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§ DeepLearning.Al § 深度学习.Al

A simple Guide  一个简单的指南

Collected Insights from Andrew Ng
《Andrew Ng 收集的见解》

Founder, DeepLearning.Al 创始人,DeepLearning.Al

"Al is the new electricity. It will transform and improve all areas of human life.'
"AI 是新的电力。它将改变并提升人类生活的各个方面。"

Andrew Ng

Table of Contents
Introduction: Coding Al is the New Literacy.
Chapter 1: Three Steps to Career Growth.
目录 引言:编码 AI 是新文化素养。 第一章:职业成长的三个步骤。

LEARNING 学习

PROJECTS 项目

Chapter 2: Learning Technical Skills for a
第二章:学习技术技能

Promising AI Career. 有前途的人工智能职业。
Chapter 3: Should You Learn Math to Get a Job in Al?
第三章:你该学数学才能在 AI 领域找工作吗?
Chapter 4: Scoping Successful Al Projects.
第四章:成功 AI 项目的范围界定。

Chapter 5: Finding Projects that Complement
第五章:寻找相辅相成的项目

Your Career Goals. 您的职业目标。
Chapter 6: Building a Portfolio of Projects that
第六章:构建项目组合

Shows Skill Progression.
显示技能进度。
Chapter 7: A Simple Framework for Starting Your AI Job Search.
第七章:开启 AI 求职之旅的简单框架。
Chapter 8: Using Informational Interviews to Find the Right Job.
第八章:利用信息面试寻找合适的工作。
Chapter 9: Finding the Right AI Job for You.
第九章:为你找到合适的 AI 工作。

Chapter 10: Keys to Building a Career in AI.
第 10 章:构建 AI 职业生涯的关键
Chapter 11: Overcoming Imposter Syndrome. Final Thoughts: Make Every Day Count.
第 11 章:克服冒名顶替综合症。结语:让每一天都算数。

Coding Al Is the New Literacy
编码即新 literacy

Today 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 Al.
今天,我们理所当然地认为许多人会读写。总有一天,我希望,人们知道如何编写代码,特别是为 AI 编写代码,将变得同样普遍。
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,社会并不认为语言读写能力是一项必要的技能。只有少数人学会了阅读和写作,其他人则让他们去阅读和写作。 literacy 的普及花了几个世纪,而现在社会因此变得更加丰富多彩。
Words enable deep human-to-human communication. Code is the deepest form of human-tomachine communication. As machines become more central to daily life, that communication becomes ever more important.
文字能够实现人与人之间深刻的交流。代码是人类与机器之间最深层次的交流方式。随着机器在日常生活中的地位日益重要,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.
传统软件工程——编写程序,明确告诉计算机执行一系列步骤——一直是代码素养的主要途径。许多入门级编程课程以创建视频游戏或构建网站为例。但人工智能、机器学习和数据科学提供了一种新的范式,其中计算机从数据中提取知识。这项技术为编码提供了一条更好的途径。
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).
许多星期天,我都会在我家附近的比萨店买一块比萨。柜台后面的那位先生没有太多理由去学习如何制作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之外)。
But Al 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.
但是,AI 和数据科学对于披萨师傅来说也具有极高的价值。一个线性回归模型可能帮助他更好地估算需求,从而优化餐厅的员工配置和供应链。他可以更准确地预测夏威夷披萨——我最喜欢的!——的销售情况,这样他就可以提前制作更多的夏威夷披萨,减少顾客等待的时间。
Uses of Al 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 Al applications and data-derived insights than for traditional software engineering. This makes literacy in Al-oriented coding even more valuable than traditional coding. It could enable countless individuals to harness data to make their lives richer.
人工智能和数据科学的应用几乎存在于产生数据的任何场合。因此,众多职业将发现定制人工智能应用和数据驱动的洞察比传统软件工程有更多用途。这使得以人工智能为导向的编码素养比传统编码更加宝贵。这可能会让无数人能够利用数据使自己的生活更加丰富。
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.
我希望能激发人们学习如何编写代码的热情,这不仅仅是构建基本人工智能应用的承诺,甚至比构建基本传统软件的承诺还要强烈。如果社会像接受阅读和写作能力一样接受这种新的读写能力,我们所有人都会从中受益。

CHAPTER 1 第一章

Three Steps to Career Growth
三步职业成长法

The rapid rise of Al has led to a rapid rise in Al 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 Al.
人工智能的快速发展导致了人工智能相关职位的快速增长,许多人在这片领域建立了令人激动的职业生涯。职业生涯是一场长达数十年的旅程,道路并不平坦。多年来,我有幸见证了数千名学生以及大小公司的工程师在人工智能领域的职业发展。
Here’s a framework for charting your own course.
这里是一个规划自己道路的框架。
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:
三個職業成長關鍵步驟是學習基礎技能、參與項目(以深化技能、建立作品集和創造影響),以及尋找工作。這些步驟相互堆疊:

Initially, you 最初,你
focus on learning foundational skills.
专注于学习基础技能。

Chapters with the 중 cover topics about learning foundational technical skills.
章节中包含有关学习基础技术技能的主题。
After having gained foundational technical skills, you will begin working on projects.
在掌握基础技术技能之后,你将开始参与项目工作。

During this period, you’ll also keep learning. Chapters with the 장 focus on projects.
在这个阶段,你还将继续学习。专注于项目的章节将会有“장”这一重点。
Later, you will work on finding a job.
稍后,你将开始寻找工作。

Throughout this process, you’ll continue to learn and work on meaningful projects. Chapters with the (圄) focus on a job search.
在整个过程中,你将继续学习和参与有意义的课题。本章着重于求职。

These phases apply in a wide range of professions, but Al involves unique elements. For example:
这些阶段适用于众多职业,但 Al 涉及独特的元素。例如:


LEARNING 学习
PROJECTS 项目
JOB 工作
Learning foundational skills is a career-long process:
学习基础技能是一个终身的过程:

Al is nascent, and many technologies are still evolving. While the foundations of machine learning and deep learning are maturing and coursework is an efficient way to master them - beyond these foundations, keeping up-to-date with changing technology is more important in Al than fields that are more mature.
人工智能尚处于起步阶段,许多技术仍在不断发展。虽然机器学习和深度学习的基础正在成熟,课程学习是掌握这些基础的有效途径,但在这些基础之外,跟上技术变化的步伐在人工智能领域比在更为成熟的技术领域更为重要。

