数据科学 (Data Science)

Success in data science and software engineering depends on our ability to continuously learn new models and concepts.

数据科学和软件工程的成功取决于我们不断学习新模型和概念的能力。

Both domains are infinitely large umbrellas of nested ideas.

这两个领域都是嵌套思想的无限大伞。

While you can spend decades in a specific branch of NLP, many of use are generalists and need to constantly acquire new knowledge.

尽管您可以在NLP的特定分支上花费数十年的时间，但许多用途是通才，需要不断获取新知识。

Here’s my framework for doing this quickly.

这是我快速执行此操作的框架。

不要以数学或科学论文开头 (Do not start with math or scientific papers)

Math is overrated.

数学被高估了。

这对两件事很有用： (It’s useful for 2 things:)

1. It gives an intuition for how things work.
   它为事物的工作方式提供了直觉。
2. It allows coding algorithms without using an existing package
   它允许编码算法而无需使用现有程序包

轶事： (Anecdotally:)

The former is useful when tuning models.

前者在调整模型时很有用。

The later is useful when I need an algorithm in a Ruby but packages supporting it only exist in Python.

当我需要Ruby中的算法但是支持它的程序包仅存在于Python中时，后者很有用。

Math is never the first step.

数学绝不是第一步。

观看youtube视频，以高水平掌握它 (Watch a youtube video to grasp it at a high level)

Watch a couple videos. You can find them covering most concepts.

观看几个视频。 您会发现它们涵盖了大多数概念。

Don’t try to understand specific technicalities at this point. Just develop a high level idea of what it does and how it’s different from related concepts.

此时不要尝试了解特定的技术。 只要对它的功能以及与相关概念的不同之处有一个高级的了解即可。

Videos are great because images cut through the technical jargon that often makes written explanations seem more complicated than reality.

视频之所以如此出色，是因为图像突破了技术行话，往往使书面说明显得比现实复杂。

Get to the point where you can describe the concept in a few sentences. At this point, it’s useful imagining that you’re explaining it to a non-technical colleague.

到达可以用几句话描述这个概念的地步。 在这一点上，想象您正在向非技术同事解释它很有用。

使一些代码正常工作 (Get some code working)

Find a code snippet online.

在线查找代码段。

You can often google “concept” + “python tutorial” to find code. Otherwise, find a related library and review the docs, or search related tags on Stack Overflow.

您通常可以通过Google“概念” +“ Python教程”来查找代码。 否则，找到一个相关的库并查看文档，或在Stack Overflow上搜索相关的标签。

Expect to wrestle with conflicting packages and APIs that differ between versions at this point. Always use a virtual environment to keep your machine clean!

期望此时解决版本之间不同的冲突软件包和API。 始终使用虚拟环境来保持机器清洁！

Get some code working. Change variables. Break it.

使一些代码正常工作。 更改变量。 打破它。

Look at how the data changes at each step.

查看数据在每个步骤中如何变化。

You’re laying the groundwork that you’ll mentally attach a deeper understanding to later.

您正在奠定基础，以后将在头脑上加深了解。

分解概念中的步骤 (Break down the steps in the concept)

Go back to youtube.

返回youtube。

Write out its steps on paper. Draw a flowchart and revise it as you better understand the concept.

在纸上写下它的步骤。 绘制流程图并对其进行修改，以使您更好地理解该概念。

High-level — what components and different steps occur?

高级别-发生了哪些组件和不同的步骤？

Isolate the setup (preprocessing) from the model itself that you’re learning.

从您正在学习的模型本身中隔离设置(预处理)。

(可选)复习数学并阅读论文 ((Optional) Review the math and read papers)

Dig into each step.

深入每一步。

Understand the math.

了解数学。

This is most beneficial if you already have experience with adjacent concepts. Reviewing an advanced paper in a domain you have no experience with will sink A LOT of time.

如果您已经有相邻概念的经验，这将是最有益的。 在您没有经验的领域中审阅高级论文会浪费很多时间。

Now lay the math on top of your previous steps. Again, Khan Academy and YouTube can be helpful here.

现在，将数学放在您之前的步骤之上。 同样，可汗学院和YouTube在这里可能会有所帮助。

我通常仅在以下情况下采取此步骤： (I typically only take this step if:)

1. An MVP (base-case) is complete and is ready to be optimized
   MVP(基本情况)已完成，可以进行优化了
2. It’s genuinely interesting
   真的很有趣
3. I need to evaluate tradeoffs between technical options
   我需要评估技术选择之间的权衡

用它建造东西 (Build something with it)

Use it or lose it.

使用它或失去它。

Personally, I don’t remember anything I only read about.

就个人而言，我不记得我只读过的任何东西。

Apply it to your own use-case. The important piece here is to apply it in a completely different situation than the example you learned it on.

将其应用于您自己的用例。 这里重要的一点是将它应用于与您所学到的示例完全不同的情况。

While reviewing the landscape of concepts in a domain is useful, it’ll be more readily available to you (when you need it) if you’ve applied it previously.

回顾领域中概念的概况很有用，但是如果您之前已经应用过它，那么在需要时将更容易使用。

Build something simple.

构建简单的东西。

结论 (Conclusion)

That’s it.

而已。

Additionally, learn things adjacent to what you already know. Foundations are underrated. You can’t learn calculus without basic arithmetic.

此外，学习与您已经知道的知识相邻的事物。 基金会被低估了。 没有基本的算术就无法学习微积分。

Now go learn some stuff. And use it (for good)!

现在去学习一些东西。 并使用它(永远)！