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Tsukistar
commented
Jun 3, 2024
Tsukistar
commented
Jun 3, 2024
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Also, I timed myself on each practice problem, as if I was in an interview. However, I used an IDE, with auto-completion and a debugger. You won’t get these in an actual interview, so I usually set a tighter time constraint when I practiced, at about 30 min/question, instead of 45 min. You may choose to mimic the real test environment by using a regular text editor only or even a whiteboard. It's up to your personal preference. | ||
同时,我在每个练习题上计时,就好像我在面试一样。但是,我使用的是带有自动完成和调试器的IDE。你不会在实际的面试中得到这些,所以我在练习时通常会把时间限制得更紧,每道题大约30分钟,而不是45分钟。你可以选择只使用常规的文本编辑器,甚至是白板来模拟真实的考试环境。这取决于你的个人喜好。 |
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我也会在每次做练习题的时候计时,就好像我在面试一样。但是我使用的是带有自动完成和调试器的IDE。
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auto-completion 自动补全
Hello @miyaliu666 ,我之前修改文件的时候好像改错了分支,所以我重新提交了一个PR,自己从前到后大概校对了一遍,后续麻烦啦 |
Tsukistar
commented
Jun 3, 2024
miyaliu666
reviewed
Jun 19, 2024
- 如果你想做移动端开发,尝试创建并发布一个苹果/安卓应用。请注意,在苹果的App Store上发布应用比在谷歌的Play Store上发布应用要花费更长的时间。 | ||
- 如果你对人工智能/机器学习感兴趣,尝试使用现有的计算机视觉或自然语言处理深度学习模型之一,并尝试从中制作产品。例如,我使用了一个预训练的[目标检测模型,SSD][10],并将其改编为我的DeepPiCar的交通标志检测器。如果您是AI / ML新手,请务必阅读[DeepPiCar的第一部分][11],在那里我列出了几个ML课程和资源,以帮助您开始学习。 | ||
- 如果你对机器人技术感兴趣,试着用树莓派或英伟达Jetson Nano制造一些很酷的机器人。一定要使用新的[Raspberry Pi 4][12]或[Jetson Nano][13]。树莓派更被制造商广泛使用,因此具有更好的技术支持,而Jetson Nano自带GPU,因此可能更适合[深度学习项目][14]。 | ||
- 如果你没有任何有关项目的想法,不要绝望。如果你对人工智能/机器学习感兴趣,你可以为上面提到的开源项目之一做出贡献,也可以为[TensorFlow][15]或[PyTorch][16]做出贡献。你可能认为您需要成为一名专家才能为这些知名的开源项目做出贡献,但实际上并不需要。每个开源项目中都有相当多的小特性和简单的错误修复是专门为新手准备的,他们可以优先发现这些,并做出相应的贡献。这样,你就可以在处理更复杂的问题之前熟悉代码库和提交流程。 |
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每个开源项目中都有相当多的小特性和简单的错误修复是专门为新手准备的,让新手可以先动手做。
谢谢 @Tsukistar 这么用心地翻译和校对这篇长文,译文非常好,我在发布时会编辑你修改的地方。 |
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Checklist:
Update index.md
)Closes #141