【人工智能】是一种强大的工具，但它并不是解决所有问题的银弹。
成为一名出色的【机器学习】工程师需要具备坚实的【数学】和【编程】技能。
在【机器学习】项目中，【数据的质量】比算法的选择更为重要。
【持续学习】和【不断实践】是成为一名成功的【机器学习】从业者的关键。 --- 知行合一
【人工智能】可以为社会带来巨大的价值，但必须在保护隐私和安全的前提下使用。
AI技术的应用必须注重伦理和社会责任，以确保其不会对人类产生负面影响。 --- 这个很难讲，如同 质能方程 一样
与其他【机器学习】从业者和领域专家建立联系和合作，可以促进个人和行业的发展。 --- 融入圈子
了解业界最新的技术和趋势，对于在【机器学习】领域保持竞争力至关重要。
AI is the new electricity. It will transform and improve all areas of human life.
【人工智能】是新的电力。它将改变和改善人类生活的所有领域。
Coding AI Is the New LiteracyToday we take it for granted that many people know how to read and write. Someday, I hope, it will be just as common that people know how to write code, specifically for AI.
今天我们认为许多人都知道如何阅读和写作是理所当然的。我希望有一天，人们知道如何编写特别是为AI编写代码也会变得像这样普遍。
Several hundred years ago, society didn’t view language literacy as a necessary skill. A small number of people learned to read and write, and everyone else let them do the reading and writing. It took centuries for literacy to spread, and now society is far richer for it.
几百年前，社会并没有将语言读写视为必要的技能。只有少数人学会了阅读和写作，其他人就让他们来阅读和写作。阅读和写作的普及花费了数个世纪的时间，而现在社会因此更加丰富多彩。
Words enable deep human-to-human communication. Code is the deepest form of human-to-machine communication. As machines become more central to daily life, that communication becomes ever more important.
文字使得人际交流加深成为可能。代码是人与机器之间最深层次的交流形式。随着机器越来越成为日常生活的核心，这种交流变得愈发重要。
Traditional software engineering — writing programs that explicitly tell a computer sequences of steps to execute — has been the main path to code literacy. Many introductory programming classes use creating a video game or building a website as examples. But AI, machine learning, and data science offer a new paradigm in which computers extract knowledge from data. This technology offers an even better pathway to coding.
传统的软件工程——编写明确告诉计算机执行步骤的程序——一直是通向代码能力的主要途径。许多入门编程课程使用创建视频游戏或构建网站作为示例。但是，【人工智能】、【机器学习】和数据科学提供了一种新的范式，让计算机从数据中提取知识。并为该技术提供了更好的编码实践。
Many Sundays, I buy a slice of pizza from my neighborhood pizza parlor. The gentleman behind the counter has little reason to learn how to build a video game or write his own website software (beyond personal growth and the pleasure of gaining a new skill). But AI and data science have great value even for a pizza maker. A linear regression model might enable him to better estimate demand so he can optimize the restaurant’s staffing and supply chain. He could better predict sales of Hawaiian pizza — my favorite! — so he could make more Hawaiian pies in advance and reduce the amount of time customers had to wait for them.
许多个星期天，我会从我的社区披萨店买一片披萨。柜台后面的那位先生没有太多理由学习如何构建视频游戏或编写自己的网站软件（除了个人成长和获得新技能的乐趣）。但是对于披萨师傅来说，【人工智能】和数据科学也有很大的价值。【线性回归】模型可能会使他更好地估计需求，因此他可以优化餐厅的人员配备和供应链。他可以更好地预测夏威夷披萨（我的最爱！
）的销售情况，以便提前制作更多夏威夷披萨并减少客户等待时间。Uses of AI and data science can be found in almost any situation that produces data. Thus, a wide variety of professions will find more uses for custom AI applications and data-derived insights than for traditional software engineering. This makes literacy in AI-oriented coding even more valuable than traditional coding. It could enable countless individuals to harness data to make their lives richer.
几乎任何产生数据的情况都可以找到使用【人工智能】和数据科学的方法。因此，各种职业将会比传统的软件工程更多地利用定制的【人工智能】应用和基于数据的洞察力。这使得面向AI编码的读写能力比传统编码更有价值。它通过利用数据使每个人的生活更加丰富。
I hope the promise of building basic AI applications, even more than that of building basic traditional software, encourages more people to learn how to code. If society embraces this new form of literacy as it has the ability to read and write, we will all benefit.
我希望，构建基本的【AI应用程序】甚至比构建【传统应用软件】更能鼓励更多的人们学习编程。如果大众像掌握阅读和写作能力一样接受这种新的文化素养，我们所有人都将受益。
Three Steps to Career GrowthThe rapid rise of AI has led to a rapid rise in AI jobs, and many people are building exciting careers in this field. A career is a decades-long journey, and the path is not straightforward. Over many years, I’ve been privileged to see thousands of students, as well as engineers in companies large and small, navigate careers in AI. Here’s a framework for charting your own course.
【人工智能】的快速崛起导致了【人工智能】工作的机会与岗位快速增加，许多人正在这个领域建立令人兴奋的职业生涯。职业生涯是一个长达几十年的旅程，道路并非一路坦途。多年来，我有幸见证了成千上万的学生以及各大大小小公司的工程师在【人工智能】领域的职业生涯。以下是规划你自己职业生涯的框架。
Three key steps of career growth are learning foundational skills, working on projects (to deepen your skills, build a portfolio, and create impact), and finding a job. These steps stack on top of each other:
职业成长的三个关键步骤是学习基础技能、开展项目（以加深你的技能、建立作品集和创造影响力），以及入职一份工作。这些步骤相互叠加：
最初，你需要专注于学习基础技能。书本内容涵盖了学习基础技术技能的主题。成为主要的学习来源。
获得了基础技术技能后，你将开始开展项目。在此期间，你还将继续学习。本章聚焦于项目。
后来，你将着手找工作。在整个过程中，你将继续学习并开展有意义的项目。本章聚焦于求职。
These phases apply in a wide range of professions, but AI involves unique elements.
这些阶段适用于广泛的职业领域，但是AI领域的职业有一些独特性：
【学习】学习基础技能是一个职业生涯长期的过程：
AI是新生的领域，许多技术仍在不断发展中。尽管【机器学习】和【深度学习】的基础已经成熟，而且课程是掌握它们的有效方法，但在这些基础之外，跟上不断变化的技术对于AI比更成熟的领域更为重要。---- 活到老 学到老
【项目】工作在项目中通常意味着与缺乏AI专业知识的利益相关者合作：
这可能会使寻找合适的项目、估计项目的时间表和投资回报以及设定期望变得具有挑战性。此外，AI项目的高度迭代特性也带来了项目管理上的特殊挑战：当您无法预知达到目标准确度需要多长时间时，如何制定构建系统的计划？即使系统已经达到目标，后续迭代可能仍然是必要的，以应对部署后的漂移。
【工作】AI技能和职业角色的看法不一致：
虽然在AI中寻找工作可能类似于在其他领域寻找工作，但也存在重要的区别。许多公司仍在努力确定他们需要哪些AI技能，以及如何雇佣拥有这些技能的人。您的工作经历可能与面试官看到的任何内容都不同，您更有可能需要向潜在雇主介绍您的一些工作要素。
As you go through each step, you should also build a supportive community. Having friends and allies who can help you — and who you strive to help — makes the path easier. This is true whether you’re taking your first steps or you’ve been on the journey for years.
在每个步骤中，您应该建立一个支持性社区。有能够帮助您的朋友和盟友，以及您努力帮助的人，会使道路变得更加容易。无论您是第一次迈出步伐还是已经走了多年，这都是正确的选择。（互帮互助，帮助别人 也是在 加深理解）
Learning Technical Skills for a Promising AI CareerIn the previous chapter, I introduced three key steps for building a career in AI: learning foundational technical skills, working on projects, and finding a job, all of which is supported by being part of a community. In this chapter, I’d like to dive more deeply into the first step: learning foundational skills.
