 深度学习课程封面，展示课程的不同章节">
</a>
</div>
## Contents of this page
## 本页目录
* [Course materials/outline](https://github.com/mrdbourke/pytorch-deep-learning#course-materialsoutline)
* [About this course](https://github.com/mrdbourke/pytorch-deep-learning#about-this-course)
* [Status](https://github.com/mrdbourke/pytorch-deep-learning#status) (the progress of the course creation)
* [Log](https://github.com/mrdbourke/pytorch-deep-learning#log) (a log of the course material creation process)
* [课程材料/大纲](https://github.com/mrdbourke/pytorch-deep-learning#course-materialsoutline)
* [关于本课程](https://github.com/mrdbourke/pytorch-deep-learning#about-this-course)
* [状态](https://github.com/mrdbourke/pytorch-deep-learning#status)（课程制作进度）
* [日志](https://github.com/mrdbourke/pytorch-deep-learning#log)（课程材料制作过程的记录）
## Course materials/outline
## 课程材料/大纲
* 📖 **Online book version:** All of course materials are available in a readable online book at [learnpytorch.io](https://learnpytorch.io).
* 🎥 **First five sections on YouTube:** Learn PyTorch in a day by watching the [first 25 hours of material](https://youtu.be/Z_ikDlimN6A).
* 🔬 **Course focus:** code, code, code, experiment, experiment, experiment.
* 🏃‍♂️ **Teaching style:** [https://sive.rs/kimo](https://sive.rs/kimo).
* 🤔 **Ask a question:** See the [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions) for existing questions/ask your own.
* 📖 **在线书籍版本：** 所有课程材料均可在 [learnpytorch.io](https://learnpytorch.io). 在线阅读。
* 🎥 **YouTube 上前五章：** 观看 [前 25 小时的内容](https://youtu.be/Z_ikDlimN6A).，一天学会 PyTorch。
* 🔬 **课程重点：** 代码、代码、代码，实验、实验、实验。
* 🏃‍♂️ **教学风格：** [https://sive.rs/kimo](https://sive.rs/kimo).
* 🤔 **提问：** 请前往 [GitHub Discussions 页面](https://github.com/mrdbourke/pytorch-deep-learning/discussions) 查看已有问题或提出自己的问题。
| **Section** | **What does it cover?** | **Exercises & Extra-curriculum** | **Slides** |
| **章节** | **涵盖内容** | **练习与课外拓展** | **幻灯片** |
| ----- | ----- | ----- | ----- |
| [00 - PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/) | Many fundamental PyTorch operations used for deep learning and neural networks. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/00_pytorch_fundamentals/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/00_pytorch_and_deep_learning_fundamentals.pdf) |
| [01 - PyTorch Workflow](https://www.learnpytorch.io/01_pytorch_workflow/) | Provides an outline for approaching deep learning problems and building neural networks with PyTorch. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/01_pytorch_workflow/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/01_pytorch_workflow.pdf) |
| [02 - PyTorch Neural Network Classification](https://www.learnpytorch.io/02_pytorch_classification/) | Uses the PyTorch workflow from 01 to go through a neural network classification problem. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/02_pytorch_classification/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf) |
| [03 - PyTorch Computer Vision](https://www.learnpytorch.io/03_pytorch_computer_vision/) | Let's see how PyTorch can be used for computer vision problems using the same workflow from 01 & 02. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/03_pytorch_computer_vision/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/03_pytorch_computer_vision.pdf) |
| [04 - PyTorch Custom Datasets](https://www.learnpytorch.io/04_pytorch_custom_datasets/) | How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05). | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/04_pytorch_custom_datasets/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/04_pytorch_custom_datasets.pdf) |
| [05 - PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/) | PyTorch is designed to be modular, let's turn what we've created into a series of Python scripts (this is how you'll often find PyTorch code in the wild). | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/05_pytorch_going_modular/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/05_pytorch_going_modular.pdf) |
| [06 - PyTorch Transfer Learning](https://www.learnpytorch.io/06_pytorch_transfer_learning/) | Let's take a well performing pre-trained model and adjust it to one of our own problems. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/06_pytorch_transfer_learning/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/06_pytorch_transfer_learning.pdf) |
| [07 - Milestone Project 1: PyTorch Experiment Tracking](https://www.learnpytorch.io/07_pytorch_experiment_tracking/) | We've built a bunch of models... wouldn't it be good to track how they're all going? | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/07_pytorch_experiment_tracking.pdf) |
| [08 - Milestone Project 2: PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/) | PyTorch is the most popular deep learning framework for machine learning research, let's see why by replicating a machine learning paper. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/08_pytorch_paper_replicating/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/08_pytorch_paper_replicating.pdf) |
| [09 - Milestone Project 3: Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/) | So we've built a working PyTorch model... how do we get it in the hands of others? Hint: deploy it to the internet. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/09_pytorch_model_deployment/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/09_pytorch_model_deployment.pdf) |
