What is PyTorch and why should I learn it?
什么是 PyTorch，我为什么要学习它？

PyTorch is a machine learning and deep learning framework written in Python.
PyTorch 是一个用 Python 编写的机器学习和深度学习框架。

PyTorch enables you to craft new and use existing state-of-the-art deep learning algorithms like neural networks powering much of today’s Artificial Intelligence (AI) applications.
PyTorch 使您能够构建新的和使用现有的最先进的深度学习算法，例如为当今许多人工智能 （AI） 应用程序提供支持的神经网络。

Plus it’s so hot right now, so there’s lots of jobs available!
再加上现在天气太热了，所以有很多工作机会！

PyTorch is used by companies like:
PyTorch 被以下公司使用：

• Tesla to build the computer vision systems for their self-driving cars
  特斯拉将为其自动驾驶汽车构建计算机视觉系统

• Meta to power the curation and understanding systems for their content timelines
  Meta 为其内容时间线的策展和理解系统提供支持

• Apple to create computationally enhanced photography.
  苹果公司将创建计算增强的摄影作品。

Want to know what’s even cooler?
想知道什么更酷吗？

Much of the latest machine learning research is done and published using PyTorch code so knowing how it works means you’ll be at the cutting edge of this highly in-demand field.
许多最新的机器学习研究都是使用 PyTorch 代码完成和发布的，因此了解它的工作原理意味着您将处于这个需求量很大的领域的最前沿。

And you’ll be learning PyTorch in good company.
您将在良好的公司中学习 PyTorch。

Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify + other top tech companies at the forefront of machine learning and deep learning.
Zero To Mastery 的毕业生现在在谷歌、特斯拉、亚马逊、苹果、IBM、优步、Meta、Shopify + 其他处于机器学习和深度学习前沿的顶级科技公司工作。

This can be you. 这可以是你。

By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.
通过今天注册，您还可以加入我们独家的在线直播社区课堂，与成千上万的学生、校友、导师、助教和讲师一起学习。

Most importantly, you will be learning PyTorch from a professional machine learning engineer, with real-world experience, and who is one of the best teachers around!
最重要的是，您将从具有实际经验的专业机器学习工程师那里学习 PyTorch，并且是周围最好的老师之一！

What will this PyTorch course be like?
这个 PyTorch 课程会是什么样子？

This PyTorch course is very hands-on and project based. You won’t just be staring at your screen. We’ll leave that for other PyTorch tutorials and courses.
这门 PyTorch 课程非常注重实践和项目。你不会只是盯着你的屏幕。我们将把它留给其他 PyTorch 教程和课程。

In this course you’ll actually be:
在本课程中，您实际上将：

• Running experiments 运行实验

• Completing exercises to test your skills
  完成练习以测试您的技能

• Building real-world deep learning models and projects to mimic real life scenarios
  构建真实世界的深度学习模型和项目以模拟现实生活场景

By the end of it all, you’ll have the skillset needed to identify and develop modern deep learning solutions that Big Tech companies encounter.
到最后，您将拥有识别和开发大型科技公司遇到的现代深度学习解决方案所需的技能。

Fair warning: this course is very comprehensive. But don’t be intimidated, Daniel will teach you everything from scratch and step-by-step!
公平警告：本课程非常全面。但不要被吓倒，丹尼尔会从头开始一步一步地教你一切！

Here’s what you’ll learn in this PyTorch course:
以下是您将在此 PyTorch 课程中学到的内容：

1. PyTorch Fundamentals — We start with the barebone fundamentals, so even if you’re a beginner you’ll get up to speed.
1. PyTorch 基础知识 — 我们从准系统基础知识开始，因此即使您是初学者，您也会跟上速度。

In machine learning, data gets represented as a tensor (a collection of numbers). Learning how to craft tensors with PyTorch is paramount to building machine learning algorithms. In PyTorch Fundamentals we cover the PyTorch tensor datatype in-depth.
在机器学习中，数据被表示为张量（数字的集合）。学习如何使用 PyTorch 制作张量对于构建机器学习算法至关重要。在 PyTorch Fundamentals 中，我们深入介绍了 PyTorch 张量数据类型。

2. PyTorch Workflow — Okay, you’ve got the fundamentals down, and you’ve made some tensors to represent data, but what now?
2. PyTorch 工作流 — 好的，你已经掌握了基础知识，并且已经制作了一些张量来表示数据，但现在怎么办？