在上一章中，我介绍了在AI领域建立职业生涯的三个关键步骤：学习基础技术技能，参与项目以及找工作。在本章中，我想更深入地探讨第一步：学习基础技能。
More research papers have been published on AI than anyone can read in a lifetime. So, when learning, it’s critical to prioritize topic selection. I believe the most important topics for a technical career in machine learning are:
AI方面的研究论文数量已经超出了任何人一生能够阅读的范围。因此，在学习时，选择优先学习的主题非常关键。我认为，对于【机器学习】技术职业而言，最重要的主题有如下几方面：
Foundational machine learning skills: For example, it’s important to understand models such as linear regression, logistic regression, neural networks, decision trees, clustering, and anomaly detection. Beyond specific models, it’s even more important to understand the core concepts behind how and why machine learning works, such as bias/variance, cost functions, regularization, optimization algorithms, and error analysis.
【机器学习】技能：例如，理解诸如线性回归、逻辑回归、神经网络、决策树、聚类和异常检测等模型是很重要的。除了具体的模型，更重要的是理解【机器学习】的核心概念，例如偏差/方差、代价函数、正则化、优化算法和误差分析等。
Deep learning: This has become such a large fraction of machine learning that it’s hard to excel in the field without some understanding of it! It’s valuable to know the basics of neural networks, practical skills for making them work (such as hyperparameter tuning), convolutional networks, sequence models, and transformers.
【深度学习】技能：这已经成为【机器学习】中很大的一部分，没有一定的了解是很难在这个领域中脱颖而出的！
了解神经网络的基础知识，掌握实用技能（例如超参数调整）、卷积网络、序列模型和变换器，都是非常有价值的。【机器学习】相关的【数学】知识：主要包括线性代数（向量、矩阵及其各种操作）以及概率论和统计学（包括离散和连续概率、标准概率分布、独立性和贝叶斯法则等基本规则和假设检验）。此外，利用可视化和其他方法系统地探索数据集的探索性数据分析（EDA）是一个被低估的技能。我发现在数据为中心的【人工智能】开发中，通过分析错误和获取洞察力，EDA特别有用！
最后，基本的微积分直觉性理解也有帮助。做好【机器学习】所需的【数学】知识正在发生变化。例如，尽管某些任务需要微积分，但改进的自动微分软件使得可以在不做任何微积分的情况下发明和实现新的神经网络架构。这在十年前几乎是不可能的。Software development: While you can get a job and make huge contributions with only machine learning modeling skills, your job opportunities will increase if you can also write good software to implement complex AI systems. These skills include programming fundamentals, data structures (especially those that relate to machine learning, such as data frames), algorithms (including those related to databases and data manipulation), software design, familiarity with Python, and familiarity with key libraries such as TensorFlow or PyTorch, and scikit-learn.
编程技能：虽然只具备【机器学习】建模技能就可以得到工作并做出巨大贡献，但如果你能编写出实现复杂AI系统的良好软件，你的就业机会将会增加。这些技能包括编程基础、数据结构（特别是与【机器学习】相关的数据框架）、算法（包括与数据库和数据操作相关的算法）、软件设计、熟悉Python以及熟悉关键库，如TensorFlow或PyTorch和Scikit-learn。
（Java 在这里没有 根基）This is a lot to learn! Even after you master everything on this list, I hope you’ll keep learning and continue to deepen your technical knowledge. I’ve known many machine learning engineers who benefitted from deeper skills in an application area such as natural language processing or computer vision, or in a technology area such as probabilistic graphical models or building scalable software systems.
这些都是需要学习的范畴！
即使你掌握了列表中的所有内容，我希望你能继续学习并深入技术知识。我认识很多【机器学习】工程师，在应用领域（如自然语言处理或计算机视觉）或技术领域（如概率图模型或构建可扩展软件系统）深入学习技能后受益匪浅。How do you gain these skills? There’s a lot of good content on the internet, and in theory, reading dozens of web pages could work. But when the goal is deep understanding, reading disjointed web pages is inefficient because they tend to repeat each other, use inconsistent terminology (which slows you down), vary in quality, and leave gaps. That’s why a good course — in which a body of material has been organized into a coherent and logical form — is often the most time-efficient way to master a meaningful body of knowledge. When you’ve absorbed the knowledge available in courses, you can switch over to research papers and other resources.
那么，如何获得这些技能呢？互联网上有很多好的内容，在理论上，阅读几十个网页可能会起到作用。但是，当目标是深入理解时，阅读不连贯的网页效率低下，因为它们倾向于重复彼此，使用不一致的术语（这会减慢你的速度），导致理解下降。这就是为什么一个好的课程——将一组材料组织成一个连贯以及逻辑自洽的形式——通常是掌握有意义的知识体系最省时的方式。当你掌握了课程中的知识后，就可以切换到研究论文和其他资源。
Finally, no one can cram everything they need to know over a weekend or even a month. Everyone I know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing, there’s little choice but to keep learning if you want to keep up.
最后，没有人可以在一个周末甚至一个月内压缩他们所需了解的一切。我认识的所有擅长【机器学习】的人都是终身学习者。鉴于我们领域的变化速度如此之快，如果想要跟上发展，就别无选择，只能不断学习。
How can you maintain a steady pace of learning for years? If you can cultivate the habit of learning a little bit every week, you can make significant progress with what feels like less effort.
那么，如何保持多年稳定的学习步伐呢？如果你能养成每周学习一点的习惯，你可以在感觉更轻松的情况下取得重大进展。
建立新习惯的最佳方法
我最喜欢的书之一是BJ Fogg的《微习惯：改变一切的小改变》。Fogg解释说，建立新习惯的最佳方法是从小处开始并取得成功，而不是从大处开始然后失败。例如，他建议不要试图每天运动30分钟，而是试着做一次俯卧撑，并且要坚持下去。
这种方法可能对那些想要花更多时间学习的人有帮助。如果你从每天只看10秒钟的教育视频开始并坚持下去，那么每天学习的习惯会自然而然地形成。即使你在那10秒钟里学不到什么，你也正在建立每天学习一点的习惯。在某些天，也许你会学习一个小时或更长时间。
Should You Learn Math to Get a Job in AI?How much math do you need to know to be a machine learning engineer? Is math a foundational skill for AI? It’s always nice to know more math! But there’s so much to learn that, realistically, it’s necessary to prioritize. Here’s how you might go about strengthening your math background.
作为【机器学习】工程师，需要了解多少【数学】？【数学】是AI的基本技能吗？当然，掌握更多【数学】知识总是很好的！
但是要学习的内容太多了，现实情况是需要进行优先排序。以下是一些提高【数学】基础的方法。To figure out what’s important to know, I find it useful to ask what you need to know to make the decisions required for the work you want to do. At DeepLearning.AI, we frequently ask, “What does someone need to know to accomplish their goals?” The goal might be building a machine learning model, architecting a system, or passing a job interview.
为了找出哪些内容是重要的，我发现询问“为了实现目标，需要知道哪些内容？”非常有用。在DeepLearning.AI，我们经常会问：“为了完成目标，需要了解哪些内容？”这个目标可能是建立一个【机器学习】模型、构建一个系统或通过一次工作面试。
Understanding the math behind algorithms you use is often helpful, since it enables you to debug them. But the depth of knowledge that’s useful changes over time. As machine learning techniques mature and become more reliable and turnkey, they require less debugging, and a shallower understanding of the math involved may be sufficient to make them work.
了解你使用的算法背后的【数学】原理通常是有帮助的，因为这使你能够进行调试。但有用的【数学】知识的深度随着时间的推移而改变。随着【机器学习】技术的成熟和变得更加可靠和成熟，它们需要的调试较少，对其中涉及的【数学】的浅显理解可能足以使其正常工作。
For instance, in an earlier era of machine learning, linear algebra libraries for solving linear systems of equations (for linear regression) were immature. I had to understand how these libraries worked so I could choose among different libraries and avoid numerical roundoff pitfalls. But this became less important as numerical linear algebra libraries matured.