| [PyTorch Extra Resources](https://www.learnpytorch.io/pytorch_extra_resources/) | This course covers a large amount of PyTorch and deep learning but the field of machine learning is vast, inside here you'll find recommended books and resources for: PyTorch and deep learning, ML engineering, NLP (natural language processing), time series data, where to find datasets and more. | - | - |
| [PyTorch Cheatsheet](https://www.learnpytorch.io/pytorch_cheatsheet/) | A very quick overview of some of the main features of PyTorch plus links to various resources where more can be found in the course and in the PyTorch documentation. | - | - |
| [A Quick PyTorch 2.0 Tutorial](https://www.learnpytorch.io/pytorch_2_intro/) | A fasssssst introduction to PyTorch 2.0, what's new and how to get started along with resources to learn more. | - | - |
| [00 - PyTorch 基础](https://www.learnpytorch.io/00_pytorch_fundamentals/) | 深度学习和神经网络中常用的诸多基础 PyTorch 操作。 | [前往练习与课外拓展](https://www.learnpytorch.io/00_pytorch_fundamentals/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/00_pytorch_and_deep_learning_fundamentals.pdf) |
| [01 - PyTorch 工作流](https://www.learnpytorch.io/01_pytorch_workflow/) | 提供用 PyTorch 解决深度学习问题并构建神经网络的思路大纲。 | [前往练习与课外拓展](https://www.learnpytorch.io/01_pytorch_workflow/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/01_pytorch_workflow.pdf) |
| [02 - PyTorch 神经网络分类](https://www.learnpytorch.io/02_pytorch_classification/) | 运用第 01 章的 PyTorch 工作流，完成一个神经网络分类问题。 | [前往练习与课外拓展](https://www.learnpytorch.io/02_pytorch_classification/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf) |
| [03 - PyTorch 计算机视觉](https://www.learnpytorch.io/03_pytorch_computer_vision/) | 看看如何沿用第 01、02 章的工作流，用 PyTorch 解决计算机视觉问题。 | [前往练习与课外拓展](https://www.learnpytorch.io/03_pytorch_computer_vision/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/03_pytorch_computer_vision.pdf) |
| [04 - PyTorch 自定义数据集](https://www.learnpytorch.io/04_pytorch_custom_datasets/) | 如何将自定义数据集加载到 PyTorch 中？本 notebook 还会为模块化代码（第 05 章介绍）打下基础。 | [前往练习与课外拓展](https://www.learnpytorch.io/04_pytorch_custom_datasets/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/04_pytorch_custom_datasets.pdf) |
| [05 - PyTorch 模块化](https://www.learnpytorch.io/05_pytorch_going_modular/) | PyTorch 的设计注重模块化；让我们把已创建的内容整理成一系列 Python 脚本（这也是你在实际项目中常见的 PyTorch 代码组织方式）。 | [前往练习与课外拓展](https://www.learnpytorch.io/05_pytorch_going_modular/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/05_pytorch_going_modular.pdf) |
| [06 - PyTorch 迁移学习](https://www.learnpytorch.io/06_pytorch_transfer_learning/) | 选取一个表现优异的预训练模型，并针对我们自己的问题进行调整。 | [前往练习与课外拓展](https://www.learnpytorch.io/06_pytorch_transfer_learning/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/06_pytorch_transfer_learning.pdf) |
| [07 - 里程碑项目 1：PyTorch 实验追踪](https://www.learnpytorch.io/07_pytorch_experiment_tracking/) | 我们已经构建了不少模型……要是能追踪它们各自的进展该多好？ | [前往练习与课外拓展](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/07_pytorch_experiment_tracking.pdf) |
| [08 - 里程碑项目 2：PyTorch 论文复现](https://www.learnpytorch.io/08_pytorch_paper_replicating/) | PyTorch 是机器学习研究领域最流行的深度学习框架；让我们通过复现一篇机器学习论文来一探究竟。 | [前往练习与课外拓展](https://www.learnpytorch.io/08_pytorch_paper_replicating/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/08_pytorch_paper_replicating.pdf) |
| [09 - 里程碑项目 3：模型部署](https://www.learnpytorch.io/09_pytorch_model_deployment/) | 我们已经有了一个可运行的 PyTorch 模型……如何把它交到他人手中？提示：部署到互联网上。 | [前往练习与课外拓展](https://www.learnpytorch.io/09_pytorch_model_deployment/#exercises) | [前往幻灯片](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/09_pytorch_model_deployment.pdf) |
| [PyTorch 额外资源](https://www.learnpytorch.io/pytorch_extra_resources/) | 本课程涵盖大量 PyTorch 与深度学习内容，但机器学习领域十分广阔；此处收录了推荐书籍与资源，涵盖：PyTorch 与深度学习、ML 工程、NLP（自然语言处理）、时间序列数据、数据集获取途径等。 | - | - |
| [PyTorch 速查表](https://www.learnpytorch.io/pytorch_cheatsheet/) | 快速概览 PyTorch 的部分主要特性，并提供课程内及 PyTorch 文档中更多资源的链接。 | - | - |
| [PyTorch 2.0 快速教程](https://www.learnpytorch.io/pytorch_2_intro/) | 对 PyTorch 2.0 的超快快快快快入门介绍：有哪些新内容、如何开始，以及进一步学习的资源。 | - | - |
## Status
## 状态
All materials completed and videos published on Zero to Mastery!
所有材料均已完成，视频已在 Zero to Mastery 发布！
See the project page for work-in-progress board - https://github.com/users/mrdbourke/projects/1
请参阅项目页面上的进行中看板 - https://github.com/users/mrdbourke/projects/1
* **Total video count:** 321
* **Done skeleton code for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
* **Done annotations (text) for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
* **Done images for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
* **Done keynotes for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
* **Done exercises and solutions for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
* **视频总数：** 321
* **已完成骨架代码：** 00、01、02、03、04、05、06、07、08、09
* **已完成注释（文本）：** 00、01、02、03、04、05、06、07、08、09
* **已完成配图：** 00、01、02、03、04、05、06、07、08、09
* **已完成主题演讲：** 00、01、02、03、04、05、06、07、08、09
* **已完成练习与解答：** 00、01、02、03、04、05、06、07、08、09
See the [log](https://github.com/mrdbourke/pytorch-deep-learning#log) for almost daily updates.
请参阅[日志](https://github.com/mrdbourke/pytorch-deep-learning#log) 获取几乎每日更新的内容。
## About this course
## 关于本课程
### Who is this course for?
### 本课程适合谁？
**You:** Are a beginner in the field of machine learning or deep learning and would like to learn PyTorch.
**你：** 是机器学习或深度学习领域的初学者，希望学习 PyTorch。
**This course:** Teaches you PyTorch and many machine learning concepts in a hands-on, code-first way.
**本课程：** 以动手实践、代码优先的方式，教你 PyTorch 以及许多机器学习（machine learning）概念。
If you already have 1-year+ experience in machine learning, this course may help but it is specifically designed to be beginner-friendly.
