On March 12, this year, the TensorFlow team introduced the TensorFlow Developer Certificate Exam.
今年3月12日，TensorFlow团队推出了TensorFlow开发人员证书考试。
Cut to June 13, and I am TensorFlow Developer Certified. ✅
截止到6月13日，我已经获得TensorFlow开发人员认证。 ✅
So what happened in this 3-month long gap?
那么在这三个月的长差距中发生了什么？
After honoring all my business and personal commitments, I managed to take off one month to prepare for the exam. After studying all the details of the exam, I created a learning plan to get myself exam-ready in 14 days*.
兑现我所有的商业和个人承诺后，我设法休假了一个月来准备考试。 在学习了所有考试细节之后，我制定了一个学习计划，以使自己在14天内准备好考试*。
太酷了–但是TensorFlow是什么？ (That’s all cool – but what is TensorFlow?)
The gist: TensorFlow is an end-to-end open-source machine learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets ML/AI Engineers, Scientists/Analysts build and deploy ML-powered applications.
要点： TensorFlow是一个端到端的开源机器学习平台。 它具有一个由库，工具和社区资源组成的综合生态系统，可让ML / AI工程师，科学家/分析师构建和部署ML支持的应用程序。
Google, Airbnb, DeepMind, intel, Twitter, and many others are currently powered by TensorFlow and it helps them solve a wide gamut of problems.
Google，Airbnb，DeepMind，intel，Twitter等许多其他公司目前都由TensorFlow提供支持，它可以帮助他们解决各种各样的问题。
Now, I am not a certification evangelist. But since I was already using and following TensorFlow so closely as a Data Science Enthusiast it got my attention.
现在，我不是认证传播者。 但是由于我已经像数据科学爱好者一样紧密地使用和关注TensorFlow，因此引起了我的注意。
It has been an amazing learning streak and I am here to share all the nitty-gritty details of what the program is, how I did it, and how you can do it too!
它一直是一个惊人的学习连胜，我在这里分享所有的程序是什么细枝末节， 我是怎么做的， 你可以怎么做呢！
这个证书计划是关于什么的？ (What is this certificate program about?)
The certificate is an official validation confirming your proficiency with TensorFlow with respect to solving deep learning and ML problems in the AI-driven job market.
该证书是官方验证，确认您在解决AI驱动的就业市场中的深度学习和ML问题方面具有TensorFlow的专业技能。
If you’re someone who has got the skills to develop those Deep Neural Networks and solve problems with it, you can take the exam to differentiate yourself with the certificate.
如果您是具备开发这些深度神经网络并解决问题的技能的人，则可以参加考试以通过证书使自己与众不同。
Oh, snap! Not another Certification Program…????
哦，快点！ 没有其他认证计划…????
你为什么要参加考试？ (Why should you take the exam?)
Firstly, this is not like the certification where you watch a few 2–3 minute-long video lectures and take a quiz of multiple-choice questions and get yourself certified. This will require you to code and solve a class of problems that you'll need to prepare for.
首先，这与认证不同，您观看了2至3分钟的视频讲座，并进行了多项选择题测验，并获得了认证。 这将需要您编写代码并解决需要准备的一类问题。
Secondly, how many times have you thought of mastering a new library or technique, and then abandoned your plans midway? If you're anything like me, 99% of the time.
其次，您考虑过多少次掌握新图书馆或新技术，然后中途放弃计划了？ 如果您像我一样，则有99％的时间。
For me, the certification worked as the destination for my learning journey. I had some experience using TensorFlow but this came in as a challenge to work on problems that I hadn't actually solved myself.
对我而言，认证是我学习旅程的目的地。 我在使用TensorFlow方面有一些经验，但这是解决我尚未真正解决的问题的挑战。
Thirdly, you should keep monitoring the technology space in your field at least. So here is a trend from StackOverflow that shows how TensorFlow is being used by a huge number of users accounting for nearly 1 out of every 100 questions on the platform:
第三，您至少应继续监视您所在领域的技术空间。 因此，这是StackOverflow的趋势，显示了TensorFlow如何被大量用户使用，占平台上每100个问题中的近1个：
Lastly, I feel that Google always provides value to its users/developers. I believe the way they have structured the exam makes it worth trying, as it validates your skillsets and adds weight to your profile.