With PyTorch Workflow you’ll learn the steps to go from data -> tensors -> trained neural network model. You’ll see and use these steps wherever you encounter PyTorch code as well as for the rest of the course.
借助 PyTorch Workflow，您将了解从数据 – >张量 – >训练的神经网络模型的步骤。无论在遇到 PyTorch 代码的任何地方，以及本课程的其余部分，您都会看到并使用这些步骤。

3. PyTorch Neural Network Classification — Classification is one of the most common machine learning problems.
3. PyTorch 神经网络分类 — 分类是最常见的机器学习问题之一。

• Is something one thing or another?
  某事是一回事还是另一回事？

• Is an email spam or not spam?
  电子邮件是垃圾邮件还是不是垃圾邮件？

• Is credit card transaction fraud or not fraud?
  信用卡交易是欺诈还是非欺诈？

With PyTorch Neural Network Classification you’ll learn how to code a neural network classification model using PyTorch so that you can classify things and answer these questions.
借助 PyTorch 神经网络分类，您将学习如何使用 PyTorch 对神经网络分类模型进行编码，以便对事物进行分类并回答这些问题。

4. PyTorch Computer Vision — Neural networks have changed the game of computer vision forever. And now PyTorch drives many of the latest advancements in computer vision algorithms.
4. PyTorch 计算机视觉 — 神经网络永远改变了计算机视觉的游戏规则。现在，PyTorch 推动了计算机视觉算法的许多最新进展。

For example, Tesla use PyTorch to build the computer vision algorithms for their self-driving software.
例如，特斯拉使用PyTorch为其自动驾驶软件构建计算机视觉算法。

With PyTorch Computer Vision you’ll build a PyTorch neural network capable of seeing patterns in images of and classifying them into different categories.
借助 PyTorch 计算机视觉，您将构建一个 PyTorch 神经网络，该神经网络能够查看图像中的模式并将其分类为不同的类别。

5. PyTorch Custom Datasets — The magic of machine learning is building algorithms to find patterns in your own custom data. There are plenty of existing datasets out there, but how do you load your own custom dataset into PyTorch?
5. PyTorch 自定义数据集 — 机器学习的魔力在于构建算法以在您自己的自定义数据中查找模式。市面上有很多现有的数据集，但是如何将自己的自定义数据集加载到 PyTorch 中呢？

This is exactly what you’ll learn with the PyTorch Custom Datasets section of this course.
这正是您将在本课程的 PyTorch 自定义数据集部分学到的内容。

You’ll learn how to load an image dataset for FoodVision Mini: a PyTorch computer vision model capable of classifying images of pizza, steak and sushi (am I making you hungry to learn yet?!).
您将学习如何为 FoodVision Mini 加载图像数据集：一个能够对比萨饼、牛排和寿司的图像进行分类的 PyTorch 计算机视觉模型（我让你渴望学习吗？！

We’ll be building upon FoodVision Mini for the rest of the course.
我们将在课程的其余部分以FoodVision Mini为基础。

6. PyTorch Going Modular — The whole point of PyTorch is to be able to write Pythonic machine learning code.
6. PyTorch 走向模块化 — PyTorch 的全部意义在于能够编写 Pythonic 机器学习代码。

There are two main tools for writing machine learning code with Python:
使用 Python 编写机器学习代码有两个主要工具：

1. A Jupyter/Google Colab notebook (great for experimenting)
   Jupyter/Google Colab 笔记本（非常适合实验）

2. Python scripts (great for reproducibility and modularity)
   Python 脚本（非常适合可重复性和模块化）

In the PyTorch Going Modular section of this course, you’ll learn how to take your most useful Jupyter/Google Colab Notebook code and turn it reusable Python scripts. This is often how you’ll find PyTorch code shared in the wild.
在本课程的 PyTorch Going Modular 部分，您将学习如何获取最有用的 Jupyter/Google Colab Notebook 代码并将其转换为可重用的 Python 脚本。这通常是你找到在野外共享的 PyTorch 代码的方式。