如果你已有 1 年以上机器学习经验，本课程仍可能有帮助，但它专为初学者友好而设计。
### What are the prerequisites?
### 先修要求是什么？
1. 3-6 months coding Python.
2. At least one beginner machine learning course (however this might be able to be skipped, resources are linked for many different topics).
3. Experience using Jupyter Notebooks or Google Colab (though you can pick this up as we go along).
4. A willingness to learn (most important).
1. 3–6 个月 Python 编程经验。
2. 至少一门机器学习入门课程（不过这一步或许可以跳过，文中为许多不同主题提供了资源链接）。
3. 有使用 Jupyter Notebooks 或 Google Colab 的经验（当然你也可以边学边掌握）。
4. 愿意学习（最重要）。
For 1 & 2, I'd recommend the [Zero to Mastery Data Science and Machine Learning Bootcamp](https://dbourke.link/ZTMMLcourse), it'll teach you the fundamentals of machine learning and Python (I'm biased though, I also teach that course).
对于第 1 和第 2 点，我推荐 [Zero to Mastery Data Science and Machine Learning Bootcamp](https://dbourke.link/ZTMMLcourse), 它会教你机器学习和 Python 的基础知识（不过我有私心，我也教那门课）。
### How is the course taught?
### 课程如何讲授？
All of the course materials are available for free in an online book at [learnpytorch.io](https://learnpytorch.io). If you like to read, I'd recommend going through the resources there.
所有课程材料均可免费在线阅读，见 [learnpytorch.io](https://learnpytorch.io). 如果你喜欢阅读，我建议你通读那里的资源。
If you prefer to learn via video, the course is also taught in apprenticeship-style format, meaning I write PyTorch code, you write PyTorch code.
如果你更喜欢通过视频学习，本课程也采用学徒式（apprenticeship-style）授课：我写 PyTorch 代码，你也写 PyTorch 代码。
There's a reason the course motto's include *if in doubt, run the code* and *experiment, experiment, experiment!*.
课程座右铭里有两句很有道理：*如有疑问，就运行代码*（if in doubt, run the code），以及 *实验、实验、再实验！*（experiment, experiment, experiment!）。
My whole goal is to help you to do one thing: learn machine learning by writing PyTorch code.
我的全部目标就是帮你做好一件事：通过编写 PyTorch 代码来学习机器学习。
The code is all written via [Google Colab Notebooks](https://colab.research.google.com) (you could also use Jupyter Notebooks), an incredible free resource to experiment with machine learning.
所有代码都通过 [Google Colab Notebooks](https://colab.research.google.com) 编写（你也可以使用 Jupyter Notebooks），这是一个用于实验机器学习的绝佳免费资源。
### What will I get if I finish the course?
### 完成课程后我能得到什么？
There's certificates and all that jazz if you go through the videos.
如果你跟着视频学习，会有证书之类的东西。
But certificates are meh.
但证书也就那样。
You can consider this course a machine learning momentum builder.
你可以把本课程看作一个机器学习动量（momentum）构建器。
By the end, you'll have written hundreds of lines of PyTorch code.
到课程结束时，你将编写数百行 PyTorch 代码。
And will have been exposed to many of the most important concepts in machine learning.
并接触到机器学习中许多最重要的概念。
So when you go to build your own machine learning projects or inspect a public machine learning project made with PyTorch, it'll feel familiar and if it doesn't, at least you'll know where to look.
因此，当你去构建自己的机器学习项目，或查看用 PyTorch 制作的公开机器学习项目时，你会觉得熟悉；即便不熟悉，至少你也知道该去哪里查找。
### What will I build in the course?
### 课程里我会构建什么？
We start with the barebone fundamentals of PyTorch and machine learning, so even if you're new to machine learning you'll be caught up to speed.
我们从 PyTorch 和机器学习最基础的内容开始，因此即使你刚接触机器学习，也能快速跟上进度。
Then we’ll explore more advanced areas including PyTorch neural network classification, PyTorch workflows, computer vision, custom datasets, experiment tracking, model deployment, and my personal favourite: transfer learning, a powerful technique for taking what one machine learning model has learned on another problem and applying it to your own!
然后我们会探索更高级的领域，包括 PyTorch 神经网络分类、PyTorch 工作流、计算机视觉（computer vision）、自定义数据集、实验跟踪、模型部署，以及我个人最喜欢的：迁移学习（transfer learning）——一种强大技术，可把某个机器学习模型在另一问题上所学到的知识，应用到你自己的问题上！
Along the way, you’ll build three milestone projects surrounding an overarching project called FoodVision, a neural network computer vision model to classify images of food.
在此过程中，你将围绕一个名为 FoodVision 的总项目完成三个里程碑项目：FoodVision 是一个用于对食物图像进行分类的神经网络计算机视觉模型。
These milestone projects will help you practice using PyTorch to cover important machine learning concepts and create a portfolio you can show employers and say "here's what I've done".
这些里程碑项目将帮助你练习使用 PyTorch 覆盖重要的机器学习概念，并打造一份可以向雇主展示的作品集，说“这就是我做过的东西”。
### How do I get started?
### 如何开始？
You can read the materials on any device but this course is best viewed and coded along within a desktop browser.
你可以在任何设备上阅读材料，但本课程最好在桌面浏览器中观看并跟着编码。
The course uses a free tool called Google Colab. If you've got no experience with it, I'd go through the free [Introduction to Google Colab tutorial](https://colab.research.google.com/notebooks/basic_features_overview.ipynb) and then come back here.
课程使用名为 Google Colab 的免费工具。如果你完全没有使用经验，建议先学习免费的 [Introduction to Google Colab tutorial](https://colab.research.google.com/notebooks/basic_features_overview.ipynb) 然后再回到这里。
To start:
开始步骤：
1. Click on one of the notebook or section links above like "[00. PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/)".
