pytorch-deep-learning README

The project's README from github.com/mrdbourke/pytorch-deep-learning. As one markdown file: README.md.

Learn PyTorch for Deep Learning

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation).

pytorch deep learning by zero to mastery cover photo with different sections of the course

Contents of this page

Course materials/outline

Section What does it cover? Exercises & Extra-curriculum Slides
00 - PyTorch Fundamentals Many fundamental PyTorch operations used for deep learning and neural networks. Go to exercises & extra-curriculum Go to slides
01 - PyTorch Workflow Provides an outline for approaching deep learning problems and building neural networks with PyTorch. Go to exercises & extra-curriculum Go to slides
02 - PyTorch Neural Network Classification Uses the PyTorch workflow from 01 to go through a neural network classification problem. Go to exercises & extra-curriculum Go to slides
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 Go to slides
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 Go to slides
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 Go to slides
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 Go to slides
07 - Milestone Project 1: 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 Go to slides
08 - Milestone Project 2: 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 Go to slides
09 - Milestone Project 3: 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 Go to slides
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 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 A fasssssst introduction to PyTorch 2.0, what's new and how to get started along with resources to learn more. - -

Status

All materials completed and videos published on Zero to Mastery!

See the project page for work-in-progress board - https://github.com/users/mrdbourke/projects/1

See the log for almost daily updates.

About this course

Who is this course for?

You: Are a beginner in the field of machine learning or deep learning and would like to learn PyTorch.

This course: Teaches you PyTorch and many machine learning concepts in a hands-on, code-first way.

If you already have 1-year+ experience in machine learning, this course may help but it is specifically designed to be beginner-friendly.

What are the prerequisites?

  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).

For 1 & 2, I'd recommend the Zero to Mastery Data Science and Machine Learning Bootcamp, it'll teach you the fundamentals of machine learning and Python (I'm biased though, I also teach that course).

How is the course taught?

All of the course materials are available for free in an online book at learnpytorch.io. If you like to read, I'd recommend going through the resources there.

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.

There's a reason the course motto's include if in doubt, run the code and experiment, experiment, experiment!.

My whole goal is to help you to do one thing: learn machine learning by writing PyTorch code.

The code is all written via Google Colab Notebooks (you could also use Jupyter Notebooks), an incredible free resource to experiment with machine learning.

What will I get if I finish the course?

There's certificates and all that jazz if you go through the videos.

But certificates are meh.

You can consider this course a machine learning momentum builder.

By the end, you'll have written hundreds of lines of PyTorch code.

And will have been exposed to many of the most important concepts in machine learning.

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.

What will I build in the course?

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.

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!

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.

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".

How do I get started?

You can read the materials on any device but this course is best viewed and coded along within a desktop browser.

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 and then come back here.

To start:

  1. Click on one of the notebook or section links above like "00. PyTorch Fundamentals".
  2. Click the "Open in Colab" button up the top.
  3. Press SHIFT+Enter a few times and see what happens.

My question isn't answered

Please leave a discussion or send me an email directly: daniel (at) mrdbourke (dot) com.

Log

Almost daily updates of what's happening.

pytorch-deep-learning (mrdbourke/pytorch-deep-learning)

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Pytorch-deep-learning
这是什么？Zero to Mastery Learn PyTorch for Deep Learning 课程的免费配套资料——一门以代码为主、注重动手实践的深度学习入门课。作者开玩笑说这是网上学 PyTorch 的第二好去处，仅次于官方文档。
我具体能拿到什么——书、视频还是代码？三种都有，围绕同样 10 个章节：learnpytorch.io 上的在线书、带练习、答案和幻灯片的 Jupyter notebook，还有视频——前五个章节在 YouTube 上免费提供，合成一个 25 小时的视频。
我该怎么开始学？从这个仓库打开任意章节的 notebook，点击顶部的 Open in Colab 按钮，用 SHIFT+Enter 运行单元格。没用过 Colab？README 里先链接了一个免费入门教程。
我需要有机器学习经验才能跟着学吗？这门课就是为机器学习新手设计的：大约 3-6 个月的 Python 编程经验，最好上过一门机器学习入门课（可跳过），再加上一些 Jupyter/Colab 的使用经验。如果你有一年以上的机器学习经验，帮助可能就不大了。
课程做完了吗，现在的 PyTorch 版本还能用吗？课程已经完结：10 个章节全部完成，共有 321 个视频、练习和答案。资料在 2023 年 4 月更新到了 PyTorch 2.0，而且这个版本向后兼容，所以内容依然有效。
我需要在自己电脑上安装 PyTorch 或 GPU 吗？不需要——所有内容都在浏览器里的 Google Colab 运行（桌面浏览器效果最好），本地什么都不用装。当然，用你自己电脑上的 Jupyter Notebook 也可以。
学完之后我到底能做出什么？你会从基础一路学到 FoodVision 相关的三个里程碑项目——FoodVision 是一个对食物图片进行分类的神经网络，涵盖分类、自定义数据集、迁移学习、实验跟踪、论文复现和模型部署。这些项目就是给你展示给雇主看的作品集。
视频内容有多少，在哪里看？总共有 321 个视频。前五个章节（从基础到自定义数据集）在 YouTube 上免费提供，是一个 25 小时的完整视频；完整课程也发布在 Zero to Mastery 和 Udemy 上。
有证书吗？如果学习视频版课程是有证书的，但作者自己都说证书一般般——真正的收获是你亲手写了成百上千行 PyTorch 代码，掌握了核心的机器学习概念。
如果我不想学完整课程，有没有更快的选择？有——附加资料里包括一个 PyTorch Cheatsheet notebook，快速概览主要功能；还有一个简短的 A Quick PyTorch 2.0 Tutorial；另外还有一份延伸书单和资源清单，涵盖深度学习、机器学习工程、NLP、时间序列和数据集。
What are similar projects?pytorch-handbook, fastbook, TensorFlow-Course, fastai, machine-learning-zoomcamp, DeepLearning, CV, ai-edu
Which topics is it in?colab, datasets, youtube, curriculum, dataset, course, neural-network, jupyter notebook, nlp, data-science, beginner-friendly, computer-vision
Creator
mrdbourke
Size
784.9 MB
Stars
19.2K
Updated
CommitSep 27
Age
5y
Commits · 90 Days