Working on projects often means collaborating with stakeholders who lack expertise in AI:
在处理项目时,常常意味着需要与缺乏人工智能专业知识的利益相关者进行合作:

This can make it challenging to find a suitable project, estimate the project’s timeline and return on investment, and set expectations. In addition, the highly iterative nature of Al projects leads to special challenges in project management: How can you come up with a plan for building a system when you don’t know in advance how long it will take to achieve the target accuracy? Even after the system has hit the target, further iteration may be necessary to address post-deployment drift.
这可能会使得寻找合适的项目变得具有挑战性,估算项目的进度和投资回报,以及设定预期都变得困难。此外,AI 项目的极高迭代性给项目管理带来了特殊的挑战:当你不知道达到目标准确度需要多长时间时,你该如何制定构建系统的计划?即使系统已经达到目标,进一步的迭代也可能有必要来解决部署后的漂移问题。

Inconsistent opinions on Al skills and jobs roles:
关于人工智能技能和职业角色的意见不一致:

While searching for a job in Al can be similar to searching for a job in other sectors, there are also important differences. Many companies are still trying to figure out which Al skills they need, and how to hire people who have them. Things you’ve worked on may be significantly different than anything your interviewer has seen, and you’re more likely to have to educate potential employers about some elements of your work.
在阿联酋找工作可能与其他行业类似,但也存在一些重要差异。许多公司仍在努力确定他们需要哪些人工智能技能,以及如何招聘具备这些技能的人。你所从事的工作可能与面试官所见到的有很大不同,你更有可能需要向潜在雇主介绍你工作中的一些元素。
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.
随着你走过每一步,你也应该建立一个支持性的社区。拥有能够帮助你——并且你努力去帮助——的朋友和盟友会使这条路更容易走。这无论你是刚开始起步,还是已经在这条路上走了很多年,都是如此。

CHAPTER 2 第二章

Learning Technical Skills for a Promising Al Career
学习为光明的 AI 职业生涯掌握技术技能
In the previous chapter, I introduced three key steps for building a career in Al: 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.
. 在前一章中,我介绍了构建人工智能职业生涯的三个关键步骤:学习基础技术技能、参与项目以及寻找工作,这一切都离不开加入一个社区的支持。在本章中,我将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:
更多关于人工智能的研究论文已经发表,任何人一生都无法全部阅读。因此,在学习时,选择主题至关重要。我认为,对于机器学习领域的技术职业生涯来说,最重要的主题是:
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.
基础机器学习技能:例如,了解线性回归、逻辑回归、神经网络、决策树、聚类和异常检测等模型非常重要。除了具体的模型之外,理解机器学习背后的核心概念,比如偏差/方差、损失函数、正则化、优化算法和错误分析,更是至关紧要。
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.
information 深度学习:这已经成为机器学习的一个很大部分,没有对其有所了解就难以在领域内脱颖而出!了解神经网络的基础知识、使它们工作所需的实用技能(如超参数调整)、卷积网络、序列模型和转换器等都是非常有价值的。
Math relevant to machine learning: Key areas include linear algebra (vectors, matrices, and various manipulations of them) as well as probability and statistics (including discrete and continuous probability, standard probability distributions, basic rules such as independence and Bayes’ rule, and hypothesis testing). In addition, exploratory data analysis (EDA) — using visualizations and other methods to systematically explore a dataset — is an underrated skill. I’ve found EDA particularly useful in data-centric Al development, where analyzing errors and gaining insights can really help drive progress! Finally, a basic intuitive understanding of calculus will also help. The math needed to do machine learning well has been changing. For instance, although some tasks require calculus, improved automatic differentiation software makes it possible to invent and implement new neural network architectures without doing any calculus. This was almost impossible a decade ago.
机器学习相关的数学:关键领域包括线性代数(向量、矩阵及其各种操作)、概率论与数理统计(包括离散和连续概率、标准概率分布、基本规则如独立性和贝叶斯定理,以及假设检验)。此外,探索性数据分析(EDA)——利用可视化和其他方法系统地探索数据集——是一项被低估的技能。我发现 EDA 在以数据为中心的机器学习开发中特别有用,分析错误和获得洞察力可以真正推动进步!最后,对微积分的基本直观理解也将有所帮助。进行机器学习所需的数学知识正在发生变化。例如,尽管一些任务需要微积分,但改进的自动微分软件使得在不进行微积分的情况下发明和实现新的神经网络架构成为可能。这在十年前几乎是不可想象的。
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 Al 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.
软件开发:虽然仅凭机器学习建模技能就能找到工作并做出巨大贡献,但如果你还能编写优秀的软件来实施复杂的人工智能系统,你的就业机会将会增加。这些技能包括编程基础、数据结构(尤其是与机器学习相关的,如数据框)、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,以及 scikit-learn。

This is a lot to learn!
这有很多东西要学!

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.
即便你掌握了这份清单上的所有内容,我也希望你能持续学习,不断深化你的技术知识。我认识许多机器学习工程师,他们从自然语言处理或计算机视觉等应用领域的深入技能,或从概率图模型或构建可扩展的软件系统等技术领域的深入技能中受益。
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.
如何获得这些技能?互联网上有很多优质内容,从理论上讲,阅读数十个网页可能有效。但当目标是深入理解时,阅读零散的网页效率低下,因为它们往往重复,使用不一致的术语(这会减慢你的速度),质量参差不齐,并且留下空白。这就是为什么一个好的课程——其中一组材料已经被组织成连贯和逻辑的形式——通常是掌握有意义知识体系最节省时间的方法。当你吸收了课程中的知识后,你可以转向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,没有人能在周末甚至一个月内掌握所有需要知道的知识。我所认识的在机器学习方面出色的人都是终身学习者。鉴于我们的领域变化如此之快,如果你想跟上,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.
如何保持多年的学习节奏?如果你能养成每周学习一点点的习惯,你就可以在看似不那么费力的过程中取得显著的进步。