2. Click the "Open in Colab" button up the top.
3. Press SHIFT+Enter a few times and see what happens.
1. 点击上方某个 notebook 或章节链接，例如“[00. PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/)”。
2. 点击顶部的“Open in Colab”按钮。
3. 按几次 SHIFT+Enter，看看会发生什么。
### My question isn't answered
### 我的问题没有得到解答
Please leave a [discussion](https://github.com/mrdbourke/pytorch-deep-learning/discussions) or send me an email directly: daniel (at) mrdbourke (dot) com.
请留下一条 [discussion](https://github.com/mrdbourke/pytorch-deep-learning/discussions) 或直接给我发邮件：daniel (at) mrdbourke (dot) com。
## Log
## 日志
Almost daily updates of what's happening.
几乎每天都会更新进展。
* 15 May 2023 - PyTorch 2.0 tutorial finished + videos added to ZTM/Udemy, see code: https://www.learnpytorch.io/pytorch_2_intro/
* 13 Apr 2023 - update PyTorch 2.0 notebook
* 30 Mar 2023 - update PyTorch 2.0 notebook with more info/clean code
* 23 Mar 2023 - upgrade PyTorch 2.0 tutorial with annotations and images
* 13 Mar 2023 - add starter code for PyTorch 2.0 tutorial
* 18 Nov 2022 - add a reference for 3 most common errors in PyTorch + links to course sections for more: https://www.learnpytorch.io/pytorch_most_common_errors/
* 9 Nov 2022 - add PyTorch cheatsheet for a very quick overview of the main features of PyTorch + links to course sections: https://www.learnpytorch.io/pytorch_cheatsheet/
* 9 Nov 2022 - full course materials (300+ videos) are now live on Udemy! You can sign up here: https://www.udemy.com/course/pytorch-for-deep-learning/?couponCode=ZTMGOODIES7 (launch deal code valid for 3-4 days from this line)
* 4 Nov 2022 - add a notebook for PyTorch Cheatsheet in `extras/` (a simple overview of many of the most important functionality of PyTorch)
* 2 Oct 2022 - all videos for section 08 and 09 published (100+ videos for the last two sections)!
* 30 Aug 2022 - recorded 15 videos for 09, total videos: 321, finished section 09 videos!!!! ... even bigger than 08!!
* 29 Aug 2022 - recorded 16 videos for 09, total videos: 306
* 28 Aug 2022 - recorded 11 videos for 09, total videos: 290
* 27 Aug 2022 - recorded 16 videos for 09, total videos: 279
* 26 Aug 2022 - add finishing touchs to notebook 09, add slides for 09, create solutions and exercises for 09
* 25 Aug 2022 - add annotations and cleanup 09, remove TK's, cleanup images, make slides for 09
* 24 Aug 2022 - add annotations to 09, main takeaways, exercises and extra-curriculum done
* 23 Aug 2022 - add annotations to 09, add plenty of images/slides
* 22 Aug 2022 - add annotations to 09, start working on slides/images
* 20 Aug 2022 - add annotations to 09
* 19 Aug 2022 - add annotations to 09, check out the awesome demos!
* 18 Aug 2022 - add annotations to 09
* 17 Aug 2022 - add annotations to 09
* 16 Aug 2022 - add annotations to 09
* 15 Aug 2022 - add annotations to 09
* 13 Aug 2022 - add annotations to 09
* 12 Aug 2022 - add demo files for notebook 09 to `demos/`, start annotating notebook 09 with explainer text
* 11 Aug 2022 - finish skeleton code for notebook 09, course finishes deploying 2x models, one for FoodVision Mini & one for (secret)
* 10 Aug 2022 - add section for PyTorch Extra Resources (places to learn more about PyTorch/deep learning): https://www.learnpytorch.io/pytorch_extra_resources/
* 09 Aug 2022 - add more skeleton code to notebook 09
* 08 Aug 2022 - create draft notebook for 09, end goal to deploy FoodVision Mini model and make it publically accessible
* 05 Aug 2022 - recorded 11 videos for 08, total videos: 263, section 08 videos finished!... the biggest section so far
* 04 Aug 2022 - recorded 13 videos for 08, total videos: 252
* 03 Aug 2022 - recorded 3 videos for 08, total videos: 239
* 02 Aug 2022 - recorded 12 videos for 08, total videos: 236
* 30 July 2022 - recorded 11 videos for 08, total videos: 224
* 29 July 2022 - add exercises + solutions for 08, see live walkthrough on YouTube: https://youtu.be/tjpW_BY8y3g
* 28 July 2022 - add slides for 08
* 27 July 2022 - cleanup much of 08, start on slides for 08, exercises and extra-curriculum next
* 26 July 2022 - add annotations and images for 08
* 25 July 2022 - add annotations for 08
* 24 July 2022 - launched first half of course (notebooks 00-04) in a single video (25+ hours!!!) on YouTube: https://youtu.be/Z_ikDlimN6A
* 21 July 2022 - add annotations and images for 08
* 20 July 2022 - add annotations and images for 08, getting so close! this is an epic section
* 19 July 2022 - add annotations and images for 08
* 15 July 2022 - add annotations and images for 08
* 14 July 2022 - add annotations for 08
* 12 July 2022 - add annotations for 08, woo woo this is bigggg section!
* 11 July 2022 - add annotations for 08
* 9 July 2022 - add annotations for 08
* 8 July 2022 - add a bunch of annotations to 08
* 6 July 2022 - course launched on ZTM Academy with videos for sections 00-07! 🚀 - https://dbourke.link/ZTMPyTorch
* 1 July 2022 - add annotations and images for 08
* 30 June 2022 - add annotations for 08
* 28 June 2022 - recorded 11 videos for section 07, total video count 213, all videos for section 07 complete!