例如，在【机器学习】的早期阶段，用于解决线性系统方程（例如用于【线性回归】）的线性代数库还不够成熟。我必须理解这些库的工作方式，以便在不同的库之间进行选择并避免数值误差的问题。但是随着数值线性代数库的发展，这变得不那么重要了。
Deep learning is still an emerging technology, so when you train a neural network and the optimization algorithm struggles to converge, understanding the math behind gradient descent, momentum, and the Adam optimization algorithm will help you make better decisions.
【深度学习】仍然是一项新兴技术，因此，当你训练神经网络时，如果优化算法难以收敛，了解梯度下降、动量和【Adam优化算法】背后的【数学】原理将有助于你做出更好的决策。
Similarly, if your neural network does something funny — say, it makes bad predictions on images of a certain resolution, but not others — understanding the math behind neural network architectures puts you in a better position to figure out what to do. Of course, I also encourage learning driven by curiosity. If something interests you, go ahead and learn it regardless of how useful it might turn out to be! Maybe this will lead to a creative spark or technical breakthrough.
同样地，如果你的神经网络表现出奇怪的行为——例如，对某种分辨率的图像做出错误的预测，但对其他图像则没有问题——了解神经网络架构背后的【数学】原理将使你更好地理解该问题以及如何解决它。当然，我也鼓励出于好奇心的学习。如果某些内容吸引你，可以随意学习，无论它可能有多大的用处！
Scoping Successful AI Projects
也许这会引发你的创造灵感，带来技术上的突破。One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. These next few chapters will discuss finding and working on projects so you can gain experience and build your portfolio.
AI架构师最重要的技能之一是能够识别值得努力的想法（指明 研究的方向）。接下来几章将讨论如何找到并参与项目，以便获得经验并建立作品集，从而积累自身的实战经验。
Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate change, agriculture, ecommerce, advertising, and other industries. How can someone who’s not an expert in all these sectors find meaningful projects within them? Here are five steps to help you scope projects.
多年来，我一直乐于将【机器学习】应用于制造业、医疗保健、气候变化、农业、电子商务、广告和其他行业。一个不精通这些领域的人如何在其中找到有意义的项目呢？以下是五个步骤，以帮助您规划项目。
Step 1:Identify a business problem (not an AI problem). I like to find a domain expert and ask, “What are the top three things that you wish worked better? Why aren’t they working yet?” For example, if you want to apply AI to climate change, you might discover that power-grid operators can’t accurately predict how much power intermittent sources like wind and solar might generate in the future.
第一步：确定一个商业问题（而不是一个AI问题）。我喜欢找一个领域专家并问：“你希望哪三件事情能更好地解决？为什么它们还没有解决？”例如，如果您想将AI应用于气候变化，您可能会发现电网运营商无法准确预测风能和太阳能等间歇性能源未来产生的电量。
Step 2:Brainstorm AI solutions. When I was younger, I used to execute on the first idea I was excited about. Sometimes this worked out okay, but sometimes I ended up missing an even better idea that might not have taken any more effort to build. Once you understand a problem, you can brainstorm potential solutions more efficiently. For instance, to predict power generation from intermittent sources, we might consider using satellite imagery to map the locations of wind turbines more accurately, using satellite imagery to estimate the height and generation capacity of wind turbines, or using weather data to better predict cloud cover and thus solar irradiance. Sometimes there isn’t a good AI solution, and that’s okay too.
第二步：构思AI解决方案。当我年轻的时候，我习惯于执行我感到兴奋的第一个想法（第一直觉，先入为主）。有时这还不错，但有时我错过了一个甚至更好的想法，而这个想法可能不需要更多的努力就可以构建。一旦您了解了问题，您就可以更有效地构思潜在的解决方案。例如，为了预测间歇性能源的发电量，我们可以考虑使用卫星图像更准确地绘制风力涡轮机的位置，使用卫星图像估算风力涡轮机的高度和发电能力，或使用天气数据更好地预测云层覆盖率，从而预测太阳辐射。有时候可能没有一个好的AI解决方案，这也无妨。
Step 3:Assess the feasibility and value of potential solutions. You can determine whether an approach is technically feasible by looking at published work, what competitors have done, or perhaps building a quick proof of concept implementation. You can determine its value by consulting with domain experts (say, power-grid operators, who can advise on the utility of the potential solutions mentioned above).
第三步：评估潜在解决方案的可行性和价值。您可以通过查阅已发表的研究、竞争对手的做法或者构建一个快速的概念验证实现来确定一个方法是否技术可行。您可以通过与领域专家（例如电网运营商）咨询潜在解决方案的实用性来确定其价值。
Step 4:Determine milestones. Once you’ve deemed a project sufficiently valuable, the next step is to determine the metrics to aim for. This includes both machine learning metrics (such as accuracy) and business metrics (such as revenue). Machine learning teams are often most comfortable with metrics that a learning algorithm can optimize. But we may need to stretch outside our comfort zone to come up with business metrics, such as those related to user engagement, revenue, and so on. Unfortunately, not every business problem can be reduced to optimizing test set accuracy! If you aren’t able to determine reasonable milestones, it may be a sign that you need to learn more about the problem. A quick proof of concept can help supply the missing perspective.
第四步：确定里程碑。一旦您确定了一个足够有价值的项目，下一步是确定要达到的指标。这包括【机器学习】指标（如准确性）和业务指标（如收入）。【机器学习】团队通常最擅长优化学习算法可以优化的指标。但是我们可能需要超出我们的舒适区来提出业务指标，例如与用户参与度、收入等相关的指标。不幸的是，并不是每个业务问题都可以简化为优化测试集准确性！
如果您无法确定合理的里程碑，这可能表明您需要更多了解问题。快速的概念验证可以帮助提供缺失的视角。（MVP的概念 Minimum Viable Product）Step 5:Budget for resources. Think through everything you’ll need to get the project done including data, personnel, time, and any integrations or support you may need from other teams. For example, if you need funds to purchase satellite imagery, make sure that’s in the budget.
第五步：为资源预算。考虑一下完成项目所需的一切，包括数据、人员、时间，以及其他团队可能需要的任何集成或支持。例如，如果您需要购买卫星图像的资金，确保它在预算中。
Working on projects is an iterative process. If, at any step, you find that the current direction is infeasible, return to an earlier step and proceed with your new understanding. Is there a domain that excites you where AI might make a difference? I hope these steps will guide you in exploring it through project work — even if you don’t yet have deep expertise in that field. AI won’t solve every problem, but as a community, let’s look for ways to make a positive impact wherever we can.
进行项目工作是一个迭代的过程。如果在任何一个步骤中，你发现当前方向不可行，回到较早的步骤，并在新的理解下继续前行。有没有让你感到兴奋的领域，在那个领域中 AI 可能会起到作用？我希望这些步骤能指导你通过项目工作来探索它，即使你对该领域还没有深入的专业知识。AI 不能解决所有问题，但作为一个社区，让我们寻找在任何可能的地方产生积极影响的方法。
Finding Projects that Complement Your Career GoalsIt goes without saying that we should only work on projects that are responsible, ethical, and beneficial to people. But those limits leave a large variety to choose from. In the previous chapter, I wrote about how to identify and scope AI projects. This chapter and the next have a slightly different emphasis: picking and executing projects with an eye toward career development.
毫无疑问，我们只应该开展那些负责任、符合伦理、对人们有益的项目。但是这些限制还是有很多选择的空间。在上一章中，我写了如何确定和范围化AI项目。本章和下一章的重点略有不同：挑选和执行项目时要考虑职业发展。
A fruitful career will include many projects, hopefully growing in scope, complexity, and impact over time. Thus, it is fine to start small. Use early projects to learn and gradually step up to bigger projects as your skills grow.
丰富的职业生涯将包括许多项目，随着时间的推移，这些项目的范围、复杂性和影响力都会不断增长。因此，从小开始是可以的。用早期的项目来学习，并逐渐步入更大的项目，随着技能的增长。
When you’re starting out, don’t expect others to hand great ideas or resources to you on a platter. Many people start by working on small projects in their spare time. With initial successes — even small ones — under your belt, your growing skills increase your ability to come up with better ideas, and it becomes easier to persuade others to help you step up to bigger projects.
