高中生ai智能产品_作为一名高中生如何学习人工智能

With AI growing faster than ever, many students and developers are starting to enter the field. However, many high schoolers who are interested in a future in computer science want to learn AI but are pushed away by the highly complex concepts. Lots of high schoolers are learning how to code these days through online courses on Khan Academy or AP Computer, but creating AI projects is a whole different ballgame.

随着AI的发展比以往更快，许多学生和开发人员开始进入这一领域。 但是，许多对计算机科学的未来感兴趣的高中生想学习AI，但却被高度复杂的概念所取代。 如今，许多高中生正在通过可汗学院或AP Computer上的在线课程来学习如何进行编码，但是创建AI项目却完全是一回事。

I faced the same dilemma. At the start of my sophomore year, I had a pretty strong understanding of coding, as I knew Java and some Swift, but I barely knew what AI was. After going to a hackathon and creating an AI project just by following some documentation, not really knowing what I was doing, I was hooked. It took almost six more months for me to actually learn what I was doing, but looking back, there are a few key steps to take in order to learn AI as a high schooler.

我面临着同样的困境。 在我大二的开始，我对编码非常了解，因为我知道Java和一些Swift，但是我几乎不知道什么是AI。 在参加黑客马拉松并仅通过阅读一些文档而不是真正了解自己的工作来创建AI项目之后，我就迷上了。 我花了将近六个月的时间才真正了解到自己在做什么，但是回想起来，要想成为一名高中生来学习AI，还需要采取一些关键步骤。

Learning AI as a high schooler can build fundamentals that will give you a leg up in college or your career, so if you are interested, don’t pass up the opportunity. Just follow a few basic steps to make sure you can digest it all.

作为高中生学习AI可以建立基础知识，这将使您在大学或职业生涯中一臂之力，因此，如果您有兴趣，请不要错过机会。 只需遵循一些基本步骤，以确保您可以消化所有内容。

1.学习Python (1. Learn Python)

Learning or brushing up on your Python skills will be the base for learning AI and ML. There are almost no other languages that AI is actually coded in, and without knowing the basic syntax of Python, you will get caught up in trying to fix your syntax errors when you could be debugging actual AI bugs instead.

学习或精通Python技能将是学习AI和ML的基础。 实际上，几乎没有其他语言可以使用AI进行编码，而且如果不了解Python的基本语法，当您可能正在调试实际的AI错误时，就会陷入尝试修复语法错误的困境。

I am going to assume that if you want to learn AI, you understand basic coding concepts. If you don’t, learn those first! Without knowing how to code, you cannot learn AI — let alone create AI projects. But if you know Java, JavaScript, or even Swift, you will be fine. Just take a quick course on Python syntax and you will be fine. Any online course or playlist will do the trick. This is step 1, though. Without knowing Python, you will definitely struggle.

我将假设，如果您想学习AI，您将了解基本的编码概念。 如果不这样做，请先学习！ 如果不知道如何编码，就无法学习AI，更不用说创建AI项目了。 但是，如果您知道Java，JavaScript甚至Swift，那么您会没事的。 只需快速学习Python语法，就可以了。 任何在线课程或播放列表都可以解决问题。 不过，这是第1步。 不了解Python，您肯定会挣扎。

2. 从传统机器学习开始 (2. Start With Traditional ML)

When I started learning AI, one of my mistakes was jumping straight into deep learning and building neural networks. This can be a common problem for high schoolers since many get caught up in the hype of deep learning. A few months into learning AI, I knew how to code a neural network, but I had no idea what linear regression was. This is a problem because learning fundamentals is always better than skipping straight to more advanced topics.

当我开始学习AI时，我的错误之一就是直接跳入深度学习和构建神经网络。 对于高中生来说，这可能是一个常见问题，因为许多人陷入了深度学习的热潮中。 学习AI几个月后，我知道如何对神经网络进行编码，但我不知道什么是线性回归。 这是一个问题，因为学习基础知识总是比直接跳到更高级的主题更好。

Truly understanding deep learning requires a strong understanding of standard machine learning, and thus all high schoolers should make sure they take a ground-up approach instead of overextending themselves.