The Best Way to Build a New Habit
最好的方法来养成新习惯

One of my favorite books is BJ Fogg’s, Tiny Habits: The Small Changes That Change Everything. Fogg explains that the best way to build a new habit is to start small and succeed, rather than start too big and fail. For example, rather than trying to exercise for 30 minutes a day, he recommends aspiring to do just one push-up, and doing it consistently.
我最喜欢的书之一是 BJ Fogg 的《小习惯:微小改变的力量》。Fogg 解释说,建立新习惯的最佳方式是从小事做起并取得成功,而不是一开始就设定过高的目标导致失败。例如,与其试图每天锻炼 30 分钟,他建议目标是只做一次俯卧撑,并且持之以恒地去做。
This approach may be helpful to those of you who want to spend more time studying. If you start by holding yourself accountable for watching, say, 10 seconds of an educational video every day - and you do so consistently — the habit of studying daily will grow naturally. Even if you learn nothing in that 10 seconds, you’re establishing the habit of studying a little every day. On some days, maybe you’ll end up studying for an hour or longer.
这种方法可能对那些想要花更多时间学习的人有所帮助。如果你从每天观看 10 秒的教育视频开始,并且持之以恒地这样做——那么,每天学习的习惯就会自然而然地形成。即使在这 10 秒内你什么也没学到,你也在培养每天学习一点点的习惯。在某些日子里,你可能最终会学习一个小时或更长时间。

CHAPTER 3 第三章

Should You Learn Math to Get a Job in Al?
你应该学习数学来在 AI 领域找工作吗?

How much math do you need to know to be a machine learning engineer?
你需要掌握多少数学知识才能成为一名机器学习工程师?

Is math a foundational skill for Al? 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.
数学是人工智能的基础技能吗?多学点数学总是好的!但学习内容太多,实际上需要有所侧重。以下是如何加强你的数学背景的方法。
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.
为了弄清楚需要了解什么,我发现询问“为了完成你想要从事的工作所需的决策,你需要知道什么”非常有用。在 DeepLearning.AI,我们经常问:“为了实现目标,一个人需要知道什么?”目标可能是构建机器学习模型、设计系统或通过工作面试。
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.
理解你所使用算法背后的数学原理通常很有帮助,因为它能帮助你调试它们。但随着机器学习技术的成熟和可靠性提高,它们需要的调试工作减少,对涉及数学知识的理解可能只需要浅显即可使它们运行。
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,在机器学习早期阶段,用于求解线性方程组(用于线性回归)的线性代数库还不够成熟。我不得不深入了解这些库的工作原理,以便在众多库中选择合适的,并避免数值舍入误差。但随着数值线性代数库的成熟,这一点变得不那么重要了。
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. 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.
深度学习仍然是一种新兴技术,因此当您训练神经网络并且优化算法难以收敛时,理解梯度下降、动量以及 Adam 优化算法背后的数学原理将帮助您做出更好的决策。同样,如果您的神经网络出现了一些奇怪的行为——比如说,它在某些分辨率的图像上做出错误的预测,而在其他分辨率上则不会——理解神经网络架构背后的数学原理将使您更有能力找出解决问题的方法。
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.
当然,我也鼓励由好奇心驱动的学习。如果你对某件事感兴趣,那就去学习它,不管它最终是否有用!也许这会激发你的创造力或带来技术突破。

CHAPTER 4 第四章

Scoping Successful Al Projects
成功规划 AI 项目
One of the most important skills of an Al 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.
一个优秀的架构师最重要的技能之一就是能够识别出值得投入精力的想法。接下来的几章将讨论如何寻找和开展项目,以便您能够积累经验并构建自己的作品集。
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,我一直在享受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、广告以及其他行业的乐趣。那么,一个不是所有这些领域专家的人如何在这些行业中找到有意义的工程项目呢?以下有五个步骤可以帮助你确定项目范围。

Step 1 步骤 1

Identify a business problem (not an Al 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.
确定一个商业问题(不是 AI 问题)。我喜欢找一个领域专家,问他:“你最希望改进的前三项是什么?为什么它们还没有得到改善?”例如,如果你想将 AI 应用于气候变化,你可能会发现电网运营商无法准确预测未来间歇性能源如风能和太阳能可能产生的电力量。

Step 2 步骤 2

Brainstorm Al 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
大脑风暴 AI 解决方案。在我年轻的时候,我总是对第一个让我兴奋的想法立即执行。有时候这还不错,但有时候我错过了可能需要付出同样努力却更好的想法。一旦你理解了问题,你就可以更有效地进行头脑风暴,寻找可能的解决方案。例如,为了预测间歇性能源的发电量,我们可能会考虑使用卫星图像更准确地绘制风力涡轮机的位置,利用卫星图像估算风力涡轮机的高度和发电能力。


turbines, or using weather data to better predict cloud cover and thus solar irradiance. Sometimes there isn’t a good Al solution, and that’s okay too.
涡轮机,或者利用气象数据来更好地预测云层覆盖,从而预测太阳辐射。有时没有好的解决方案,那也行。

Step 3 步骤 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).
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文献、观察竞争对手的做法,或者构建一个快速的概念验证实现来判定一个方法的技术可行性。您可以通过咨询领域专家(例如,电网运营商,他们可以就上述潜在解决方案的实用性提供建议)来确定其价值。

Step 4 第 4 步

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.
确定里程碑。一旦你认为一个项目足够有价值,下一步就是确定目标指标。这包括机器学习指标(如准确率)和业务指标(如收入)。机器学习团队通常更熟悉那些学习算法可以优化的指标。但我们可能需要跳出舒适区,提出与用户参与度、收入等相关的业务指标。不幸的是,并非每个业务问题都可以简化为优化测试集准确率!如果你无法确定合理的里程碑,这可能意味着你需要更多地了解这个问题。一个快速的概念验证可以帮助提供缺失的视角。

Step 5 第 5 步

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.
资源预算。仔细考虑完成项目所需的一切,包括数据、人员、时间以及可能需要的其他团队的支持或集成。例如,如果您需要资金购买卫星图像,确保这一点包含在预算中。

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 Al 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. Al won’t solve every problem, but as a community, let’s look for ways to make a positive impact wherever we can.
项目工作是一个迭代的过程。如果在任何一步中发现当前的方向不可行,请回到早期步骤,并带着新的理解继续前进。有没有一个领域让您感到兴奋,AI 可能在那里产生影响?我希望这些步骤能引导您通过项目工作来探索它——即使您在这个领域还没有深厚的专业知识。AI 不能解决所有问题,但作为一个社区,让我们寻找我们能在哪里产生积极影响的方法。