最后，我觉得Google始终为用户/开发人员提供价值。 我认为他们构成考试的方式值得一试，因为它可以验证您的技能并增加个人资料的分量。
OKAY! I’m sold, can you tell me what am I supposed to do in this exam?
好的！ 我卖了，你能告诉我这次考试我应该做什么吗？
考试演练 (Exam Walkthrough)
The exam is an online performance-based test where you are provided with questions to solve by building TensorFlow models within a dedicated PyCharm environment.
该考试是一项基于在线性能的考试，您可以通过在专用PyCharm环境中构建TensorFlow模型来解决问题。
You can take this exam from your computer that supports the PyCharm IDE requirements. You'll need a reliable internet connection, and you can take the exam at whatever time suits you (I started mine at midnight).
您可以从支持PyCharm IDE要求的计算机上参加此考试。 您将需要一个可靠的互联网连接，并且您可以在任何适合自己的时间参加考试(我从午夜开始参加考试)。
The exam tests your ability to solve problems like Image classification from real-world images, Natural Language Processing, and time series forecasting using Tensorflow 2.x.
该考试测试您解决问题的能力，例如从实际图像中进行图像分类，自然语言处理以及使用Tensorflow 2.x进行时间序列预测。
You can take up to 5 hours for the exam. If you exceed the time limit, the exam will be auto-submitted and you will only be graded for the questions for which you have submitted and tested your model.
您最多可能需要5个小时来参加考试。 如果您超过了时间限制，则考试将自动提交，并且只会对您提交并测试过的模型的问题进行评分。
You are allowed to use whatever learning resources you would normally use during your ML development work.
您可以使用在ML开发工作中通常使用的任何学习资源。
Exam Cost: Each attempt costs you $100 USD.
考试费用：每次尝试都将花费您$ 100美元。
Ah-hah! so how did you prepare for this scary long exam?
啊哈哈！ 那么您如何为这次可怕的长期考试做准备？
我是如何开始准备考试的 (How I started preparing for the Exam)
The first thing I did was spend a good amount of time studying the exam itself. The TensorFlow team provides you with this comprehensive handbook that tells you every detail about the exam and what skills you should master before taking it:
我要做的第一件事是花大量时间研究考试本身。 TensorFlow团队为您提供了这本综合手册 它告诉您有关考试的所有细节以及参加考试之前应掌握的技能：
After studying the exam, I designed a curriculum for myself to cover every skillset that is mentioned in this handbook.
学习完考试后，我为自己设计了一门课程 ，以涵盖本手册中提到的所有技能。
Next, I set myself up with a schedule so that my work engagements didn't push me off track and I prioritized learning for those ~20 days.
接下来，我为自己设定了时间表，这样我的工作投入不会使我偏离正轨，因此我优先考虑这20天的学习时间。
And that’s all – I started preparing for the exam using this curriculum comprised of these recommended and useful resources:
仅此而已–我开始使用包含以下推荐和有用资源的课程来准备考试：
[提示]：学习课程-复习我通过考试的所有资源 ([Imp]: Learning Curriculum — Review of all the resources I used to pass the exam)
For someone new to Tensorflow or Machine learning, the handbook might portray a terrifying picture. But having a plan and setting up a schedule will get you through. Here’s the curriculum that will prepare you well for the exam.
对于Tensorflow或Machine Learning的新手来说，该手册可能会描绘出可怕的图画。 但是，制定计划并制定时间表可以使您顺利完成工作。 这是为您准备考试做好准备的课程。
The Tensorflow team again did an amazing job of suggesting the resources based on your familiarity with Machine Learning. On top of that, I had been following a few books and playlists that helped me a great deal to cement the fundamentals in my brain and helped me go beyond the exam requirements themselves.