真正了解深度学习需要对标准机器学习有深入的了解，因此所有高中生都应确保他们采用一种全面的方法，而不是过度扩张自己。

I would recommend that high schoolers start with basic linear and logistic regression models with common datasets to really understand how machine learning works. Linear regression is basically a “line of best fit,” which almost all high schoolers are familiar with. Linear regression very simply explains how loss functions work, which are one of the most fundamental concepts of AI and deep learning. I think learning other algorithms like decision trees and random forests might not be as useful since they don’t translate as well to deep learning, which most high school students seem interested in.

我建议高中生从基本线性和逻辑回归模型与常见数据集入手，以真正了解机器学习的工作原理。 线性回归基本上是几乎所有高中生都熟悉的“最佳拟合线”。 线性回归非常简单地解释了损失函数的工作原理，这是人工智能和深度学习的最基本概念之一。 我认为学习决策树和随机森林之类的其他算法可能没有用，因为它们不能很好地转化为深度学习，大多数高中生似乎对此感兴趣。

Regardless, if you want to really get into AI and DL, learn the fundamentals first. It is not worth stunting your long-term development just to show that you know DL.

无论如何，如果您想真正地学习AI和DL，请先学习基础知识。 仅仅表明您了解DL并不能阻止您的长期发展。

3. 不用担心数学 (3. Don’t Worry About the Math)

I think the biggest barrier in students’ eyes is thinking they have to learn the math behind AI and DL. I just started taking calculus this year as a junior, and I still don’t know a lot of the math for AI and DL. I think understanding math is overrated. You can still create great projects and understand enough about AI without knowing how gradient descent and backpropagation work. All you really need to know to create good models is that backpropagation optimizes the weights in a model.

我认为，在学生眼中最大的障碍是认为他们必须学习AI和DL背后的数学知识。 我从大三开始才开始学习微积分，但我仍然不了解AI和DL的很多数学知识。 我认为对数学的理解被高估了。 您仍然可以创建出色的项目并充分了解AI，而无需了解梯度下降和反向传播的工作原理。 创建良好模型时，您真正需要知道的就是反向传播可以优化模型中的权重。

Obviously, knowing calculus and advanced math will eventually be important, but for high schoolers, I think just knowing Algebra II is enough.

显然，了解微积分和高级数学最终将很重要，但对于高中生来说，我认为仅了解Algebra II就足够了。

If you know calculus and are able to understand the math behind deep learning, most definitely learn it. But if you don’t, don’t stress about it. You can still have a strong understanding of how deep learning works. If you stay persistent and continue to stay in the field, you will eventually learn the math — either by the end of high school or early in college.

如果您了解微积分并能够理解深度学习背后的数学，则绝对可以学习。 但是，如果您不这样做，请不要过分强调。 您仍然可以对深度学习的工作原理有深刻的了解。 如果您坚持不懈并继续留在野外，最终您将学习数学-高中毕业或大学早期学习。

4. 使用Jupyter Notebook / Google Colab (4. Use Jupyter Notebook/Google Colab)

Another important tip is to code all your projects in a notebook format. Jupyter Notebooks are a data science tool that contains both code and text in a cell format and allows for instant compiling and saving of notes and results. For starters, notebooks and their markdown features in between cells can make each segment of code clearer and easier to comprehend. When you go back to review what you have coded, markdown allows for intuitive organization and easy revision of notes or concepts you have written down with the code. Comments in scripts may be adequate, but they are hard to keep organized, and writing lots of notes for segments of code is really hard since it just becomes messy.

另一个重要提示是以笔记本格式对所有项目进行编码。 Jupyter Notebooks是一种数据科学工具，其中包含单元格格式的代码和文本，并允许即时编译和保存注释和结果。 对于初学者来说，笔记本及其在单元格之间的减价功能可以使每个代码段更清晰，更易于理解。 当您回头查看编码内容时，markdown可以使您直观地组织代码，并轻松修改使用该代码编写的注释或概念。 脚本中的注释可能足够了，但是很难保持井井有条，并且为代码段编写大量注释真的很困难，因为它变得很混乱。

Most high schoolers are used to taking notes in class, so using a Juypter Notebook will be a familiar experience for learning new concepts.