CHAPTER 5 第五章

Finding Projects that Complement Your Career Goals
寻找与您职业目标相匹配的项目
It 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 Al projects. This chapter and the next have a slightly different emphasis: picking and executing projects with an eye toward career development.
不言而喻,我们只应致力于那些对人们负责、符合伦理且有益于人类的项目。但这些限制为我们提供了大量的选择。在前一章中,我讲述了如何识别和界定 AI 项目。本章和下一章将略有不同,重点是着眼于职业发展来挑选和执行项目。
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.
一个成功的职业生涯将包括许多项目,希望随着时间的推移,项目规模、复杂性和影响力都能逐渐增长。因此,从小项目开始是完全可以的。利用早期项目来学习和积累经验,随着技能的提升,逐步承担更大的项目。
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.
当你刚开始时,不要期待别人会把好点子或资源端上桌来。许多人都是从利用业余时间做小项目开始的。在你积累了一些初始的成功——即使是小的成功——之后,你的技能增长将增强你提出更好想法的能力,并且更容易说服别人帮助你承担更大的项目。

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,这里有一些生成想法的方法:

\checkmark Join existing projects. If you find someone else with an idea, ask to join their project.
\checkmark 加入现有项目。如果你发现有人有想法,就请加入他们的项目。

✓quad\checkmark \quad 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.
✓quad\checkmark \quad 持续阅读和与人交流。每当我花大量时间阅读、上课或与领域专家交谈时,我都会产生新的想法。我坚信你们也会如此。

✓quad\checkmark \quad Focus on an application area. Many researchers are trying to advance basic Al 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.
✓quad\checkmark \quad 关注一个应用领域。许多研究人员正在努力推进基本人工智能技术——比如,发明下一代变压器或进一步扩大语言模型规模——虽然这是一个令人兴奋的方向,但也非常困难。但机器学习尚未被应用到的应用领域非常广泛!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各个方面,部分原因是我当时是少数研究这些应用的人之一。如果你的公司或学校关注某个特定应用,探索机器学习的可能性。这可以让你先睹为快一个可能具有创造性的应用——一个你可以独辟蹊径的地方——别人还没有做过。

\checkmark 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.
\checkmark 发展副业。即使你有全职工作,一个可能或可能不会发展成更大项目的有趣项目也能激发创造力,加强与合作伙伴的联系。当我是一名全职教授时,从事在线教育并不属于我的“工作”(我的工作是进行研究和教授课程)。这只是一个出于对教育的热情而经常在业余时间从事的有趣爱好。我在家录制视频的早期经验后来帮助我在在线教育方面进行了更实质性的工作。硅谷充满了以副业起家的创业公司的故事。只要这些项目不会与你的雇主产生冲突,它们就可以成为通向重大成就的垫脚石。

Given a few project ideas, which one should you jump into? Here's a quick checklist of factors to consider:
以下是一些项目想法,您应该选择哪一个来着手?以下是一些快速检查的因素:

\checkmark 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.
该项目能帮助你技术成长吗?理想情况下,它应该足够具有挑战性以拓展你的技能,但又不能过于困难以至于你成功的机会很小。这将使你走上掌握更高技术复杂性的道路。

\checkmark 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.
你有优秀的队友一起工作吗?如果没有,你能否与别人讨论问题?我们从周围的人那里学到很多东西,好的合作伙伴将对你的成长产生巨大影响。
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.
它能否成为垫脚石?如果项目成功,其技术复杂度或商业影响能否使其成为通向更大项目的有意义垫脚石?如果你之前参与的项目规模更大,那么它很可能就是这样的垫脚石。
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 Al 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。花一个月的时间来决定是否要开始一个只需一周就能完成的项目是没有意义的。你将在职业生涯中从事多个项目,因此你将有充足的机会来完善你对什么值得做的思考。鉴于可能的 AI 项目数量庞大,与其采取传统的“准备、瞄准、射击”方法,不如用“准备、射击、瞄准”来加速你的进步。

Ready, Fire, Aim 准备,开火,瞄准

Working on projects requires making tough choices about what to build and how to go about it. Here are two distinct styles:
项目开发需要就建设内容和方式做出艰难的选择。以下是两种不同的风格:

\checkmark Ready, Aim, Fire: Plan carefully and carry out careful validation. Commit and execute only when you have a high degree of confidence in a direction.
准备,瞄准,开火:精心制定计划,仔细进行验证。只有在你对方向有高度信心时,才做出承诺并执行。