* 27 June 2022 - recorded 11 videos for section 07, total video count 202
* 25 June 2022 - recreated 7 videos for section 06 to include updated APIs, total video count 191
* 24 June 2022 - recreated 12 videos for section 06 to include updated APIs
* 23 June 2022 - finish annotations for 07, add exercise template and solutions for 07 + video walkthrough on YouTube: https://youtu.be/cO_r2FYcAjU
* 21 June 2022 - make 08 runnable end-to-end, add images and annotations for 07
* 17 June 2022 - fix up 06, 07 v2 for upcoming torchvision version upgrade, add plenty of annotations to 08
* 13 June 2022 - add notebook 08 first version, starting to replicate the Vision Transformer paper
* 10 June 2022 - add annotations for 07 v2
* 09 June 2022 - create 07 v2 for `torchvision` v0.13 (this will replace 07 v1 when `torchvision=0.13` is released)
* 08 June 2022 - adapt 06 v2 for `torchvision` v0.13 (this will replace 06 v1 when `torchvision=0.13` is released)
* 07 June 2022 - create notebook 06 v2 for upcoming `torchvision` v0.13 update (new transfer learning methods)
* 04 June 2022 - add annotations for 07
* 03 June 2022 - huuuuuuge amount of annotations added to 07
* 31 May 2022 - add a bunch of annotations for 07, make code runnable end-to-end
* 30 May 2022 - record 4 videos for 06, finished section 06, onto section 07, total videos 186
* 28 May 2022 - record 10 videos for 06, total videos 182
* 24 May 2022 - add solutions and exercises for 06
* 23 May 2022 - finished annotations and images for 06, time to do exercises and solutions
* 22 May 2202 - add plenty of images to 06
* 18 May 2022 - add plenty of annotations to 06
* 17 May 2022 - added a bunch of annotations for section 06
* 16 May 2022 - recorded 10 videos for section 05, finish videos for section 05 ✅
* 12 May 2022 - added exercises and solutions for 05
* 11 May 2022 - clean up part 1 and part 2 notebooks for 05, make slides for 05, start on exercises and solutions for 05
* 10 May 2022 - huuuuge updates to the 05 section, see the website, it looks pretty: https://www.learnpytorch.io/05_pytorch_going_modular/
* 09 May 2022 - add a bunch of materials for 05, cleanup docs
* 08 May 2022 - add a bunch of materials for 05
* 06 May 2022 - continue making materials for 05
* 05 May 2022 - update section 05 with headings/outline
* 28 Apr 2022 - recorded 13 videos for 04, finished videos for 04, now to make materials for 05
* 27 Apr 2022 - recorded 3 videos for 04
* 26 Apr 2022 - recorded 10 videos for 04
* 25 Apr 2022 - recorded 11 videos for 04
* 24 Apr 2022 - prepared slides for 04
* 23 Apr 2022 - recorded 6 videos for 03, finished videos for 03, now to 04
* 22 Apr 2022 - recorded 5 videos for 03
* 21 Apr 2022 - recorded 9 videos for 03
* 20 Apr 2022 - recorded 3 videos for 03
* 19 Apr 2022 - recorded 11 videos for 03
* 18 Apr 2022 - finish exercises/solutions for 04, added live-coding walkthrough of 04 exercises/solutions on YouTube: https://youtu.be/vsFMF9wqWx0
* 16 Apr 2022 - finish exercises/solutions for 03, added live-coding walkthrough of 03 exercises/solutions on YouTube: https://youtu.be/_PibmqpEyhA
* 14 Apr 2022 - add final images/annotations for 04, begin on exercises/solutions for 03 & 04
* 13 Apr 2022 - add more images/annotations for 04
* 3 Apr 2022 - add more annotations for 04
* 2 Apr 2022 - add more annotations for 04
* 1 Apr 2022 - add more annotations for 04
* 31 Mar 2022 - add more annotations for 04
* 29 Mar 2022 - add more annotations for 04
* 27 Mar 2022 - starting to add annotations for 04
* 26 Mar 2022 - making dataset for 04
* 25 Mar 2022 - make slides for 03
* 24 Mar 2022 - fix error for 03 not working in docs (finally)
* 23 Mar 2022 - add more images for 03
* 22 Mar 2022 - add images for 03
* 20 Mar 2022 - add more annotations for 03
* 18 Mar 2022 - add more annotations for 03
* 17 Mar 2022 - add more annotations for 03
* 16 Mar 2022 - add more annotations for 03