Tensorflow团队根据您对机器学习的熟悉程度再次提出了令人惊讶的建议资源。 最重要的是，我一直在关注一些书籍和播放列表，这些书籍和播放列表极大地帮助我巩固了大脑的基础知识，并帮助我超越了考试要求。
I have also reviewed all these resources that I used with a scoring scale of 5, based off the following qualities:
我还基于以下质量，对评分等级为5的所有资源进行了审查：
- Usefulness — to pass the exam 有用性-通过考试
- Learning Value — might not have a direct effect on the exam results but will help you build a strong foundation and work on more complex problems. 学习价值-可能不会直接影响考试成绩，但会帮助您建立坚实的基础并解决更复杂的问题。
Here’s the list of resources along with the time and cost that each will incur:
以下是资源列表以及每种资源将要花费的时间和成本：
1. Coursera的TensorFlow实践专业化 (1. Coursera’s TensorFlow in Practice Specialization)
Usefulness: 5/5 — This is absolutely needed to score well (or even pass) on the exam. It will help you cover every skill mentioned on the skills checklist in the Handbook. This is the recommended course on the Certification home page.
有用：5/5-这是绝对必要的，以便在考试中取得良好的分数(甚至通过)。 它将帮助您涵盖手册技能清单中提到的所有技能。 这是“ 认证”主页上的推荐课程。
If you carefully study the skills checklist and then compare it with the course outline, you’ll be able to figure out the direct mapping of each skill. It looks like either the course was created with the certification exam in mind or vice versa.
如果您仔细研究技能清单，然后将其与课程大纲进行比较，则可以找出每种技能的直接对应关系。 看起来这门课程是在考虑认证考试的基础上创建的，反之亦然。
The entire specialization contains 4 courses:
整个专业包含4门课程：
- Introduction to Machine Learning and Deep Learning. 机器学习和深度学习简介。
- Convolutional Neural Networks in TensorFlow TensorFlow中的卷积神经网络
- Natural Language Processing in TensorFlow TensorFlow中的自然语言处理
- Sequence, Time series, and Prediction 序列，时间序列和预测
Learning Value: 4/5 — The course itself depends on other resources to help you get an in-depth understanding of the fundamental concepts and topics that it uses. This is more of a Hands-On course.
学习价值：4/ 5-课程本身取决于其他资源，以帮助您深入了解其使用的基本概念和主题。 这更多是动手课程。
Time: It should take you 4–8 weeks depending on the amount of time you dedicate. I had prior experience with Image classification problems, and it took me 14 days to watch the entire specialization series and practice all the exercises they provide.
时间：您将花费4到8周的时间，具体取决于您花费的时间。 我以前在图像分类问题上有过经验，花了14天的时间观看整个专业系列并练习它们提供的所有练习。
Cost: This comes at a cost of $59 per month after a 7-day free trial. Totally worth it if you have to pay. The other resources provide a free alternative.
费用：免费试用7天，每月费用为59美元。 如果您必须付款，那完全值得。 其他资源提供了免费的替代方案。
2. 劳伦斯·莫罗尼(Laurence Moroney)在机器学习基金会(Machine Learning Foundation)上发布的YouTube播放列表 (2. YouTube Playlists on Machine Learning Foundation by Laurence Moroney)
Usefulness: 4/5 — This is an alternative to the starting 2 courses in the TensorFlow specialization on the Google Developers YouTube channel.
有用：4/5-这是Google Developers YouTube频道上TensorFlow专业化课程的前2门课程的替代方法。
There is a dedicated NLP zero to hero playlist by the same author — Laurence Moroney.
同一位作者Laurence Moroney 对英雄播放列表有一个专用的NLP零 。
Learning Value: 3/5 —Same as above but relies on other videos and resources in case you’re a beginner in Machine Learning.
学习价值：3/ 5-与上述相同，但如果您是机器学习的初学者，则依赖其他视频和资源。
Time: 1-2 weeks per playlist if you’re dedicating like 3–4 hours daily to your preparation.