\checkmark Ready, Fire, Aim: Jump into development and start executing. This allows you to discover problems quickly and pivot along the way if necessary.
准备好,开火,瞄准:直接进入开发并开始执行。这样可以快速发现问题,并在必要时进行转向。
Say you’ve built a customer-service chatbot for retailers, and you think it could help restaurants, too. Should you take time to study the restaurant market before starting development, moving slowly but cutting the risk of wasting time and resources? Or jump in right away, moving quickly and accepting a higher risk of pivoting or failing?
假设您为零售商开发了一个客户服务聊天机器人,您认为它也可以帮助餐厅。您应该在开始开发之前花时间研究餐饮市场,稳步推进以降低浪费时间和资源的风险,还是立即行动,快速推进,接受更高的转型或失败的风险?
Both approaches have their advocates, and the best choice depends on the situation.
两种方法都有其支持者,最佳选择取决于具体情况。
Ready, Aim, Fire tends to be superior when the cost of execution is high and a study can shed light on how useful or valuable a project could be. For example, if you can brainstorm a few other use cases (restaurants, airlines, telcos, and so on) and evaluate these cases to identify the most promising one, it may be worth taking the extra time before committing to a direction.
准备、瞄准、开火在执行成本高昂时往往更优越,一项研究可以揭示一个项目可能的有用性或价值。例如,如果你能头脑风暴出一些其他用例(如餐馆、航空公司、电信公司等),并对这些用例进行评估以确定最有希望的用例,那么在做出决定之前花些额外的时间可能是值得的。
Ready, Fire, Aim tends to be better if you can execute at low cost and, in doing so, determine whether the direction is feasible and discover tweaks that will make it work. For example, if you can build a prototype quickly to figure out if users want the product, and if canceling or pivoting after a small amount of work is acceptable, then it makes sense to consider jumping in quickly. When taking a shot is inexpensive, it also makes sense to take many shots. In this case, the process is actually Ready, Fire, Aim, Fire, Aim, Fire, Aim, Fire.
准备好,开火,瞄准——如果能在低成本下执行,并在此过程中确定方向是否可行,发现使其成功的调整,那么这种方法通常更好。例如,如果你能快速构建原型来了解用户是否需要该产品,并且在小量工作后取消或转型是可以接受的,那么快速行动是有意义的。当开火成本较低时,也意味着可以开很多枪。在这种情况下,这个过程实际上是准备好,开火,瞄准,开火,瞄准,开火,瞄准,开火。
After agreeing upon a project direction, when it comes to building a machine learning model that’s part of the product, I have a bias toward Ready, Fire, Aim. Building models is an iterative process. For many applications, the cost of training and conducting error analysis is not prohibitive. Furthermore, it is very difficult to carry out a study that will shed light on the appropriate model, data, and hyperparameters. So it makes sense to build an end-to-end system quickly and revise it until it works well.
在确定项目方向后,当涉及到构建作为产品一部分的机器学习模型时,我倾向于“先开枪,再瞄准”。构建模型是一个迭代的过程。对于许多应用来说,训练和进行错误分析的成本并不高。此外,进行一项能够阐明适当模型、数据和超参数的研究非常困难。因此,快速构建一个端到端系统并不断修订直到其运行良好是很有道理的。
But when committing to a direction means making a costly investment or entering a oneway door (meaning a decision that’s hard to reverse), it’s often worth spending more time in advance to make sure it really is a good idea.
但是,当坚持一个方向意味着进行一笔高昂的投资或进入一个单行道(意味着一个难以逆转的决定)时,提前花更多的时间来确保这确实是一个好主意通常是值得的。

CHAPTER 6 第六章

Building a Portfolio of Projects that Shows Skill Progression
构建展示技能进步的项目组合
Over 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,你可能会连续参与多个项目,每个项目的规模和复杂度都在不断增长。例如:

1. Class projects: 1. 课程项目:

The first few projects might be narrowly scoped homework assignments with predetermined right answers. These are often great learning experiences!
最初的一些项目可能是范围较窄的作业,具有预定的正确答案。这些通常是极好的学习经历!

2. Personal projects 2. 个人项目

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
你可能会继续参与小型项目,无论是独自还是与朋友合作。例如,你可能重新实现一个已知的算法,将机器学习应用于你的爱好(比如预测你最喜欢的运动队是否会赢),或者在工作之余构建一个小巧但实用的系统。


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.
作为一款基于机器学习的脚本,帮助同事自动化部分工作。参加如 Kaggle 举办的竞赛也是积累经验的一种方式。

3. Creating value 3. 创造价值

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.
最终,你将获得足够的技能来构建他人能感受到更多实际价值的项目。这将为你的资源打开大门。例如,你不再只是在业余时间开发机器学习系统,这可能会成为你的工作内容,你可能会获得更多设备、计算时间、标注预算或人员编制的访问权限。

4. Rising scope and complexity
4. 范围和复杂性不断提升

Successes build on each other, opening the door to more technical growth, more resources, and increasingly significant project opportunities.
成功层层累积,为更深入的技术发展、更多资源和越来越重要的项目机会打开了大门。

Each project is only one step on a longer journey, hopefully one that has a positive impact. In addition:
每个项目只是更长旅程中的一步,希望这是一段能够产生积极影响的旅程。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.
不用担心起点太小。我最早的一个机器学习研究项目是训练一个神经网络来模拟正弦函数,效果并不理想,但这是一次极好的学习经历,让我能够继续进行更大的项目。
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.
沟通至关重要。如果你想让别人看到你工作的价值,并信任你分配资源以投入更大项目,你就需要能够解释你的思考。为了启动一个项目,阐述你希望构建的价值将有助于让同事、导师和经理们支持你,并帮助他们指出你推理中的缺陷。在你完成项目后,清晰地解释你所取得的成就将有助于说服别人为你打开通往更大项目的门。
Leadership isn’t just for managers. When you reach the point of working on larger Al 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.
领导力不仅仅是管理者的专利。当你开始参与更大型的 AI 项目,这些项目需要团队合作时,你的项目管理能力将变得更加重要,无论你是否担任正式的领导职位。我的许多朋友都成功地选择了技术而非管理职业,他们通过运用深入的技术见解来引导项目——例如,何时投资于新的技术架构或收集特定类型的数据——这不仅帮助他们成长为领导者,也极大地提升了项目质量。
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.
构建一个项目组合,尤其是展示从简单到复杂项目逐步进展的,在寻找工作时将大有裨益。

CHAPTER 7 第七章

A Simple Framework for Starting Your AI Job Search
一个简单的框架助你开启 AI 求职之路
Finding 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, l’d like to focus on a framework that’s useful for many job seekers in Al, especially those who are entering Al from a different field.
找工作有几个可预测的步骤,包括选择想要申请的公司、准备面试,最后选择职位并协商薪资和福利。在本章中,我想重点介绍一个对许多求职者,尤其是那些从不同领域进入 AI 领域的求职者有用的框架。
If you’re considering your next job, ask yourself:
如果你正在考虑你的下一份工作,问问自己:

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.
您是在转换角色吗?例如,如果您是一名软件工程师、大学生或物理学家,现在想成为机器学习工程师,这就是一种角色转换。

\checkmark 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.
您是否正在转换行业?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,而想进入软件公司工作,那么这就是一个行业的转换。

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.
一位在科技初创公司担任产品经理的人,后来在同一公司(或不同公司)成为数据科学家,已经转换了角色。一位在制造企业担任市场营销人员的人,后来在科技公司担任市场营销人员,已经转换了行业。一位在金融服务公司担任分析师的人,后来在科技公司成为机器学习工程师,已经转换了角色和行业。
If you’re looking for your first job in Al, 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:
如果你在阿尔寻找你的第一份工作,你可能会发现同时转换角色或行业比同时进行两者更容易。比如说,你是一名在金融服务行业工作的分析师:

\checkmark 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 Al. 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).
如果在金融服务领域找到数据科学或机器学习的工作,你可以在继续运用你的专业知识的同时,获得人工智能方面的知识和技能。在担任这一职位一段时间后,你将更有条件转向科技公司(如果这仍然是你的目标)。

\checkmark 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 Al, key skills to be successful in Al , and so on.
“或者,如果你成为了一家科技公司的分析师,你仍然可以继续运用你的分析技能,但将其应用于不同的行业。成为科技公司的成员也使得你更容易从同事那里学习到关于人工智能的实际挑战、在人工智能领域取得成功的关键技能等等。”

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 Al 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.
如果您考虑转换角色,初创公司可能比大型公司更容易实现。虽然存在例外,但初创公司通常人手不足,无法完成所有期望的工作。如果您能够帮助处理 AI 任务——即使这不是您的正式工作——您的贡献很可能会得到认可。这为在不离职的情况下进行角色转换奠定了基础。相比之下,在大型公司中,僵化的奖励体系更可能奖励您出色地完成本职工作(以及您的经理支持您完成招聘时的工作),但不太可能奖励超出工作范围外的贡献。
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.
在您在理想角色和行业(例如,在科技公司担任机器学习工程师)工作了一段时间后,您将对该行业该角色的更高层次要求有很好的了解。您还将拥有一个在该行业内的网络,以帮助您前进。因此,如果您选择继续留在该角色和行业,未来的求职过程可能会更容易。
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.
当转换工作时,你正迈入未知领域,尤其是如果你正在转换角色或行业。了解新角色和/或行业最被忽视的工具之一就是信息面试。我将在下一章中详细介绍这一点。
I’m grateful to Salwa Nur Muhammad, CEO of FourthBrain (a DeepLearning.Al affiliate), for providing some of the ideas presented in this chapter.
我对 Salwa Nur Muhammad,FourthBrain(DeepLearning.Al 的附属公司)的 CEO,为其提供本章中的一些想法表示感激。

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?
未来有很多我们不知道的事情:我们何时能治愈阿尔茨海默病?下届选举谁会获胜?或者,在商业环境中,明年我们将有多少客户?
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:
在世界发生如此多变化的情况下,许多人对于未来感到压力重重,尤其是在找工作方面。我有一个帮助我恢复控制感的练习。面对不确定性,我会尝试:
Make a list of plausible scenarios, acknowledging that I don’t know which will come to pass.
列出一些可能发生的情景,尽管我不知道哪些会成真。
2 Create a plan of action
2 制定行动计划
Review scenarios and plans periodically as the future comes into focus.
定期回顾评估情景和计划,随着未来逐渐清晰。
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 COVID-19 大流行期间,我进行了情景规划练习。我设想了从 COVID-19 中快速(三个月)、中等(一年)和缓慢(两年)恢复的情况,并为每种情况制定了计划。这些计划帮助我确定了优先事项。
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.
同样的方法也适用于个人生活。如果你不确定能否通过考试、获得工作机会或获得签证——这些事情都可能带来压力——你可以列出在每种可能的情况中你会做什么。思考各种可能性并执行计划,可以帮助你无论未来带来什么都能有效地应对。
Bonus: With training in AI and statistics, you can calculate a probability for each scenario. I’m a fan of the Superforecasting methodology, in which the judgments of many experts are synthesized into a probability estimate.
奖励:通过人工智能和统计学培训,你可以为每个场景计算一个概率。我是超级预测法的粉丝,这种方法将许多专家的判断综合成一个概率估计。

CHAPTER 8 第八章

Using Informational Interviews to Find the Right Job
使用信息面试寻找合适的工作
If 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 Al 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.
如果你即将转换角色(比如,第一次担任机器学习工程师)或者行业(比如,第一次在 AI 科技公司工作),你可能会对你目标工作的很多方面一无所知。一种称为信息面试的技术是学习的好方法。
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.
信息面试是指找到一家公司或一个你想要了解更多信息的职位,并对其进行非正式的访谈,了解他们的工作情况。这类谈话与找工作是分开的。实际上,在开始找工作之前,了解与你兴趣相符的职位的人是非常有帮助的。

\checkmark Informational interviews are particularly relevant to Al. 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 Al people in a particular company actually do.
信息面试对于 AI 领域尤为重要。由于该领域正在不断发展,许多公司对职位名称的使用方式不一致。在一个公司,数据科学家可能主要被期望分析业务数据并在幻灯片上展示结论。在另一个公司,他们可能需要编写和维护生产代码。信息面试可以帮助你弄清楚特定公司中的 AI 人员实际上做什么。

\checkmark With the rapid expansion of opportunities in Al, many people will be taking on an Al 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 Al movement - to learn how much time most machine learning engineers spend iteratively cleaning datasets.
随着人工智能(AI)领域机会的迅速扩张,许多人将首次从事 AI 相关工作。在这种情况下,进行一次信息面试对于了解工作内容和所需技能非常有价值。例如,你可以了解特定公司使用的算法、部署流程和软件栈。如果你之前并不熟悉以数据为中心的 AI 运动,可能会惊讶地发现,大多数机器学习工程师在迭代清洗数据集上花费了大量的时间。
Prepare for informational interviews by researching the interviewee and company in advance, so you can arrive with thoughtful questions. You might ask:
为信息面试做好准备,提前对被面试者和公司进行调研,以便带着深思熟虑的问题到来。你可能想问:

\checkmark What do you do in a typical week or day?
在您典型的一周或一天中,您都做些什么?

\checkmark What are the most important tasks in this role?
\checkmark 这个角色最重要的任务是什么?

\checkmark What skills are most important for success?
\checkmark 哪些技能对于成功最重要?”

\checkmark How does your team work together to accomplish its goals?
您的团队是如何共同协作以实现目标的?

\checkmark What is the hiring process?
\checkmark 招聘流程是怎样的?”