* 15 Mar 2022 - add more annotations for 03
* 14 Mar 2022 - start adding annotations for notebook 03, see the work in progress here: https://www.learnpytorch.io/03_pytorch_computer_vision/
* 12 Mar 2022 - recorded 12 videos for 02, finished section 02, now onto making materials for 03, 04, 05
* 11 Mar 2022 - recorded 9 videos for 02
* 10 Mar 2022 - recorded 10 videos for 02
* 9 Mar 2022 - cleaning up slides/code for 02, getting ready for recording
* 8 Mar 2022 - recorded 9 videos for section 01, finished section 01, now onto 02
* 7 Mar 2022 - recorded 4 videos for section 01
* 6 Mar 2022 - recorded 4 videos for section 01
* 4 Mar 2022 - recorded 10 videos for section 01
* 20 Feb 2022 - recorded 8 videos for section 00, finished section, now onto 01
* 18 Feb 2022 - recorded 13 videos for section 00
* 17 Feb 2022 - recorded 11 videos for section 00
* 16 Feb 2022 - added setup guide
* 12 Feb 2022 - tidy up README with table of course materials, finish images and slides for 01
* 10 Feb 2022 - finished slides and images for 00, notebook is ready for publishing: https://www.learnpytorch.io/00_pytorch_fundamentals/
* 01-07 Feb 2022 - add annotations for 02, finished, still need images, going to work on exercises/solutions today
* 31 Jan 2022 - start adding annotations for 02
* 28 Jan 2022 - add exercies and solutions for 01
* 26 Jan 2022 - lots more annotations to 01, should be finished tomorrow, will do exercises + solutions then too
* 24 Jan 2022 - add a bunch of annotations to 01
* 21 Jan 2022 - start adding annotations for 01
* 20 Jan 2022 - finish annotations for 00 (still need to add images), add exercises and solutions for 00
* 19 Jan 2022 - add more annotations for 00
* 18 Jan 2022 - add more annotations for 00
* 17 Jan 2022 - back from holidays, adding more annotations to 00
* 10 Dec 2021 - start adding annotations for 00
* 9 Dec 2021 - Created a website for the course ([learnpytorch.io](https://learnpytorch.io)) you'll see updates posted there as development continues
* 8 Dec 2021 - Clean up notebook 07, starting to go back through code and add annotations
* 26 Nov 2021 - Finish skeleton code for 07, added four different experiments, need to clean up and make more straightforward
* 25 Nov 2021 - clean code for 06, add skeleton code for 07 (experiment tracking)
* 24 Nov 2021 - Update 04, 05, 06 notebooks for easier digestion and learning, each section should cover a max of 3 big ideas, 05 is now dedicated to turning notebook code into modular code
* 22 Nov 2021 - Update 04 train and test functions to make more straightforward
* 19 Nov 2021 - Added 05 (transfer learning) notebook, update custom data loading code in 04
* 18 Nov 2021 - Updated vision code for 03 and added custom dataset loading code in 04
* 12 Nov 2021 - Added a bunch of skeleton code to notebook 04 for custom dataset loading, next is modelling with custom data
* 10 Nov 2021 - researching best practice for custom datasets for 04
* 9 Nov 2021 - Update 03 skeleton code to finish off building CNN model, onto 04 for loading custom datasets
* 4 Nov 2021 - Add GPU code to 03 + train/test loops + `helper_functions.py`
* 3 Nov 2021 - Add basic start for 03, going to finish by end of week
* 29 Oct 2021 - Tidied up skeleton code for 02, still a few more things to clean/tidy, created 03
* 28 Oct 2021 - Finished skeleton code for 02, going to clean/tidy tomorrow, 03 next week
* 27 Oct 2021 - add a bunch of code for 02, going to finish tomorrow/by end of week
* 26 Oct 2021 - update 00, 01, 02 with outline/code, skeleton code for 00 & 01 done, 02 next
* 23, 24 Oct 2021 - update 00 and 01 notebooks with more outline/code
* 20 Oct 2021 - add v0 outlines for 01 and 02, add rough outline of course to README, this course will focus on less but better
* 19 Oct 2021 - Start repo 🔥, add fundamentals notebook draft v0
* 15 May 2023 - PyTorch 2.0 教程完成 + 视频已添加到 ZTM/Udemy，见代码：https://www.learnpytorch.io/pytorch_2_intro/