当您刚开始时，不要指望别人会毫不费力地为您提供绝佳的想法或资源。许多人会开始在业余时间里从事小型项目。有了最初的成功——即使只是小小的成功——您不断增长的技能将增加您提出更好想法的能力，也更容易说服他人帮助您步入更大的项目。
What if you don’t have any project ideas? Here are a few ways to generate them:
如果你没有任何项目想法怎么办？以下是几种生成项目想法的方法：
Join existing projects. If you find someone else with an idea, ask to join their project. Keep reading and talking to people. I come up with new ideas whenever I spend a lot of time reading, taking courses, or talking with domain experts. I’m confident that you will, too.Focus on an application area. Many researchers are trying to advance basic AI technology — say, by inventing the next generation of transformers or further scaling up language models — so, while this is an exciting direction, it is also very hard. But the variety of applications to which machine learning has not yet been applied is vast! I’m fortunate to have been able to apply neural networks to everything from autonomous helicopter flight to online advertising, partly because I jumped in when relatively few people were working on those applications. If your company or school cares about a particular application, explore the possibilities for machine learning. That can give you a first look at a potentially creative application — one where you can do unique work — that no one else has done yet.Develop a side hustle. Even if you have a full-time job, a fun project that may or may not develop into something bigger can stir the creative juices and strengthen bonds with collaborators. When I was a full-time professor, working on online education wasn’t part of my “job” (which was doing research and teaching classes). It was a fun hobby that I often worked on out of passion for education. My early experiences in recording videos at home helped me later in working on online education in a more substantive way. Silicon Valley abounds with stories of startups that started as side projects. As long as it doesn’t create a conflict with your employer, these projects can be a stepping stone to something significant.加入现有项目：如果你找到了别人的想法，可以请求加入他们的项目。继续阅读和与人交谈。当我花费大量时间阅读、参加课程或与领域专家交谈时，我总是能想出新点子。我相信你也会。
关注一个应用领域：许多研究人员试图推进基础的AI技术——比如发明下一代Transformer或进一步扩大语言模型的规模——因此，虽然这是一个令人兴奋的方向，但也非常困难。但是，【机器学习】尚未应用到的应用领域的种类是巨大的！
我很幸运能够将神经网络应用于从自主直升机飞行到在线广告的一切，部分原因是在相对较少的人从事这些应用时就开始了工作。如果你的公司或学校关注某个特定的应用，探索【机器学习】的可能性可以让你第一次看到一个潜在的创意应用——一个你可以独特地工作的地方——还没有其他人做过。培养一项副业：即使你有一份全职工作，一个可能或可能不会成为更大事的有趣项目可以激发创意，增强与合作者的联系。当我是一名全职教授时，从事在线教育不是我的“工作”（我的工作是做研究和教授课程）。这是一项有趣的爱好，我常常出于对教育的热爱而从事。我在家录制视频的早期经验帮助我更深入地从事在线教育。硅谷充满了从副业开始的初创公司的故事。只要不会与你的雇主产生冲突，这些项目可以成为实现重大目标的跳板。
Given a few project ideas, which one should you jump into? Here’s a quick checklist of factors to consider:
给出了几个项目想法，那么你应该选择哪一个来着手呢？以下是一个快速的考虑因素清单：
Will the project help you grow technically? Ideally, it should be challenging enough to stretch your skills but not so hard that you have little chance of success. This will put you on a path toward mastering ever-greater technical complexity.Do you have good teammates to work with? If not, are there people you can discuss things with? We learn a lot from the people around us, and good collaborators will have a huge impact on your growth.Can it be a stepping stone? If the project is successful, will its technical complexity and/or business impact make it a meaningful stepping stone to larger projects? If the project is bigger than those you’ve worked on before, there’s a good chance it could be such a stepping stone.该项目是否能促使你在技术上成长？理想情况下，它应该具有足够挑战性，能够拓展你的技能，但又不至于太难以让你没有成功的机会。这将使你在不断掌握技术复杂性的道路上不断进步。
你是否有好的团队成员一起工作？如果没有，是否有人可以讨论问题？我们从身边的人身上学到了很多，好的合作者将对你的成长产生巨大的影响。
它是否可以成为一个垫脚石？如果该项目成功了，它的技术复杂性和/或业务影响是否能够成为更大项目的有意义的垫脚石？如果该项目比你之前参与的项目都要大，那么很可能它是这样一个垫脚石。
Finally, avoid analysis paralysis. It doesn’t make sense to spend a month deciding whether to work on a project that would take a week to complete. You'll work on multiple projects over the course of your career, so you’ll have ample opportunity to refine your thinking on what’s worthwhile. Given the huge number of possible AI projects, rather than the conventional “ready, aim, fire” approach, you can accelerate your progress with “ready, fire, aim.”
最后，避免分析瘫痪。如果一个项目只需要一周的时间，但你花了一个月来决定是否要投入，这是没有意义的。在职业生涯中，你会有多个项目的机会，因此你将有足够的机会来完善自己的想法，决定哪些是值得的。考虑到可能的AI项目数量巨大，与传统的“准备、瞄准、射击”方法不同，你可以通过“准备、射击、瞄准”来加速进展。
准备、开火、调整 这是项目的两种模式
工作中需要做出关于建设什么和如何建设的艰难选择。下面有两种不同的方式：
准备、瞄准、开火：精心规划和验证。只有在对方向有高度信心的情况下才承诺并执行。准备、开火、调整：跳入开发并开始执行。这样可以快速发现问题，并在必要时进行调整。假设你为零售商建立了一个客服聊天机器人，并认为它也可以帮助餐厅。在开始开发之前，你是否应该花时间研究餐厅市场，慢慢行动但削减浪费时间和资源的风险？还是立即着手，快速行动并接受更高的重定位或失败风险？
两种方法都有支持者，最佳选择取决于情况。
当执行成本高且研究可以揭示项目有多有用或有价值时，准备、瞄准、开火通常更好。例如，如果你可以集思广益几个其他用例（餐厅、航空公司、电信等）并评估这些用例以确定最有前途的方向，那么在承诺方向之前花费额外时间可能是值得的。
当可以低成本执行并通过执行确定方向是否可行以及发现使其正常工作的调整时，准备、开火、调整往往更好。例如，如果你可以快速构建一个原型来确定用户是否需要该产品，并且在少量工作后取消或重定位是可以接受的，那么快速行动可能是有意义的。当射击的成本低时，进行多次尝试也是有意义的。在这种情况下，实际的过程是准备、开火、调整、开火、调整、开火、调整、开火。
在同意项目方向后，对于构建产品的一部分的【机器学习】模型，我有倾向于准备、开火、调整。构建模型是一个迭代过程。对于许多应用程序，培训和进行误差分析的成本并不是禁止性的。此外，很难进行研究来揭示适当的模型、数据和超参数。因此，快速构建一个端到端系统并进行修订直到其正常工作是有意义的。
但是，当承诺方向意味着进行昂贵的投资或进入单向门（指难以逆转的决策）时，通常值得提前花更多时间。
Building a Portfolio of Projects that Shows Skill ProgressionOver the course of a career, you’re likely to work on projects in succession, each growing in scope and complexity. For example:
在职业生涯中，你可能会接连参与各种项目，每个项目的规模和复杂度都会逐渐增加。例如：
Class projects:The first few projects might be narrowly scoped homework assignments with predetermined right answers. These are often great learning experiences!
Personal projects
You might go on to work on small-scale projects either alone or with friends. For instance, you might re-implement a known algorithm, apply machine learning to a hobby (such as predicting whether your favorite sports team will win), or build a small but useful system at work in your spare time (such as a machine learning-based script that helps a colleague automate some of their work). Participating in competitions such as those organized by Kaggle is also one way to gain experience.
Creating value
Eventually, you will gain enough skill to build projects in which others see more tangible value. This opens the door to more resources. For example, rather than developing machine learning systems in your spare time, it might become part of your job, and you might gain access to more equipment, compute time, labeling budget, or head count.