时间：如果您每天准备3到4个小时，则每个播放列表需要1-2周。
Cost: Free
费用：免费
3. 使用Scikit-Learn，Keras和TensorFlow进行动手机器学习，第二版 (3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition)
Usefulness: 3/5 — The score is because of its relevance to the exam. For beginners, this will be a foundational resource to understanding Machine Learning and then gradually diving into the depths of Deep Learning, TensorFlow, Computer Vision, CNNs, RNNs, and much more.
有用性：3/5-分数是由于它与考试的相关性 。 对于初学者来说，这将是理解机器学习，然后逐步深入研究深度学习，TensorFlow，计算机视觉，CNN，RNN等的基础资源。
Following are the most useful chapters from the book:
以下是 本书中最有用的章节：
- Chapter 10 — Introduction to Artificial Neural Networks with Keras 第10章-Keras人工神经网络介绍
- Chapter 11 — Training Deep Neural Networks 第11章-训练深度神经网络
- Chapter 12 — Custom Models and Training with TensorFlow 第十二章— TensorFlow的自定义模型和培训
- Chapter 13 — Loading and Preprocessing Data with TensorFlow 第13章-使用TensorFlow加载和预处理数据
- Chapter 14 — Deep Computer Vision Using Convolutional Neural Networks 第十四章使用卷积神经网络的深度计算机视觉
- Chapter 15 — Processing Sequences Using RNNs and CNNs 第15章-使用RNN和CNN处理序列
- Chapter 16 — Natural Language Processing with RNNs and Attention 第十六章使用RNN和注意力进行自然语言处理
I have been reading this book since before the exam and the author Aurelion has created a gem of a book for aspiring Data Scientists, ML/AI engineers.
自从考试前我就一直在读这本书，作者Aurelion为有抱负的数据科学家，ML / AI工程师创造了本书的瑰宝。
It elucidates the foundational concepts, explains the mathematics behind each algorithm, and then explains the hands-on code to solve problems along with the best practices, covering everything. A MUST-read for all Machine Learning aspirants.
它阐明了基本概念，解释了每种算法背后的数学原理，然后解释了解决问题的动手代码以及涵盖所有内容的最佳实践。 所有机器学习爱好者的必读。
Learning Value: 5/5 — This is by far the best book to get started with Machine Learning.
学习价值：5/5 —这是迄今为止机器学习入门的最佳书。
Time: 3–4 Months — I would recommend that you read each chapter slowly and then practice the exercise given at the end of each chapter.
时间：3-4个月-我建议您慢慢阅读每一章，然后练习每章结尾给出的练习。
Cost: If you can afford it, I’d recommend getting an O’Reilly Media subscription for $50 a month where you not only get this book but all the publications and video/live lectures. Alternatively, you can buy the paperback on Amazon for the price it is available in your region (around $60).
费用：如果您负担得起，我建议您以每月50美元的价格订阅O'Reilly Media ，在这里您不仅可以获取本书，还可以获取所有出版物和视频/现场讲座。 另外，您也可以在亚马逊上以所在地区可用的价格购买平装本(约合60美元)。
I am an O’Reilly Instructor, so I have the resources available in my portal.
我是O'Reilly的讲师 ，所以我的门户网站上有可用的资源。
4.其他有用的YouTube播放列表 (4. Other Useful YouTube Playlists)
These are a few playlists that I went over to get a good grip over each of the required concepts:
以下是一些播放列表，我通过这些列表可以很好地掌握每个必需的概念：
-
MIT 6.S191: Introduction to Deep Learning:
MIT 6.S191: Introduction to Deep Learning:Usefulness 3/5 — It will help you get familiar with Deep learning and developing neural networks using TensorFlow. You should cover the first 3 videos in the playlist — Intro to DL, Recurrent Neural Network and Convolutional Neural Networks.