7. PyTorch Transfer Learning — What if you could take what one model has learned and leverage it for your own problems? That’s what PyTorch Transfer Learning covers.
7. PyTorch 迁移学习 — 如果你能利用一个模型学到的东西，并利用它来解决你自己的问题，会怎么样？这就是 PyTorch 迁移学习所涵盖的内容。

You’ll learn about the power of transfer learning and how it enables you to take a machine learning model trained on millions of images, modify it slightly, and enhance the performance of FoodVision Mini, saving you time and resources.
您将了解迁移学习的强大功能，以及它如何使您能够采用在数百万张图像上训练的机器学习模型，对其进行轻微修改，并增强 FoodVision Mini 的性能，从而节省您的时间和资源。

8. PyTorch Experiment Tracking — Now we’re going to start cooking with heat by starting Part 1 of our Milestone Project of the course!
8. PyTorch 实验跟踪 — 现在我们将通过开始课程里程碑项目的第 1 部分来开始用热烹饪！

At this point you’ll have built plenty of PyTorch models. But how do you keep track of which model performs the best?
此时，你将构建大量 PyTorch 模型。但是，您如何跟踪哪个模型表现最好呢？

That’s where PyTorch Experiment Tracking comes in.
这就是 PyTorch 实验跟踪的用武之地。

Following the machine learning practitioner’s motto of experiment, experiment, experiment! you’ll setup a system to keep track of various FoodVision Mini experiment results and then compare them to find the best.
遵循机器学习从业者的座右铭：实验、实验、实验！您将设置一个系统来跟踪各种 FoodVision Mini 实验结果，然后进行比较以找到最佳结果。

9. PyTorch Paper Replicating — The field of machine learning advances quickly. New research papers get published every day. Being able to read and understand these papers takes time and practice.
9. PyTorch Paper Replicating — 机器学习领域发展迅速。每天都有新的研究论文发表。能够阅读和理解这些论文需要时间和练习。

So that’s what PyTorch Paper Replicating covers. You’ll learn how to go through a machine learning research paper and replicate it with PyTorch code.
这就是 PyTorch Paper Replicating 所涵盖的内容。您将学习如何阅读机器学习研究论文并使用 PyTorch 代码进行复制。

At this point you’ll also undertake Part 2 of our Milestone Project, where you’ll replicate the groundbreaking Vision Transformer architecture!
在这一点上，您还将承担我们里程碑项目的第 2 部分，在那里您将复制突破性的 Vision Transformer 架构！

10. PyTorch Model Deployment — By this stage your FoodVision model will be performing quite well. But up until now, you’ve been the only one with access to it.
10. PyTorch 模型部署 — 到此阶段，您的 FoodVision 模型将表现良好。但到目前为止，您是唯一可以访问它的人。

How do you get your PyTorch models in the hands of others?
如何将 PyTorch 模型交到他人手中？

That’s what PyTorch Model Deployment covers. In Part 3 of your Milestone Project, you’ll learn how to take the best performing FoodVision Mini model and deploy it to the web so other people can access it and try it out with their own food images.
这就是 PyTorch 模型部署所涵盖的内容。在里程碑项目的第 3 部分中，您将学习如何采用性能最佳的 FoodVision Mini 模型并将其部署到网络上，以便其他人可以访问它并使用自己的食物图像进行试用。

What’s the bottom line? 底线是什么？

Machine learning’s growth and adoption is exploding, and deep learning is how you take your machine learning knowledge to the next level. More and more job openings are looking for this specialized knowledge.
机器学习的增长和采用呈爆炸式增长，而深度学习是您将机器学习知识提升到新水平的方式。越来越多的职位空缺正在寻找这种专业知识。

Companies like Tesla, Microsoft, OpenAI, Meta (Facebook + Instagram), Airbnb and many others are currently powered by PyTorch.
特斯拉、Microsoft、OpenAI、Meta （Facebook + Instagram）、Airbnb 等公司目前都由 PyTorch 提供支持。

And this is the most comprehensive online bootcamp to learn PyTorch and kickstart your career as a Deep Learning Engineer.
这是学习 PyTorch 并开始您作为深度学习工程师的职业生涯的最全面的在线训练营。

So why wait? Advance your career and earn a higher salary by mastering PyTorch and adding deep learning to your toolkit?
那为什么还要等呢？通过掌握 PyTorch 并将深度学习添加到您的工具包中来提升您的职业生涯并获得更高的薪水？