Rising scope and complexitySuccesses build on each other, opening the door to more technical growth, more resources, and increasingly significant project opportunities.
课程项目：最初的几个项目可能是范围较小的作业任务，具有预先确定的正确答案。这些通常是非常好的学习经验！
个人项目：你可能会开始独自或与朋友一起工作于小规模的项目。例如，你可能会重新实现一个已知的算法，将【机器学习】应用于兴趣爱好（如预测你最喜欢的体育队是否会获胜），或在工作中的空闲时间内构建一个小而有用的系统（如基于【机器学习】的脚本，帮助同事自动化他们的一些工作）。参加 Kaggle 等竞赛也是获取经验的一种途径。
价值创造：最终，你会获得足够的技能，可以构建其他人认为更具有实际价值的项目。这为你开启了获得更多资源的大门。例如，与其在业余时间开发【机器学习】系统，你可能会将其作为工作的一部分，从而获得更多的设备、计算时间、标注预算或人力资源。
不断提升的范围和复杂度：成功会在彼此之间构建，从而为你开启更多的技术成长、更多的资源和日益重要的项目机会。
Each project is only one step on a longer journey, hopefully one that has a positive impact. In addition:
每个项目只是漫长旅程中的一步，希望这步会产生积极的影响。此外：
Don’t worry about starting too small. One of my first machine learning research projects involved training a neural network to see how well it could mimic the sin(x) function. It wasn’t very useful, but was a great learning experience that enabled me to move on to bigger projects.
不要担心开始得太小。我最初的【机器学习】研究项目之一是训练神经网络，以查看它能否很好地模仿sin（x）函数。这个项目没有什么用处，但它是一次很好的学习经历，让我能够转向更大的项目。
Communication is key. You need to be able to explain your thinking if you want others to see the value in your work and trust you with resources that you can invest in larger projects. To get a project started, communicating the value of what you hope to build will help bring colleagues, mentors, and managers onboard — and help them point out flaws in your reasoning. After you’ve finished, the ability to explain clearly what you accomplished will help convince others to open the door to larger projects.
沟通是关键。如果你希望其他人看到你的工作价值并信任你投资更大的项目资源，你需要能够解释你的思路。为了启动一个项目，阐明你希望构建的价值将有助于将同事、导师和管理人员纳入团队，并帮助他们指出你思考的缺陷。完成后，清晰地解释你所完成的事情将有助于说服他人打开更大项目的大门。
Leadership isn’t just for managers. When you reach the point of working on larger AI projects that require teamwork, your ability to lead projects will become more important, whether or not you are in a formal position of leadership. Many of my friends have successfully pursued a technical rather than managerial career, and their ability to help steer a project by applying deep technical insights — for example, when to invest in a new technical architecture or collect more data of a certain type — allowed them to grow as leaders and also helped significantly improve the project. Building a portfolio of projects, especially one that shows progress over time from simple to complex undertakings, will be a big help when it comes to looking for a job.
领导不仅仅是为管理人员而设。当你开始着手处理需要团队合作的更大型 AI 项目时，你的项目领导能力将变得更加重要，无论你是否处于正式领导职位上。我的许多朋友成功地追求了技术而非管理职业，他们能够通过应用深刻的技术见解来帮助引导项目，例如何时投资于新的技术架构或收集更多某种类型的数据，这使得他们成长为领导者，并显著提高了项目的效率。建立一个项目组合，特别是一个显示从简单到复杂的进展的项目组合，将在寻找工作时大有帮助。
A Simple Framework for Starting Your AI Job SearchFinding a job has a few predictable steps that include selecting the companies to which you want to apply, preparing for interviews, and finally picking a role and negotiating a salary and benefits. In this chapter, I’d like to focus on a framework that’s useful for many job seekers in AI, especially those who are entering AI from a different field. If you’re considering your next job, ask yourself:
找工作通常包括一些可预测的步骤，例如选择想要申请的公司、准备面试，最后选择一个角色并协商薪资和福利。在本章中，我想关注一个对许多寻求AI工作的人有用的框架，特别是那些从不同领域进入AI的人。 如果您正在考虑下一个工作，请问自己以下问题：
Are you switching roles? For example, if you’re a software engineer, university student, or physicist who’s looking to become a machine learning engineer, that’s a role switch.Are you switching industries? For example, if you work for a healthcare company, financial services company, or a government agency and want to work for a software company, that’s a switch in industries.您是否在转换角色？例如，如果您是一名软件工程师、大学生或物理学家，想成为一名【机器学习】工程师，那就是角色转换。
您是否在转换行业？例如，如果您在医疗保健公司、金融服务公司或政府机构工作，并想在软件公司工作，那就是转换行业。
A product manager at a tech startup who becomes a data scientist at the same company (or a different one) has switched roles. A marketer at a manufacturing firm who becomes a marketer in a tech company has switched industries. An analyst in a financial services company who becomes a machine learning engineer in a tech company has switched both roles and industries.
一个在科技创业公司担任产品经理的人，如果转而在同一家公司（或其他公司）成为数据科学家，那么他已经进行了职业角色转换。一个在制造公司的市场营销人员，如果在科技公司成为市场营销人员，那么他已经转行了。一个金融服务公司的分析师，如果在科技公司成为【机器学习】工程师，那么他已经同时转换了职业角色和行业。
If you’re looking for your first job in AI, you’ll probably find switching either roles or industries easier than doing both at the same time. Let’s say you’re the analyst working in financial services:
如果你正在寻找你在【人工智能】领域的第一份工作，你可能会发现，只进行职业角色转换或行业转换要比同时进行两者更容易。假设你是一名在金融服务领域工作的分析师：
If you find a data science or machine learning job in financial services, you can continue to use your domain-specific knowledge while gaining knowledge and expertise in AI. After working in this role for a while, you’ll be better positioned to switch to a tech company (if that’s still your goal).Alternatively, if you become an analyst in a tech company, you can continue to use your skills as an analyst but apply them to a different industry. Being part of a tech company also makes it much easier to learn from colleagues about practical challenges of AI, key skills to be successful in AI, and so on.如果你在金融服务行业找到了一个数据科学或【机器学习】的工作，你可以继续利用你在特定领域的知识，同时获得关于【人工智能】的知识和专业技能。在这个职位上工作一段时间后，你将更有可能转向科技公司（如果这仍然是你的目标）。
或者，如果你成为了一家科技公司的分析师，你可以继续运用你的分析技能，但将其应用于不同的行业。成为科技公司的一员也使你更容易从同事那里学习有关【人工智能】的实际挑战、成功从事【人工智能】所需的关键技能等方面的知识。
If you’re considering a role switch, a startup can be an easier place to do it than a big company. While there are exceptions, startups usually don’t have enough people to do all the desired work. If you’re able to help with AI tasks — even if it’s not your official job — your work is likely to be appreciated. This lays the groundwork for a possible role switch without needing to leave the company. In contrast, in a big company, a rigid reward system is more likely to reward you for doing your job well (and your manager for supporting you in doing the job for which you were hired), but it’s not as likely to reward contributions outside your job’s scope.
如果你正在考虑职业转换，初创公司可能比大公司更容易做到。虽然有例外情况，但初创公司通常没有足够的人手来完成所有需要的工作。如果你能够帮助完成AI任务，即使这不是你的本职工作，你的工作可能会得到赞赏。这为可能在不离开公司的情况下进行职业转换奠定了基础。相比之下，在大公司中，严格的奖励体系更有可能奖励你的工作表现（并奖励你的经理支持你完成你被雇佣的工作），但不太可能奖励你在职责范围之外的贡献。
After working for a while in your desired role and industry (for example, a machine learning engineer in a tech company), you’ll have a good sense of the requirements for that role in that industry at a more senior level. You’ll also have a network within that industry to help you along.So future job searches — if you choose to stick with the role and industry — likely will be easier.
在你所期望的职业和行业工作一段时间后（例如，在科技公司中成为【机器学习】工程师），你将对该行业中该职位更高级别的要求有了很好的了解。你还会在该行业中建立一些人脉，以帮助你前进。因此，如果你决定坚持这个角色和行业，未来的工作搜索可能会更容易。
When changing jobs, you’re taking a step into the unknown, particularly if you’re switching either roles or industries. One of the most underused tools for becoming more familiar with a new role and/or industry is the informational interview. I’ll share more about that in the next chapter.