MIT 6.S191：深度学习简介： 实用性3/ 5-它可以帮助您熟悉使用TensorFlow进行深度学习和开发神经网络。 您应该覆盖播放列表中的前3个视频-DL简介，递归神经网络和卷积神经网络。
MIT 6.S191: Introduction to Deep Learning:Usefulness 3/5 — It will help you get familiar with Deep learning and developing neural networks using TensorFlow. You should cover the first 3 videos in the playlist — Intro to DL, Recurrent Neural Network and Convolutional Neural Networks.Learning Value 4/5 — Gives you a good refresher on the basics and I used it as a good video to watch when I was just in the mood to watch and not actually do much hands-on.
MIT 6.S191：深度学习简介： 实用性3/ 5-它可以帮助您熟悉使用TensorFlow进行深度学习和开发神经网络。 您应该覆盖播放列表中的前3个视频-DL简介，递归神经网络和卷积神经网络。 学习价值4 /5-使您在基础知识方面得到很好的复习，当我只是想观看而不实际动手时，我将其用作观看的好视频。
MIT 6.S191: Introduction to Deep Learning:Usefulness 3/5 — It will help you get familiar with Deep learning and developing neural networks using TensorFlow. You should cover the first 3 videos in the playlist — Intro to DL, Recurrent Neural Network and Convolutional Neural Networks.Learning Value 4/5 — Gives you a good refresher on the basics and I used it as a good video to watch when I was just in the mood to watch and not actually do much hands-on.Cost: Free
MIT 6.S191：深度学习简介： 实用性3/ 5-它可以帮助您熟悉使用TensorFlow进行深度学习和开发神经网络。 您应该覆盖播放列表中的前3个视频-DL简介，递归神经网络和卷积神经网络。 学习价值4 /5-使您在基础知识方面得到很好的复习，当我只是想观看而不实际动手时，我将其用作观看的好视频。 费用：免费
MIT 6.S191: Introduction to Deep Learning:Usefulness 3/5 — It will help you get familiar with Deep learning and developing neural networks using TensorFlow. You should cover the first 3 videos in the playlist — Intro to DL, Recurrent Neural Network and Convolutional Neural Networks.Learning Value 4/5 — Gives you a good refresher on the basics and I used it as a good video to watch when I was just in the mood to watch and not actually do much hands-on.Cost: FreeTime: 3 hours
MIT 6.S191：深度学习简介： 实用性3/ 5-它可以帮助您熟悉使用TensorFlow进行深度学习和开发神经网络。 您应该覆盖播放列表中的前3个视频-DL简介，递归神经网络和卷积神经网络。 学习价值4 /5-使您在基础知识方面得到很好的复习，当我只是想观看而不实际动手时，我将其用作观看的好视频。 费用：空闲时间： 3小时
-
Convolutional Neural Networks by Andrew NG
Just like the above playlist but with Andrew NG’s method of explaining Deep learning. I watched this series last year, very helpful.
就像上面的播放列表一样，但是使用了Andrew NG解释深度学习的方法。 我去年看了这个系列，非常有帮助。
I watched the videos that Laurence recommended in his course.
我看了劳伦斯在他的课程中推荐的视频。
Usefulness: 3/5 — More on the basics.
有用：3/5 —更多基础知识。
Usefulness: 3/5 — More on the basics.Learning Value: 4/5
有用：3/5 —更多基础知识。 学习价值：4/5
Usefulness: 3/5 — More on the basics.Learning Value: 4/5Time: 8–10 hours to understand the concepts in each video.
有用：3/5 —更多基础知识。 学习价值：4/5 时间： 8-10小时以了解每个视频中的概念。
-
Sequence Models by Andrew NG
NG的 序列模型
Sequence Models by Andrew NGUsefulness: 3/5 — More on the basics.
序列模型 ，作者：Andrew NG 有用：3/5 —更多基础知识。
Sequence Models by Andrew NGUsefulness: 3/5 — More on the basics.Learning Value: 4/5
序列模型 ，作者：Andrew NG 有用：3/5 —更多基础知识。 学习价值：4/5
Sequence Models by Andrew NGUsefulness: 3/5 — More on the basics.Learning Value: 4/5Time: 8–10 hours to understand concepts in each video.