\checkmark Considering candidates who stood out in the past, what enabled them to shine?
考虑到过去脱颖而出的候选人,是什么因素使他们脱颖而出?
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 & Al can also help you build your network.
寻找采访对象并不总是容易,但今天许多担任高级职位的人,在他们初入职场时都得到了那些先他们进入该领域的人的帮助,而且许多人也乐于将这份帮助传递下去。如果你能联系到你网络中的人,比如在你之前完成转变的朋友,或者和你上过同一所学校的人——那就太好了!Pie & Al 这样的聚会也能帮助你拓展人脉。
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.Al 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,请保持礼貌和专业,感谢您所采访的人。有机会的话,也请将这份善意传递下去,帮助那些在您之后的人。如果您收到来自 DeepLearning.Al 社区的信息面试请求,我希望您能伸出援手,帮助他们迈出一步!如果您想了解更多关于信息面试的内容,我推荐您阅读加州大学伯克利分校职业中心的文章。
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.
我已经多次提到过,你的社交圈和社区的重要性。你遇到的人,除了提供有价值的信息外,还可以通过推荐你到潜在雇主那里,发挥无价的作用。

CHAPTER 9 第九章

Finding the Right Al Job for You
找到适合您的合适工作

In this chapter, I'd like to discuss some fine points of finding a job.
在这一章中,我想讨论一些找工作的要点。

The typical job search follows a fairly predictable path.
典型的求职过程遵循一条相对可预测的路径。

\checkmark Research roles and companies online or by talking to friends.
在网络上或通过和朋友交谈来了解研究职位和公司。

\checkmark Optionally, arrange informal informational interviews with people in companies that appeal to you.
可选,与您感兴趣的公司人员进行非正式的信息访谈。

\checkmark Either apply directly or, if you can, get a referral from someone on the inside.
直接申请,或者如果可能的话,从内部人员那里获得推荐。

\checkmark Interview with companies that give you an invitation.
\checkmark 面试邀请的公司访谈。”

\checkmark 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.
收到一个或多个报价后,选择其中一个。或者,如果您没有收到报价,可以向面试官、人力资源人员、在线讨论板或任何可以帮助您规划下一步行动的联系人寻求反馈。
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.
尽管这个过程可能很熟悉,但每一次求职都是独一无二的。以下是一些提高你找到支持你蓬勃发展的事业并使你能够继续成长的职位的几条建议。
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.
请注意基础。一份吸引人的简历、技术项目集和出色的面试表现将打开大门。即使你有公司内部人士的推荐,简历和项目集也将是你与许多不熟悉你的人的首次接触。更新你的简历,确保它清晰地展示与你想申请的职位相关的教育和经验。针对每家公司定制你的沟通,解释为什么你是一个合适的人选。在面试前,询问招聘人员可以期待什么。花时间回顾和练习常见面试问题的答案,提升关键技能,并学习技术材料,确保它们在你的脑海中保持新鲜。之后,记笔记以帮助你记住所说过的话。
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.
请尊重并负责任地行事。以双赢的心态对待面试和谈判。在社交媒体上,愤怒比理性传播得更快,因此关于雇主欠薪的故事会被放大,而关于雇主公平对待员工的故事则不会。绝大多数雇主都是道德和公平的,所以不要让少数受虐待者的故事影响你的态度。如果你要离职,请优雅地离开。提前通知你的雇主,在你工作的最后时刻全力以赴,尽可能过渡未完成的工作,并以一种尊重你被托付的责任的方式离开。
Choose who to work with. It’s tempting to take a position because of the projects you’ll work on. But the teammates you’ll work with are at least equally important. We’re influenced by people around us, so your colleagues will make a big difference. For example, if your friends smoke, the odds increase that you, too, will smoke. I don’t know of a study that shows this, but l’m pretty sure that if most of your colleagues work hard, learn continuously, and build AI to benefit all people, you’re likely to do the same. (By the way, some large companies won’t tell you who your teammates will be until you’ve accepted an offer. In this case, be persistent and keep pushing to identify and speak with potential teammates. Strict policies may make it impossible to accommodate you, but in my mind, that increases the risk of accepting the offer, as it increases the odds you’ll end up with a manager or teammates who aren’t a good fit.)
选择你愿意与之共事的人。虽然因为将要参与的项目而接受职位很有吸引力,但与你共事的队友至少同样重要。我们受到周围人的影响,所以你的同事会对你产生重大影响。例如,如果你的朋友吸烟,你吸烟的概率也会增加。我不知道有没有研究显示这一点,但我很确定,如果你的大部分同事都努力工作、持续学习并致力于构建造福所有人的 AI,你也很可能这么做。(顺便说一句,一些大公司在你接受工作邀请之前不会告诉你你的队友是谁。在这种情况下,你要坚持不懈,不断争取了解并和潜在的队友交流。严格的政策可能使你无法得到满足,但在我看来,这增加了接受邀请的风险,因为它增加了你最终会遇到不合适的经理或队友的概率。)
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.
从您的社区寻求帮助。我们大多数人一生中求职的次数并不多,因此很少有人在这方面有太多实践经验。然而,您身边的社区成员可能积累了丰富的经验。不要害羞,大胆地向他们求助。朋友和同事可以提供建议,分享内部信息,并将您推荐给可能帮助您的人。我在申请第一个教职时,得到了许多支持性朋友和导师的帮助,他们给出的许多建议都非常有用。
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.
我知道求职过程可能会让人感到害怕。与其将其视为一次巨大的飞跃,不如考虑采取渐进式的方法。首先,确定可能的职位,并进行几场信息面试。如果这些对话告诉你,在你准备好申请之前,你还有更多的学习要做,那很好!至少你有一个清晰的路径。任何旅程最重要的部分就是迈出第一步,而这第一步可以是很小的一步。

CHAPTER 10 第十章

Keys to Building a Career in Al
"人工智能职业发展的关键"

JOBS 职位
The path to career success in Al is more complex than what I can cover in one short eBook. Hopefully the previous chapters will give you momentum to move forward.
职业成功之路在 AI 领域比我在一本简短电子书中所能涵盖的要复杂得多。希望前几章能给你前进的动力。
Here are additional things to think about as you plot your path to success:
以下是一些在规划成功之路时需要考虑的额外事项:

1. Teamwork: 1. 团队合作:

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.)
当我们面对大型项目时,团队合作比个人工作更能取得成功。与他人协作、影响他人以及受到他人影响的能力至关重要。因此,人际交往和沟通技巧真的非常重要。(顺便说一句,我以前是个很糟糕的沟通者。)