* 13 Apr 2023 - 更新 PyTorch 2.0 notebook
* 30 Mar 2023 - 更新 PyTorch 2.0 notebook，补充更多信息/清理代码
* 23 Mar 2023 - 升级 PyTorch 2.0 教程，添加注释和图片
* 13 Mar 2023 - 为 PyTorch 2.0 教程添加入门代码
* 18 Nov 2022 - 添加 PyTorch 3 个最常见错误的参考 + 更多课程章节链接：https://www.learnpytorch.io/pytorch_most_common_errors/
* 9 Nov 2022 - 添加 PyTorch cheatsheet，快速概览 PyTorch 主要特性 + 课程章节链接：https://www.learnpytorch.io/pytorch_cheatsheet/
* 9 Nov 2022 - 完整课程材料（300+ 个视频）现已在 Udemy 上线！可在此注册：https://www.udemy.com/course/pytorch-for-deep-learning/?couponCode=ZTMGOODIES7（上线优惠码自本条起 3–4 天内有效）
* 4 Nov 2022 - 在 `extras/` 中添加 PyTorch Cheatsheet 的 notebook（简要概览 PyTorch 许多最重要功能）
* 2 Oct 2022 - 第 08 和 09 节的所有视频已发布（最后两节 100+ 个视频）！
* 30 Aug 2022 - 为 09 录制 15 个视频，视频总数：321，第 09 节视频完成！！！……甚至比 08 还大！！
* 29 Aug 2022 - 为 09 录制 16 个视频，视频总数：306
* 28 Aug 2022 - 为 09 录制 11 个视频，视频总数：290
* 27 Aug 2022 - 为 09 录制 16 个视频，视频总数：279
* 26 Aug 2022 - 为 notebook 09 做收尾，添加 09 的 slides，创建 09 的 solutions 和 exercises
* 25 Aug 2022 - 为 09 添加注释并清理，移除 TK's，清理图片，制作 09 的 slides
* 24 Aug 2022 - 为 09 添加注释，main takeaways、exercises 和 extra-curriculum 完成
* 23 Aug 2022 - 为 09 添加注释，添加大量图片/slides
* 22 Aug 2022 - 为 09 添加注释，开始制作 slides/图片
* 20 Aug 2022 - 为 09 添加注释
* 19 Aug 2022 - 为 09 添加注释，看看这些很棒的 demos！
* 18 Aug 2022 - 为 09 添加注释
* 17 Aug 2022 - 为 09 添加注释
* 16 Aug 2022 - 为 09 添加注释
* 15 Aug 2022 - 为 09 添加注释
* 13 Aug 2022 - 为 09 添加注释
* 12 Aug 2022 - 将 notebook 09 的 demo 文件添加到 `demos/`，开始为 notebook 09 添加说明文字注释
* 11 Aug 2022 - 完成 notebook 09 的骨架代码，课程完成部署 2 个模型，一个用于 FoodVision Mini，另一个用于（secret）
* 10 Aug 2022 - 添加 PyTorch Extra Resources 章节（学习更多 PyTorch/深度学习的去处）：https://www.learnpytorch.io/pytorch_extra_resources/
* 09 Aug 2022 - 为 notebook 09 添加更多骨架代码
* 08 Aug 2022 - 创建 09 的草稿 notebook，最终目标是部署 FoodVision Mini 模型并使其公开可访问
* 05 Aug 2022 - 为 08 录制 11 个视频，视频总数：263，第 08 节视频完成！……目前为止最大的一节
* 04 Aug 2022 - 为 08 录制 13 个视频，视频总数：252
* 03 Aug 2022 - 为 08 录制 3 个视频，视频总数：239
* 02 Aug 2022 - 为 08 录制 12 个视频，视频总数：236
* 30 July 2022 - 为 08 录制 11 个视频，视频总数：224
* 29 July 2022 - 为 08 添加 exercises + solutions，见 YouTube 上的 live walkthrough：https://youtu.be/tjpW_BY8y3g
* 28 July 2022 - 为 08 添加 slides
* 27 July 2022 - 清理 08 的大部分内容，开始制作 08 的 slides，接下来做 exercises 和 extra-curriculum
* 26 July 2022 - 为 08 添加注释和图片
* 25 July 2022 - 为 08 添加注释
* 24 July 2022 - 在 YouTube 上以单个视频（25+ 小时！！！）发布课程前半部分（notebooks 00–04）：https://youtu.be/Z_ikDlimN6A
* 21 July 2022 - 为 08 添加注释和图片
* 20 July 2022 - 为 08 添加注释和图片，越来越近了！这是史诗级的一节
* 19 July 2022 - 为 08 添加注释和图片
* 15 July 2022 - 为 08 添加注释和图片
* 14 July 2022 - 为 08 添加注释
* 12 July 2022 - 为 08 添加注释，woo woo 这是超大大的一节！
* 11 July 2022 - 为 08 添加注释
* 9 July 2022 - 为 08 添加注释
* 8 July 2022 - 为 08 添加大量注释
* 6 July 2022 - 课程在 ZTM Academy 上线，包含第 00–07 节的视频！🚀 - https://dbourke.link/ZTMPyTorch
* 1 July 2022 - 为 08 添加注释和图片
* 30 June 2022 - 为 08 添加注释
* 28 June 2022 - 为第 07 节录制 11 个视频，视频总数 213，第 07 节所有视频完成！
* 27 June 2022 - 为第 07 节录制 11 个视频，视频总数 202
* 25 June 2022 - 为第 06 节重录 7 个视频以包含更新后的 API，视频总数 191
* 24 June 2022 - 为第 06 节重录 12 个视频以包含更新后的 API
* 23 June 2022 - 完成 07 的注释，为 07 添加 exercise template 和 solutions + YouTube 上的 video walkthrough：https://youtu.be/cO_r2FYcAjU
* 21 June 2022 - 使 08 可端到端运行，为 07 添加图片和注释
* 17 June 2022 - 修复 06、07 v2 以适配即将发布的 torchvision 版本升级，为 08 添加大量注释
* 13 June 2022 - 添加 notebook 08 第一版，开始复现 Vision Transformer 论文
* 10 June 2022 - 为 07 v2 添加注释
* 09 June 2022 - 为 `torchvision` v0.13 创建 07 v2（当 `torchvision=0.13` 发布后将替换 07 v1）
* 08 June 2022 - 为 `torchvision` v0.13 适配 06 v2（当 `torchvision=0.13` 发布后将替换 06 v1）
* 07 June 2022 - 为即将发布的 `torchvision` v0.13 更新创建 notebook 06 v2（新的 transfer learning 方法）
* 04 June 2022 - 为 07 添加注释
* 03 June 2022 - 为 07 添加海量注释
* 31 May 2022 - 为 07 添加大量注释，使代码可端到端运行
* 30 May 2022 - 为 06 录制 4 个视频，完成第 06 节，进入第 07 节，视频总数 186
* 28 May 2022 - 为 06 录制 10 个视频，视频总数 182
* 24 May 2022 - 为 06 添加 solutions 和 exercises
* 23 May 2022 - 完成 06 的注释和图片，该做 exercises 和 solutions 了
* 22 May 2202 - 为 06 添加大量图片
* 18 May 2022 - 为 06 添加大量注释
* 17 May 2022 - 为第 06 节添加大量注释