序列模型 ，作者：Andrew NG 有用：3/5 —更多基础知识。 学习价值：4/5 时间： 8-10小时以了解每个视频中的概念。
5. PyCharm教程系列和环境设置指南 (5. PyCharm Tutorial Series and Environment Set up guidelines)
In case you have never worked in an IDE before, getting familiar with the exam environment is highly recommended.
如果您以前从未在IDE中工作过，强烈建议您熟悉考试环境。
Usefulness: 5/5 (required) — This is a getting started series for PyCharm beginners that’ll help you get up to speed with how to use PyCharm efficiently.
实用性：5/5 (必需)—这是PyCharm 初学者的入门系列，可帮助您快速掌握如何有效使用PyCharm。
Learning Value: NA
学习价值：不适用
Make sure you read the environment set up guidelines to take the TensorFlow Developer Certificate exam.
确保您已阅读环境设置指南以参加TensorFlow开发人员证书考试 。
Follow the instructions mentioned in the PDF because the certification team can’t be held responsible for your negligence.
请遵循PDF中提到的说明，因为认证团队不对您的疏忽负责。
Whoa! That is a long list of resources, how did you manage to study?
哇！ 那是一长串资源，您是如何学习的？
我的准备时间表 (My Schedule for Preparation)
By the end of April, I was sure to check this off my list. I’d take it up just like any other project and was determined to see it through.
到4月底，我确定将其从清单中剔除。 我会像处理其他任何项目一样处理它，并下定决心要通过。
So, I used to plan every night what I was about to do the next morning. The pink-colored time slots are blocked for studying for the course. These 3–4 hours in the morning were my most productive where I could grasp the most.
因此，我习惯于每晚计划第二天早上要做什么。 粉色时隙被阻止用于学习本课程。 早晨的这3-4个小时是我最能掌握的生产力。
I had a fairly consistent routine throughout the 2 weeks and I raised the intensity when I got close to exam day with more than 5–6 hours of practice each day.
在过去的两个星期中，我的日常活动相当一致，并且在接近考试日且每天练习5至6个小时以上时，我会提高强度。
Ok, so what was your process of studying?
好了，W¯¯ 帽子是你学习的过程？
我的学习方式 (How I studied)
I used to first watch the lessons of each week, then practice the code in the colab provided following the video lessons.
我曾经先看每周的课程，然后在视频课程之后的colab中练习代码。
At the end of each week, I would complete the assignment designed by Laurence in his course.NOTE: I used to write the entire code myself rather than just completing the placeholder code.
在每个星期的结尾，我将完成Laurence在他的课程中设计的作业。 注意：我过去常常自己编写整个代码，而不仅仅是完成占位符代码。
I would also revisit the chapters in the Hands-on ML book later at night before sleeping or at the end of my time slot just to make everything crystal clear. Then I would learn about the next steps that were beyond the exam curriculum.
我还将在晚上晚上入睡之前或在我的时段结束时重新阅读动手ML手册中的各章，以使所有内容都变得清晰。 然后，我将了解考试课程之外的后续步骤。
TL;DR: WATCH. CODE. PRACTICE. READ. REPEAT.
TL; DR： 观看。 码。 实践。 读。 重复。
所有人都准备参加考试-接下来是什么？ (All prepared to take the Exam — What’s next?)
If you think that you have covered all the skills mentioned in the Handbook and feel like you’re ready to take the exam, that's great.
如果您认为自己已经掌握了《手册》中提到的所有技能，并且觉得自己已准备好参加考试，那就太好了。
Now you're ready to purchase your exam. It's served by a third party platform called TrueAbility. You are required to submit your government issued ID (passport would work) for authentication.
现在，您可以购买考试了。 它由称为TrueAbility的第三方平台提供服务。 您需要提交政府颁发的ID(护照可以使用)以进行身份验证。
Pay $100 for the exam. You are now good to go, you can start the exam as and when you feel ready.