在换工作时，你是在走向未知，尤其是如果你正在转换职业或行业。了解新职位和/或行业的最常被忽视的工具之一是信息面试。下一章节我将分享更多信息面试的内容。
I’m grateful to Salwa Nur Muhammad, CEO of FourthBrain (a DeepLearning.AI affiliate), for providing some of the ideas presented in this chapter.
我很感激FourthBrain（DeepLearning.AI的附属机构）的CEO Salwa Nur Muhammad提供了本章节中提出的一些想法。
Overcoming Uncertainty 克服不确定性
There’s a lot we don’t know about the future: When will we cure Alzheimer’s disease? Who will win the next election? Or, in a business context, how many customers will we have next year? With so many changes going on in the world, many people are feeling stressed about the future, especially when it comes to finding a job. I have a practice that helps me regain a sense of control. Faced with uncertainty, I try to:
我们对未来有很多不确定性：什么时候才能治愈阿尔茨海默病？谁会赢得下一届选举？或者在商业环境下，我们明年会有多少客户？随着世界发生了这么多变化，许多人对未来感到紧张，特别是在寻找工作时。我有一个习惯可以帮助我恢复对未来的掌控感。面对不确定性，我尝试：
Make a list of plausible scenarios, acknowledging that I don’t know which will come to pass.Create a plan of action for each scenario.Start executing actions that seem reasonable.Review scenarios and plans periodically as the future comes into focus.列出可能的情景，承认我不知道哪个会出现。
为每种情景制定行动计划。
开始执行看起来合理的行动。
随着未来变得更加明朗，定期审查情景和计划。
For example, during the Covid-19 pandemic back in March 2020, I did this scenario planning exercise. I imagined quick (three months), medium (one year), and slow (two years) recoveries from Covid-19 and made plans for managing each case. These plans have helped me prioritize where I can.
例如，在2020年3月的新冠疫情期间，我进行了这个情景规划演习。我想象了快速（三个月）、中等（一年）和缓慢（两年）的疫情恢复情况，并为应对每种情况制定了计划。这些计划帮助我确定了自己的重点。
The same method can apply to personal life, too. If you’re not sure you’ll pass an exam, get a job offer, or be granted a visa — all of which can be stressful — you can write out what you’d do in each of the likely scenarios. Thinking through the possibilities and following through on plans can help you navigate the future effectively no matter what it brings.
相同的方法也适用于个人生活。如果你不确定是否能通过考试、获得工作机会或获得签证（这些都可能让人感到压力），你可以写下你在每种可能的情况下会做什么。思考各种可能性并执行计划可以帮助你有效地应对未来，无论未来会带来什么。
奖励内容：通过接受【人工智能】和统计学培训，你可以计算每个场景的概率。我喜欢超级预测方法，它将许多专家的判断综合成一个概率估计。
Using Informational Interviews to Find the Right JobIf you’re preparing to switch roles (say, taking a job as a machine learning engineer for the first time) or industries (say, working in an AI tech company for the first time), there’s a lot about your target job that you probably don’t know. A technique known as informational interviewing is a great way to learn.
如果你正准备换工作（比如第一次担任【机器学习】工程师的工作）或者换行业（比如第一次在一个AI科技公司工作），那么你可能不知道目标工作的很多信息。信息采访是一个学习的好方法。
An informational interview involves finding someone in a company or role you’d like to know more about and informally interviewing them about their work. Such conversations are separate from searching for a job. In fact, it’s helpful to interview people who hold positions that align with your interests well before you’re ready to kick off a job search.
信息采访包括找到一个在你想了解更多信息的公司或者职位中的人，然后非正式地采访他们的工作。这些谈话是与找工作分开的。事实上，你最好在开始找工作之前就采访与你兴趣相符的职位上的人。
Informational interviews are particularly relevant to AI. Because the field is evolving, many companies use job titles in inconsistent ways. In one company, data scientists might be expected mainly to analyze business data and present conclusions on a slide deck. In another, they might write and maintain production code. An informational interview can help you sort out what the AI people in a particular company actually do.With the rapid expansion of opportunities in AI, many people will be taking on an AI job for the first time. In this case, an informational interview can be invaluable for learning what happens and what skills are needed to do the job well. For example, you can learn what algorithms, deployment processes, and software stacks a particular company uses. You may be surprised — if you’re not already familiar with the data-centric AI movement — to learn how much time most machine learning engineers spend iteratively cleaning datasets.信息采访在AI领域尤其重要。因为这个领域正在不断发展，许多公司以不一致的方式定义职称。在一家公司中，【数据科学家】可能主要负责分析业务数据并在幻灯片中呈现结论。而在另一家公司，他们可能编写和维护生产代码。信息采访可以帮助你弄清楚一个特定公司的AI团队实际在做什么。
随着AI机会的迅速扩大，许多人将首次担任AI工作。在这种情况下，信息采访可以为学习工作细节和所需技能提供无价的帮助。例如，你可以了解到一个特定公司使用哪些算法、部署流程和软件堆栈。如果你还不熟悉以数据为中心的AI运动，你可能会惊讶地发现，大多数【机器学习】工程师都会花费大量时间反复清理数据集。
Prepare for informational interviews by researching the interviewee and company in advance,so you can arrive with thoughtful questions. You might ask:
提前研究受访者和公司，准备一些有思考性的问题，以便做好信息采访的准备。你可以问以下问题：
What do you do in a typical week or day?What are the most important tasks in this role?What skills are most important for success?How does your team work together to accomplish its goals?What is the hiring process?Considering candidates who stood out in the past, what enabled them to shine?您在一周或一天中通常要做什么？
这个角色中最重要的任务是什么？
成功所需的最重要的技能是什么？
您的团队如何共同合作以实现其目标？
招聘流程是什么？
考虑过去表现出色的候选人，是什么让他们脱颖而出的？
Finding someone to interview isn’t always easy, but many people who are in senior positions today received help when they were new from those who had entered the field ahead of them, and many are eager to pay it forward. If you can reach out to someone who’s already in your network — perhaps a friend who made the transition ahead of you or someone who attended the same school as you — that’s great! Meetups such as Pie & AI can also help you build your network.
寻找人进行面试并不总是容易的，但许多现在担任高级职位的人在他们新入行时也曾受到前辈的帮助，他们也很愿意回报。如果你可以联系到已经在你的网络中的人，比如一个先于你成功转型的朋友，或是和你一样的校友，那就太好了！
像 Pie & AI 这样的聚会也可以帮助你建立你的网络。Finally, be polite and professional, and thank the people you’ve interviewed. And when you get a chance, please pay it forward as well and help someone coming up after you. If you receive a request for an informational interview from someone in the DeepLearning.AI community, I hope you’ll lean in to help them take a step up! If you’re interested in learning more about informational interviews, I recommend this article from the UC Berkeley Career Center.
最后，保持礼貌专业，并感谢你面试过的人。当你有机会的时候，请回报帮助你的人，也帮助那些接下来要来的人。如果你收到 DeepLearning.AI 社区的人发来的请求进行信息面试，我希望你能倾力帮助他们向前迈进！
如果你对信息面试想要了解更多，我推荐你阅读加州大学伯克利分校职业中心的这篇文章。I’ve mentioned a few times the importance of your network and community. People you’ve met, beyond providing valuable information, can also play an invaluable role by referring you to potential employers.
我多次提到网络和社区的重要性。你遇到的人，除了提供有价值的信息外，还可以通过向潜在雇主推荐你发挥不可估量的作用。
Finding the Right AI Job for YouIn this chapter, I’d like to discuss some fine points of finding a job. The typical job search follows a fairly predictable path.