2. Networking: . 2. 网络通信:

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 Al; 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
我讨厌社交!作为一个内向的人,不得不去参加派对,面带微笑,尽可能多地握手,这种活动对我来说几乎是一种折磨。我更愿意待在家里看书。然而,我很幸运在阿勒找到了许多真诚的朋友;这些人我愿意为他们出力,也依赖他们。没有人是一座孤岛,拥有强大的职业网络可以帮助你前进。


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.
在需要帮助或建议的时刻,我更倾向于向前迈进。与建立人脉相比,我发现构建社区更有帮助。因此,我不再试图建立个人人脉,而是专注于构建我所在的社区。这意外地帮助我结识了更多的人,也交到了朋友。
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.
在构建职业生涯的所有步骤中,这一步往往受到最多的关注。不幸的是,关于这一点在互联网上有很多糟糕的建议。(例如,许多文章敦促对潜在雇主采取对抗态度,我认为这并不有帮助。)尽管找到工作可能看起来是最终目标,但它只是职业生涯漫长旅程中的一小步。

4. Personal discipline 4. 个人自律

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.
很少有人会知道你是在周末学习还是在狂追电视剧——但他们会随着时间的推移注意到差异。许多成功人士在饮食、锻炼、睡眠、人际关系、工作、学习和自我照顾等方面养成了良好的习惯。这些习惯帮助他们不断前进,同时保持健康。

5. Altruism 5. 利他主义

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?
我发现那些在自身旅程的每一步都致力于帮助他人的人,往往能为自己带来更好的结果。我们如何在为自己打造一个令人激动的职业生涯的同时,帮助他人呢?

CHAPTER 11 第 11 章

Overcoming Imposter Syndrome
克服“冒名顶替综合症”
Before we dive into the final chapter of this book, l’d like to address the serious matter of newcomers to Al sometimes experiencing imposter syndrome, where someone - regardless of their success in the field - wonders if they’re a fraud and really belong in the Al community. I want to make sure this doesn’t discourage you or anyone else from growing in Al.
在深入本书最后一章之前,我想谈谈一些新加入人工智能领域的人可能会遇到的严重问题——冒充者综合征。无论他们在该领域取得了怎样的成功,总有人会怀疑自己是否是骗子,是否真的属于人工智能社区。我希望确保这一点不会让你或其他人从人工智能领域成长中感到气馁。

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,那么我热情地欢迎你!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.
大约 70%的人在其一生中都会经历某种形式的“冒名顶替综合症”。许多有才华的人公开谈论过这种经历,包括前 Facebook 首席运营官雪莉·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 公司联合首席执行官迈克·卡农-布鲁克斯。在我们社区中,即使是成就卓著的人也会遇到这种情况。如果你自己从未经历过,那真是太好了!我希望你能和我一起鼓励并欢迎所有想要加入我们社区的人。
Al 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!
AI 技术复杂,里面也有不少聪明能干的人。但人们很容易忘记,要想在任何事情上变得出色,第一步就是先做不好。如果你在 AI 上已经做得不好了——恭喜你,你已经在通往成功的路上了!
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).
我曾经在理解线性回归的数学原理上挣扎过。当逻辑回归在我的数据上表现异常时,我感到困惑,并且花费了数天时间才在我的基本神经网络实现中找到错误。如今,我仍然觉得许多研究论文难以阅读,最近在调整神经网络超参数时,我犯了一个明显的错误(幸运的是,一位同事发现了并修复了它)。
So if you, too, find parts of Al challenging, it’s okay. We’ve all been there. I guarantee that everyone who has published a seminal Al paper struggled with similar technical challenges at some point.
所以,如果你也觉得阿尔的部分内容有难度,那很正常。我们都有过这样的经历。我保证,每一位发表过阿尔重要论文的人,在某个时刻都曾遇到过类似的技術挑战。

Here are some things that can help.
以下是一些可以帮助的事情。

\checkmark 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 Al community and hope to make finding and giving support easier for everyone.
您是否有支持性的导师或同行?如果您还没有,请参加 Pie & AI 或其他活动,使用讨论板,努力寻找一些。如果您的导师或经理不支持您的成长,请寻找那些支持您的人。我还在努力打造一个支持性的 AI 社区,希望让每个人都能更容易地找到和给予支持。

\checkmark 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.
没有人是万事通。认识到自己的长处。如果你擅长的是向朋友解释《Batch》中的一十分之一的文章,那么你已经走在正确的路上了!让我们努力让你理解两十分之一的文章。
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 Al 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(她连 12 都数不过来)经常试图教我的一个岁儿子东西。无论你走到哪一步——只要你的知识至少和一个三岁孩子一样多——你都可以鼓励和提升那些在你后面的人。这样做也会对你有所帮助,因为那些在你后面的人会认识到你的专业知识,并也会鼓励你继续发展。当你邀请别人加入 Al 社区时——我希望你会这样做——这也会减少你已经是其中一员的任何疑虑。
Al 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 如此重要的一部分,我希望能让每一个想要成为其中一员的人都能感受到家的温暖,成为我们社区的一员。让我们共同努力,让这一切成为现实。

Final Thoughts 最后思考

Make Every Day Count
让每一天都值得度过

Every year on my birthday, I get to thinking about the days behind and those that may lie ahead.
每年在我生日的时候,我都会回想起过去的时光,以及那些可能即将到来的日子。
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.
也许你擅长数学;我确信你能够通过快速计算回答以下问题。但让我问你一个问题,请你凭直觉回答,不要计算。

How many days is a typical human lifespan?
一个典型的人类寿命有多长?

20,000 days 两万天
1 million days 一百万天
100,000 days 10 万天
5 million days 五百万天
When I ask friends, many choose a number in the hundreds of thousands. (Many others can’t resist calculating the answer, to my annoyance!)
当我问朋友们时,很多人会选择一个数,这个数在十万以上。(还有很多人忍不住去计算答案,这让我很烦恼!)
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.
当年我还是个研究生的时候,我记得我把统计数据输入到寿命计算器中,来计算我的预期寿命。计算器显示我总共可以活 27,649 天。这个数字让我感到非常渺小。我用大号字体打印出来,把它贴在了办公室墙上,作为每天的提醒。
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?
那都是我们用来陪伴亲人、学习、为未来建设以及帮助他人的日子。今天你所做的事情,值得你生命的 1/30,000 吗?

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