大多数高中生习惯于在课堂上做笔记，因此使用Juypter Notebook将是学习新概念的熟悉体验。

Notebooks also allow for instant testing and feedback. This is great for high schoolers and beginners, as you will usually have errors in your code. Being able to quickly write down what the error was in markdown, edit a line, and then press the run shortcut simplifies the process.

笔记本还可以进行即时测试和反馈。 这对高中生和初学者来说非常有用，因为您的代码通常会出现错误。 能够快速写下降价错误的原因，编辑一行，然后按运行快捷方式简化了该过程。

Moreover, notebooks allow for visualization of code and data, and for high schoolers who might not be great at digesting hard numbers and data, notebooks make the process much easier. Lastly, using Google Colab allows for free GPU use, and most high schoolers don’t have local GPUs or university-level compute, so using the free GPU in Colab allows for easy DL training.

此外，笔记本允许对代码和数据进行可视化，而对于可能不擅长提取硬数字和数据的高中生，笔记本使此过程变得更加容易。 最后，使用Google Colab可以免费使用GPU，而且大多数高中生没有本地GPU或大学级别的计算，因此在Colab中使用免费的GPU可以轻松进行DL培训。

5. 继续编码项目 (5. Continue Coding Projects)

This might seem obvious, but I cannot stress how important practice is. Learning theory is important, but without putting it into practice, there really isn’t value in learning AI so early in your life. Creating a project creates a sense of validation and pride, as you can take what you have learned and see its manifestation. Moreover, functional and valuable projects can be displayed on GitHub and in portfolios, which can help you show off your work. Most importantly, coding a project always refreshes what you have learned, and just like with any other class in high school, without doing practice problems, you will forget what you learned and will have to waste time relearning it.

这似乎很明显，但是我不能强调实践的重要性。 学习理论很重要，但是如果不付诸实践，那么这么早就学习AI确实没有任何价值。 创建项目会产生一种验证和自豪感，因为您可以学到的东西并查看其表现形式。 此外，功能强大且有价值的项目可以在GitHub和项目组合中显示，这可以帮助您炫耀您的工作。 最重要的是，对项目进行编码始终可以刷新您所学的知识，就像在高中的任何其他班级一样，在没有练习问题的情况下，您会忘记所学的知识，并且不得不浪费时间来重新学习它。

Create anything you wish — maybe go to a hackathon or participate in a Kaggle Competition or even begin your own AI startup. Just make sure you are putting what you learn into practice.

创建您想要的任何东西-可能参加黑客马拉松或参加Kaggle竞赛，甚至开始自己的AI创业公司。 只需确保将所学内容付诸实践即可。

结论 (Conclusion)

At the end of the day, you can’t really learn much about AI as a high schooler. I code AI projects almost every day and have been doing so for almost a year, but I still have barely scratched the surface of the field. Learning AI as a high schooler should be a fun experience. Don’t burn yourself out. Practicing concepts and creating fun projects keeps you engaged, and when you are in a field that is so rigorous, making sure you enjoy what you are doing is the best practice. Take breaks and don’t push yourself beyond your limits. Just because your projects aren’t as good as those of a professional data scientist doesn’t mean what you are doing isn’t valuable.

归根结底，作为一名高中生，您对AI的了解并不多。 我几乎每天都在编写AI项目代码，并且已经进行了将近一年的时间，但是我仍然几乎没有涉足这一领域。 作为高中生学习AI应该是一种有趣的体验。 不要让自己筋疲力尽。 练习概念和创建有趣的项目可保持您的参与度，而当您在一个如此严格的领域中时，确保您喜欢自己正在做的事情是最佳实践。 休息一下，不要超越自己的极限。 仅仅因为您的项目不如专业数据科学家的项目好，并不意味着您正在做的事情没有价值。

I applaud you if you are a high schooler like me and want to learn AI. It’s not easy, but if you are up for the challenge, just make sure you follow these basic guidelines. They’ll make your experience much easier and more enriching.

如果您是像我这样的高中生，并且想学习AI，我会为您鼓掌。 这并不容易，但是如果您准备好迎接挑战，只需确保遵循这些基本准则即可。 它们将使您的体验更加轻松和丰富。