在本章中，我想讨论一些关于找工作的细节。典型的求职过程遵循一个相当可预测的路径。
Research roles and companies online or by talking to friends.Optionally, arrange informal informational interviews with people in companies that appeal to you.Either apply directly or, if you can, get a referral from someone on the inside.Interview with companies that give you an invitation.Receive one or more offers and pick one. Or, if you don’t receive an offer, ask for feedback from the interviewers, human resources staff, online discussion boards, or anyone in your network who can help you plot your next move.在网上或通过与朋友交谈进行研究职位和公司。
可选地，安排与感兴趣的公司中的人进行非正式的信息性面试。
直接申请或者如果可能的话，从内部人员获得推荐信。
接受公司的邀请进行面试。
接受一个或多个工作提议并选择其中一个。或者，如果你没有收到工作提议，请向面试官、人力资源工作人员、在线讨论论坛或任何能够帮助你规划下一步行动的人员索取反馈。
Although the process may be familiar, every job search is different. Here are some tips to increase the odds you’ll find a position that supports your thriving career and enables you to keep growing.
虽然求职的流程可能会比较熟悉，但每个求职过程都是不同的。以下是一些提示，有助于提高您找到支持您职业发展和继续成长的职位的几率：
Pay attention to the fundamentals. A compelling resume, portfolio of technical projects, and a strong interview performance will unlock doors. Even if you have a referral from someone in a company, a resume and portfolio will be your first contact with many people who don’t already know about you. Update your resume and make sure it clearly presents your education and experience relevant to the role you want. Customize your communications with each company to explain why you’re a good fit. Before an interview, ask the recruiter what to expect. Take time to review and practice answers to common interview questions, brush up key skills, and study technical materials to make sure they are fresh in your mind. Afterward, take notes to help you remember what was said.
关注基本要素。令人信服的简历、技术项目组合以及强大的面试表现会为你打开大门。即使你已经得到某家公司内部员工的推荐，简历和项目组合也会成为许多人第一次认识你的途径。更新你的简历，确保它清晰地呈现了你与目标职位相关的教育和经验。与每个公司进行个性化沟通，解释为什么你是一个合适的人选。在面试之前，询问招聘人员会有什么期望。花时间复习和练习常见的面试问题，提高关键技能水平，并研究技术材料，以确保它们仍然新鲜在你的脑海里。面试后，记下笔记以帮助你记住谈话内容。
Proceed respectfully and responsibly. Approach interviews and offer negotiations with a winwin mindset. Outrage spreads faster than reasonableness on social media, so a story about how an employer underpaid someone gets amplified, whereas stories about how an employer treated someone fairly do not. The vast majority of employers are ethical and fair, so don’t let stories about the small fraction of mistreated individuals sway your approach. If you’re leaving a job, exit gracefully. Give your employer ample notice, give your full effort through your last hour on the job, transition unfinished business as best you can, and leave in a way that honors the responsibilities you were entrusted with.
以尊重和责任感为出发点。以双赢的心态对待面试和工作机会的谈判。在社交媒体上，愤怒比理性更容易传播，所以有关雇主低薪招聘某人的故事会得到放大，而雇主公正地对待员工的故事则不会。绝大多数雇主都是道德和公平的，所以不要让关于那一小部分被虐待的人的故事影响你的态度。如果你要离开一份工作，请优雅地离开。提前通知你的雇主，全力以赴完成你的最后工作，尽最大努力交接未完成的业务，并以一种尊重你被委托的职责的方式离开。
选择合适的合作伙伴。有时候会因为想参与某个项目而接受某份工作，但是你的同事至少同样重要。我们受周围人的影响很大，所以你的同事会产生很大的影响。比如说，如果你的朋友吸烟，你也有吸烟的可能性增加。我不知道有没有研究能够证明这一点，但我非常确定，如果你的大多数同事工作努力，持续学习，并建立人人受益的AI，你也有可能会做同样的事情。（顺便说一句，有些大公司在你接受了一个工作邀请之后才会告诉你你的队友是谁。在这种情况下，要坚持不懈地推动识别和与潜在的队友交谈。严格的政策可能使你无法得到满足，但在我看来，这会增加接受邀请的风险，因为这会增加你最终遇到不合适的经理或队友的可能性。）
Get help from your community. Most of us go job hunting only a small number of times in our careers, so few of us get much practice at doing it well. Collectively, though, people in your immediate community probably have a lot of experience. Don’t be shy about calling on them. Friends and associates can provide advice, share inside knowledge, and refer you to others who may help. I got a lot of help from supportive friends and mentors when I applied for my first faculty position, and many of the tips they gave me were very helpful.
从你的社区中获取帮助。我们大多数人的职业生涯中只会找工作几次，所以很少有人有很好的实践经验。但是，你所在的社区中的人们可能有很多经验。不要害羞，可以向他们寻求建议、分享内部信息，并推荐其他可能有帮助的人。当我申请我的第一个教职时，我得到了很多来自支持我的朋友和导师的帮助，他们给我的很多建议都非常有用。
I know that the job-search process can be intimidating. Instead of viewing it as a great leap, consider an incremental approach. Start by identifying possible roles and conducting a handful of informational interviews. If these conversations tell you that you have more learning to do before you’re ready to apply, that’s great! At least you have a clear path forward. The most important part of any journey is to take the first step, and that step can be a small one.
我知道找工作的过程可能会让人感到害怕。不要将其视为一个巨大的飞跃，而是考虑一种逐步的方法。首先，确定可能的职位，并进行少数几次信息性面试。如果这些谈话告诉你，在你准备好申请之前，还有更多需要学习的东西，那太好了！
Keys to Building a Career in AI
至少你有了一个清晰的前进道路。任何旅程中最重要的部分是迈出第一步，而这一步可以很小。The path to career success in AI is more complex than what I can cover in one short eBook. Hopefully the previous chapters will give you momentum to move forward. Here are additional things to think about as you plot your path to success:
AI 职业成功之路比我在这本简短的电子书中所涵盖的更为复杂。希望前面的章节能够为您提供前进的动力。在规划您的成功之路时，请考虑以下额外的事项：
Teamwork:When we tackle large projects, we succeed better by working in teams than individually. The ability to collaborate with, influence, and be influenced by others is critical. Thus, interpersonal and communication skills really matter. (I used to be a pretty bad communicator, by the way.)Networking:I hate networking! As an introvert, having to go to a party to smile and shake as many hands as possible is an activity that borders on horrific. I’d much rather stay home and read a book. Nonetheless, I’m fortunate to have found many genuine friends in AI; people I would gladly go to bat for and who I count on as well. No person is an island, and having a strong professional network can help propel you forward in the moments when you need help or advice. In lieu of networking, I’ve found it more helpful to think about building up a community. So instead of trying to build up my personal network, I focus instead on building up the communities that I’m part of. This has the side effect of helping me meet more people and make friends as well.Job search:Of all the steps in building a career, this one tends to receive the most attention. Unfortunately, there is a lot of bad advice about this on the internet. (For example, many articles urge taking an adversarial attitude toward potential employers, which I don’t think is helpful.) Although it may seem like finding a job is the ultimate goal, it’s just one small step in the long journey of a career.Personal discipline:Few people will know whether you spend your weekends learning, or binge watching TV — but they will notice the difference over time. Many successful people develop good habits in eating, exercise, sleep, personal relationships, work, learning, and self-care. Such habits help them move forward while staying healthy.Altruism:I find that people who aim to lift others during every step of their own journey often achieve better outcomes for themselves. How can we help others even as we build an exciting career for ourselves?团队合作：在处理大型项目时，团队合作比个人单打独斗更容易成功。与他人合作、影响和被影响的能力至关重要。因此，人际交往和沟通能力非常重要。（顺便说一下，我过去的沟通能力相当差。）
社交网络：我讨厌社交网络！
作为一个内向的人，不得不参加聚会，尽可能地微笑和握手，几乎是一种可怕的活动。我更喜欢呆在家里读书。尽管如此，我很幸运在AI领域找到了许多真正的朋友；这些人我很乐意支持，也依赖他们。没有人是孤岛，拥有强大的职业网络可以帮助你在需要帮助或建议的时候向前推进。而我发现，与其建立个人网络，不如集中精力建立自己所在社区的网络。这样做还有一个副作用，就是帮助我结识更多人并交朋友。求职：在职业生涯中的所有步骤中，这一步通常受到最多的关注。不幸的是，网络上有很多错误的建议。（例如，许多文章敦促与潜在雇主采取对抗态度，我认为这不是有益的。）尽管找到一份工作似乎是最终目标，但它只是职业生涯漫长旅程中的一小步。
个人纪律：很少有人会知道你是花周末时间学习，还是狂看电视剧，但随着时间的推移，人们会注意到其中的差异。许多成功人士在饮食、运动、睡眠、个人关系、工作、学习和自我照顾方面都养成了良好的习惯。这些习惯帮助他们不断前进，同时保持健康。
利他主义：我发现，那些在自己职业生涯的每个阶段都希望帮助他人的人，通常会为自己实现更好的结果。我们如何在建立自己令人兴奋的职业生涯的同时帮助他人呢？
Overcoming Imposter SyndromeBefore we dive into the final chapter of this book, I’d like to address the serious matter of newcomers to AI sometimes experiencing imposter syndrome, where someone — regardless of their success in the field — wonders if they’re a fraud and really belong in the AI community. I want to make sure this doesn’t discourage you or anyone else from growing in AI.