* 16 May 2022 - 为第 05 节录制 10 个视频，完成第 05 节视频 ✅
* 12 May 2022 - 为 05 添加 exercises 和 solutions
* 11 May 2022 - 清理 05 的 part 1 和 part 2 notebooks，制作 05 的 slides，开始做 05 的 exercises 和 solutions
* 10 May 2022 - 对 05 节进行海量更新，见网站，看起来很漂亮：https://www.learnpytorch.io/05_pytorch_going_modular/
* 09 May 2022 - 为 05 添加大量材料，清理文档
* 08 May 2022 - 为 05 添加大量材料
* 06 May 2022 - 继续制作 05 的材料
* 05 May 2022 - 用标题/大纲更新第 05 节
* 28 Apr 2022 - 为 04 录制 13 个视频，完成第 04 节视频，现在制作 05 的材料
* 27 Apr 2022 - 为 04 录制 3 个视频
* 26 Apr 2022 - 为 04 录制 10 个视频
* 25 Apr 2022 - 为 04 录制 11 个视频
* 24 Apr 2022 - 为 04 准备 slides
* 23 Apr 2022 - 为 03 录制 6 个视频，完成第 03 节视频，现在进入 04
* 22 Apr 2022 - 为 03 录制 5 个视频
* 21 Apr 2022 - 为 03 录制 9 个视频
* 20 Apr 2022 - 为 03 录制 3 个视频
* 19 Apr 2022 - 为 03 录制 11 个视频
* 18 Apr 2022 - 完成 04 的 exercises/solutions，在 YouTube 上添加 04 exercises/solutions 的 live-coding walkthrough：https://youtu.be/vsFMF9wqWx0
* 16 Apr 2022 - 完成 03 的 exercises/solutions，在 YouTube 上添加 03 exercises/solutions 的 live-coding walkthrough：https://youtu.be/_PibmqpEyhA
* 14 Apr 2022 - 为 04 添加最终图片/注释，开始制作 03 和 04 的 exercises/solutions
* 13 Apr 2022 - 为 04 添加更多图片/注释
* 3 Apr 2022 - 为 04 添加更多注释
* 2 Apr 2022 - 为 04 添加更多注释
* 1 Apr 2022 - 为 04 添加更多注释
* 31 Mar 2022 - 为 04 添加更多注释
* 29 Mar 2022 - 为 04 添加更多注释
* 27 Mar 2022 - 开始为 04 添加注释
* 26 Mar 2022 - 为 04 制作 dataset
* 25 Mar 2022 - 为 03 制作 slides
* 24 Mar 2022 - 修复 03 在 docs 中无法运行的问题（终于）
* 23 Mar 2022 - 为 03 添加更多图片
* 22 Mar 2022 - 为 03 添加图片
* 20 Mar 2022 - 为 03 添加更多注释
* 18 Mar 2022 - 为 03 添加更多注释
* 17 Mar 2022 - 为 03 添加更多注释
* 16 Mar 2022 - 为 03 添加更多注释
* 15 Mar 2022 - 为 03 添加更多注释
* 14 Mar 2022 - 开始为 notebook 03 添加注释，见进行中的工作：https://www.learnpytorch.io/03_pytorch_computer_vision/
* 12 Mar 2022 - 为 02 录制 12 个视频，完成第 02 节，现在制作 03、04、05 的材料
* 11 Mar 2022 - 为 02 录制 9 个视频
* 10 Mar 2022 - 为 02 录制 10 个视频
* 9 Mar 2022 - 清理 02 的 slides/code，准备录制
* 8 Mar 2022 - 为第 01 节录制 9 个视频，完成第 01 节，现在进入 02
* 7 Mar 2022 - 为第 01 节录制 4 个视频
* 6 Mar 2022 - 为第 01 节录制 4 个视频
* 4 Mar 2022 - 为第 01 节录制 10 个视频
* 20 Feb 2022 - 为第 00 节录制 8 个视频，完成该节，现在进入 01
* 18 Feb 2022 - 为第 00 节录制 13 个视频
* 17 Feb 2022 - 为第 00 节录制 11 个视频
* 16 Feb 2022 - 添加 setup guide
* 12 Feb 2022 - 整理 README，添加课程材料表格，完成 01 的图片和 slides
* 10 Feb 2022 - 完成 00 的 slides 和图片，notebook 已可发布：https://www.learnpytorch.io/00_pytorch_fundamentals/
* 01-07 Feb 2022 - 为 02 添加注释，已完成，仍需要图片，今天将做 exercises/solutions
* 31 Jan 2022 - 开始为 02 添加注释
* 28 Jan 2022 - 为 01 添加 exercies 和 solutions
* 26 Jan 2022 - 为 01 添加更多注释，明天应该能完成，之后也会做 exercises + solutions
* 24 Jan 2022 - 为 01 添加大量注释
* 21 Jan 2022 - 开始为 01 添加注释
* 20 Jan 2022 - 完成 00 的注释（仍需添加图片），为 00 添加 exercises 和 solutions
* 19 Jan 2022 - 为 00 添加更多注释
* 18 Jan 2022 - 为 00 添加更多注释
* 17 Jan 2022 - 假期回来，为 00 添加更多注释
* 10 Dec 2021 - 开始为 00 添加注释
* 9 Dec 2021 - 为课程创建网站（[learnpytorch.io](https://learnpytorch.io)) 随着开发推进，你会在那里看到更新发布
* 8 Dec 2021 - 清理 notebook 07，开始回头检查代码并添加注释
* 26 Nov 2021 - 完成 07 的骨架代码，添加了四个不同实验，需要清理并使其更直观
* 25 Nov 2021 - 清理 06 的代码，为 07 添加骨架代码（experiment tracking）
* 24 Nov 2021 - 更新 04、05、06 notebooks 以便更易消化和学习，每节最多涵盖 3 个大概念，05 现在专门用于将 notebook 代码转为模块化代码
* 22 Nov 2021 - 更新 04 的 train 和 test 函数，使其更直观
* 19 Nov 2021 - 添加 05（transfer learning）notebook，更新 04 中的 custom data loading 代码
* 18 Nov 2021 - 更新 03 的 vision 代码，并在 04 中添加 custom dataset loading 代码
* 12 Nov 2021 - 为 notebook 04 添加大量 custom dataset loading 的骨架代码，下一步是用 custom data 建模
* 10 Nov 2021 - 为 04 研究 custom datasets 的最佳实践
* 9 Nov 2021 - 更新 03 骨架代码以完成 CNN model 构建，接下来进入 04 加载 custom datasets
* 4 Nov 2021 - 为 03 添加 GPU 代码 + train/test loops + `helper_functions.py`
* 3 Nov 2021 - 为 03 添加基础起步，计划本周末完成
* 29 Oct 2021 - 整理 02 的骨架代码，仍有一些需要清理/整理，创建了 03
* 28 Oct 2021 - 完成 02 的骨架代码，明天将清理/整理，下周做 03
* 27 Oct 2021 - 为 02 添加大量代码，计划明天/本周末完成
* 26 Oct 2021 - 用大纲/代码更新 00、01、02，00 和 01 的骨架代码完成，接下来是 02
* 23, 24 Oct 2021 - 用更多大纲/代码更新 00 和 01 notebooks
* 20 Oct 2021 - 为 01 和 02 添加 v0 大纲，在 README 中添加课程粗略大纲，本课程将聚焦更少但更好的内容
* 19 Oct 2021 - 启动仓库 🔥，添加 fundamentals notebook 草稿 v0