支付$ 100的考试费用。 现在您一切顺利，可以在准备就绪时开始考试。
They provide you detailed instructions on how to set up your PyCharm for the exam. Here’s what I recommend doing before starting your exam:
他们为您提供有关如何设置考试的PyCharm的详细说明。 建议您在开始考试之前执行以下操作：
- Make sure that you have a good reliable internet connection. 确保您具有良好的可靠互联网连接。
- Make sure that you have gone through the PyCharm beginner tutorial if you’re new to the IDE. 如果您不熟悉IDE，请确保已阅读PyCharm初学者教程。
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I tested my PyCharm by running a few TensorFlow tutorials. They worked fine and I was ready to install the exam plugin to get started.
我通过运行一些TensorFlow 教程测试了PyCharm。 他们工作正常，我已经准备好安装考试插件以开始使用。
- I read the exam instructions thoroughly before hitting the start exam button. It will be provided to you after signing up for the exam. 在点击开始考试按钮之前，我已仔细阅读了考试说明。 报名参加考试后将提供给您。
HIT the Start Exam button!
点击“开始考试”按钮！
考试期间 (During the Exam)
Your exam environment will be created and you’ll be directed to the questions you'll have to solve. I won’t be sharing the details of the exam as that’d be unethical.
系统将创建您的考试环境，并指导您解决必须解决的问题。 我不会分享考试的细节，因为那是不道德的。
In my experience, it all went smoothly, and I was fairly confident I'd complete the exam after looking at the questions. And sure enough I completed the exam within 3 hours.
以我的经验来看，一切都进行得很顺利，而且我非常有信心在看完问题后就完成了考试。 可以肯定的是，我在3个小时内完成了考试。
技巧和窍门 (Tips and Tricks)
- Make sure you practice a few exercises on PyCharm 1–2 days before the exam rather than just working on Colab notebooks. 确保在考试前1-2天在PyCharm上进行一些练习，而不是仅仅在Colab笔记本上进行练习。
- For the models that took time on my local machine, I trained them on Google Colab and then uploaded the trained model in the project folder. 对于在我的本地计算机上花费时间的模型，我在Google Colab上对其进行了训练，然后将经过训练的模型上传到项目文件夹中。
- Keep working on other questions while your model is training; I had 3 models under training — 1 on my machine and 2 on Google colab and I was working on the 4th while I was trying to tune the hyperparameters. 在训练模型时继续处理其他问题； 我接受了3个模型的训练-1个在我的机器上，2个在Google colab上，当我尝试调整超参数时，我正在研究第4个模型。
- If you have enough time, keep trying to get the best results for each model. 如果您有足够的时间，请继续尝试为每个模型获取最佳结果。
考试后仪式 (Post-Exam Rituals)
When you're finished, hit the Submit and End Exam button. When I was done, I received an email from TrueAbility congratulating me on passing the exam:
完成后，点击“提交并结束考试”按钮。 完成后，我收到了TrueAbility的电子邮件，祝贺我通过了考试：
There is no detailed analysis or report on how you did on the exam. They simply mention whether or not you’ve passed the exam.
没有关于考试方式的详细分析或报告。 他们只是提到您是否通过了考试。
After passing the exam, you are requested to join the TensorFlow Certificate Network that tells you the Certificate holders in different regions:
通过考试后，您需要加入TensorFlow证书网络，该网络告诉您不同地区的证书持有者：
证书在哪里？ (Where is the Certificate?)
It takes a week or so to actually get your hands onto the certificate. I got mine 3 days after the exam.
实际需要一周左右的时间才能获得证书。 考试后三天我就来了。
Once you received your certificate, you can flash that badge on your social media profiles and mark it as an achievement in your resume.
收到证书后，您可以在社交媒体个人资料上刷新该徽章，并在简历中将其标记为成就。
考试常见问题 (Exam FAQs)
参加考试真的很重要吗，我是否只能根据每个部分从事同等的项目？ (Is it really that important to take the exam, can’t I just work on an equivalent project based on each section?)
I’d say you can definitely do that and in fact, that is probably the better approach when you’re developing a new skill.