在我们进入本书的最后一章之前，我想要谈一件严肃的事情：新手在【人工智能】领域有时会经历“冒充综合征”，即无论他们在该领域的成就如何，都会怀疑自己是个骗子，不知道自己是否真的属于【人工智能】社区。我希望确保这不会让你或任何其他人对在【人工智能】领域取得进展感到泄气。
Let me be clear: If you want to be part of the AI community, then I welcome you with open arms. If you want to join us, you fully belong with us!
让我明确一点：如果你想成为【人工智能】社区的一员，我会毫不保留地欢迎你。如果你想加入我们，你完全属于我们！
An estimated 70 percent of people experience some form of imposter syndrome at some point. Many talented people have spoken publicly about this experience, including former Facebook COO Sheryl Sandberg, U.S. first lady Michelle Obama, actor Tom Hanks, and Atlassian co-CEO Mike Cannon-Brookes. It happens in our community even among accomplished people. If you’ve never experienced this yourself, that’s great! I hope you’ll join me in encouraging and welcoming everyone who wants to join our community.
据估计，70%的人在某个时候都会经历某种形式的冒充综合征。许多才华横溢的人公开谈论过这种经历，包括前Facebook首席运营官谢丽尔·桑德伯格、美国第一夫人米歇尔·奥巴马、演员汤姆·汉克斯和Atlassian联席首席执行官迈克·坎农-布鲁克斯。即使是在有成就的人中，这种情况也会发生在我们的社区中。如果你从未经历过这种情况，那太好了！
我希望你能与我一起鼓励和欢迎每一个想加入我们社区的人。AI is technically complex, and it has its fair share of smart and highly capable people. But it is easy to forget that to become good at anything, the first step is to suck at it. If you’ve succeeded at sucking at AI — congratulations, you’re on your way!
【人工智能】技术非常复杂，拥有一些聪明而高能的人。但很容易忘记，想要变得擅长任何事情，第一步是要不擅长它。如果你已经成功地不擅长【人工智能】，恭喜你，你正在走上成功之路！
I once struggled to understand the math behind linear regression. I was mystified when logistic regression performed strangely on my data, and it took me days to find a bug in my implementation of a basic neural network. Today, I still find many research papers challenging to read, and I recently made an obvious mistake while tuning a neural network hyperparameter (that fortunately a fellow engineer caught and fixed).
我曾经也很难理解【线性回归】背后的【数学】。当我的数据在逻辑回归中表现奇怪时，我感到困惑，而且我花了好几天的时间才找到一个基本神经网络实现中的一个错误。今天，我仍然发现许多研究论文很难读懂，最近在调整神经网络超参数时，我犯了一个显而易见的错误（幸运的是，一位同事发现并修复了它）。
So if you, too, find parts of AI challenging, it’s okay. We’ve all been there. I guarantee that everyone who has published a seminal AI paper struggled with similar technical challenges at some point. Here are some things that can help：
因此，如果你也觉得AI的某些部分具有挑战性，没关系。我们都曾经历过这个阶段。我保证，每个发表过开创性AI论文的人，在某个时刻都曾遇到类似的技术挑战。以下是一些有助于应对的方法：
Do you have supportive mentors or peers? If you don’t yet, attend Pie & AI or other events, use discussion boards, and work on finding some. If your mentors or manager don’t support your growth, find ones who do. I’m also working on how to grow a supportive AI community and hope to make finding and giving support easier for everyone.No one is an expert at everything. Recognize what you do well. If what you do well is understand and explain to your friends one-tenth of the articles in The Batch, then you’re on your way! Let’s work on getting you to understand two-tenths of the articles.你是否有支持你的导师或同行？如果还没有，请参加 Pie＆AI 或其他活动，使用讨论论坛，并努力寻找一些支持者。如果你的导师或经理不支持你的成长，请找到那些支持你的人。我也正在努力构建一个支持性的AI社区，并希望让大家更轻松地寻找和给予支持。
没有人是万事通。认识到自己擅长什么。如果你擅长的是理解和向朋友解释《The Batch》十分之一的文章，那么你已经在正确的道路上！
让我们一起努力，让你能够理解两成的文章。My three-year-old daughter (who can barely count to 12) regularly tries to teach things to my one-year-old son. No matter how far along you are — if you’re at least as knowledgeable as a three-year-old — you can encourage and lift up others behind you. Doing so will help you, too, as others behind you will recognize your expertise and also encourage you to keep developing. When you invite others to join the AI community, which I hope you will do, it also reduces any doubts that you are already one of us.
我的三岁女儿（她只能数到12）经常试图教一岁的儿子一些东西。无论你已经走得多远，只要你至少和一个三岁儿童一样有知识，你就可以鼓励和支持身后的人。这样做也会帮助你自己，因为身后的人会认识到你的专业知识，并鼓励你不断发展。当你邀请其他人加入AI社区时，这也减少了你已经是我们其中一员的任何怀疑。
AI is such an important part of our world that I would like everyone who wants to be part of it to feel at home as a member of our community. Let’s work together to make it happen.
AI是我们世界中非常重要的一部分，我希望所有想要成为其中一员的人都能感到自己是我们社区的一员。让我们一起努力实现这一目标。
Final ThoughtsMake Every Day Count 让每一天都有意义
Every year on my birthday, I get to thinking about the days behind and those that may lie ahead. Maybe you’re good at math; I’m sure you’ll be able to answer the following question via a quick calculation. But let me ask you a question, and please answer from your gut, without calculating.
每年到了我的生日，我就会思考已经过去的日子以及未来可能会发生的事情。也许你擅长【数学】，我相信你可以通过快速计算回答以下问题。但请让我问你一个问题，并请毋需计算即可从直觉上回答。 例如：一个典型的人类寿命有多少天？
20,000 days
100,000 days
1 million days
5 million days
When I ask friends, many choose a number in the hundreds of thousands. (Many others can’t resist calculating the answer, to my annoyance!)
当我问朋友时，许多人选择数十万的数字。（还有许多人不能估算出答案，这让我感到恼火！
）When I was a grad student, I remember plugging my statistics into a mortality calculator to figure out my life expectancy. The calculator said I could expect to live a total of 27,649 days. It struck me how small this number is. I printed it in a large font and pasted it on my office wall as a daily reminder. That’s all the days we have to spend with loved ones, learn, build for the future, and help others. Whatever you’re doing today, is it worth 1/30,000 of your life?
当我还是一名研究生时，我记得曾经将我的统计数据输入到一款死亡率计算器中，以便计算我的预期寿命。计算器显示我总共可以期望活 27,649 天。这个数字让我感到很小。我将它用大字体打印并贴在我的办公室墙上，作为每天提醒我的 醒世恒言。这就是我们拥有的所有时间，用来陪伴我们的亲人、学习、为未来打造、帮助他人。无论你今天在做什么，它值得你生命的 1/30,000 吗？