我会说您绝对可以做到，实际上，这可能是您开发新技能时的更好方法。
But the Exam helps you get recognized and, since it is coming from Google, it is nice to have. It's not a be-all-end-all solution to learning Deep learning or TensorFlow.
但是，该考试可以帮助您获得认可，并且由于它来自Google，因此非常高兴。 它不是学习深度学习或TensorFlow的万能解决方案。
我想从头开始，我应该看什么资源？ (I want to start from scratch, what resources should I be looking at?)
Learn by doing things. Many blogs talk about learning deep mathematics first but you’ll soon loose interest using that approach.
通过做事来学习。 许多博客都首先谈论学习深度数学，但是很快您就会失去使用这种方法的兴趣。
Start by learning programming (Python or any other language) and then gradually dive into Machine Learning. You can also look at this course by Andrew NG.
首先学习编程(Python或任何其他语言)，然后逐步深入研究机器学习。 您也可以阅读Andrew NG的这门课程。
我总是需要一位导师或某人来推动我做事情并解决我的疑惑和问题，您能提出解决方案吗？ (I always need a mentor or someone to push me to do things and solve my doubts and problems, can you propose a solution?)
A mentor does indeed help in many cases. If you’re someone who wants someone to help you with theses details apart from these resources, you can look at Codementor where you’ll find ML and AI experts who can help you resolve all your queries.
在很多情况下，导师确实有帮助。 如果您是除了这些资源之外还希望有人帮助您提供这些详细信息的人，您可以在Codementor上找到可以帮助您解决所有查询的ML和AI专家。
这对我来说有点贵，有没有免费或便宜的方法？ (This is a little expensive for me, is there a free or less expensive approach?)
Yes, the Tensorflow team is offering a few stipends to people who might have some trouble affording the exam. Visit this link for more details.
是的，Tensorflow团队正在为可能难以负担考试的人们提供一些津贴。 请访问此链接以获取更多详细信息 。
If your question is not addressed here, feel free to respond to this post and I’ll get back to you. :)
如果此处未解决您的问题，请随时回复此帖子，我们会尽快与您联系。 :)
下一步是什么？ (What’s next?)
Just like with any other skill, start building things and working on real-world projects. Start looking into open-source projects like TensorFlow. Apply for jobs with this badge and share your story with others.
就像使用其他任何技能一样，开始构建东西并从事实际项目。 开始研究TensorFlow等开源项目。 使用此徽章申请职位，并与他人分享您的故事。
I’m working on a complete Deep Learning Foundation series that’ll be useful for ML/DL aspirants. You can watch me teach on to my Youtube channel in the meanwhile.
我正在研究完整的深度学习基础系列，这将对ML / DL有抱负的人有用。 您可以同时观看我的YouTube频道教学。
Here is a video based on this blog where you can watch me share my journey:
这是基于此博客的视频，您可以在其中观看我分享我的旅程：
I’ll be rolling out a complete series on TensorFlow soon. Subscribe to my channel for interesting data science content.
我将很快在TensorFlow上推出完整系列。 订阅我的频道以获取有趣的数据科学内容。
Harshit的数据科学 (Data Science with Harshit)
With this channel, I am planning to roll out a couple of series covering the entire data science space. Here is why you should be subscribing to the channel:
我打算通过这个渠道推出一系列涵盖整个数据科学领域的系列 。 这就是为什么您要订阅该频道的原因 ：
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These series would cover all the required/demanded quality tutorials on each of the topics and subtopics like Python fundamentals for Data Science.
这些系列将涵盖关于每个主题和子主题(如数据科学的Python基础知识)的所有必需/要求的质量教程。
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Explained Mathematics and derivations of why we do what we do in ML and Deep Learning.
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Podcasts with Data Scientists and Engineers at Google, Microsoft, Amazon, etc, and CEOs of big data-driven companies.
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Projects and instructions to implement the topics learned so far.
实施到目前为止所学主题的项目和说明 。
翻译自: https://www.freecodecamp.org/news/how-i-passed-the-certified-tensorflow-developer-exam/