linkedin爬蟲

經(jīng)驗 (Experience)

介紹 (Introduction)

LinkedIn has grown in popularity over the years, and it has become the social network space for professionals.

多年來，LinkedIn越來越流行，它已成為專業(yè)人士的社交網(wǎng)絡(luò)空間。

I’ve seen many professionals utilize LinkedIn in various ways. Some use it as a personal brand-building tool, and others use it as a lead generation and marketing tool.

我已經(jīng)看到許多專業(yè)人士以各種方式利用LinkedIn。 有些人將其用作個人品牌建立工具，而另一些人將其用作潛在客戶生成和營銷工具。

One prominent use of LinkedIn is to reach individuals for career and academic advice. These individuals might be out of reach physically, and therefore LinkedIn is a communication channel that bridges the physical distance between individuals.

LinkedIn的一種顯著用途是吸引個人尋求職業(yè)和學(xué)術(shù)建議。 這些人的身體可能無法觸及，因此，LinkedIn是溝通個人之間物理距離的溝通渠道。

Using LinkedIn as an advice platform is what this article focuses on. Individuals have read my articles, and have reached out to me via LinkedIn to get more information on specific topics and advice, all machine learning related.

本文重點介紹使用LinkedIn作為建議平臺。 個人已經(jīng)閱讀了我的文章，并通過LinkedIn與我聯(lián)系，以獲取有關(guān)特定主題和建議(與所有機器學(xué)習(xí)相關(guān)的信息)的更多信息。

在本文中，我將包括機器學(xué)習(xí)從業(yè)人員和學(xué)生提出的一些常見問題，并且還將包括我提供的相應(yīng)答案。 (In this article, I will include some common questions that have been asked by machine learning practitioners and students, and I’ll also include the corresponding answers I’ve provided.)

I have removed any names and personal information from questions to protect the identity of the individuals who have reached out to me.

我已刪除問題中的所有姓名和個人信息，以保護與我聯(lián)系的個人的身份。

如何閱讀這篇文章 (How To Read This Article)

Questions asked by individuals will follow the format of this particular statement you are reading.

個人提出的問題將遵循您正在閱讀的特定說明的格式 。

“Statements from me will look like this”

“我的陳述看起來像這樣”

Answers will follow the format below:

答案將遵循以下格式：

回答： (Answer:)

A paragraph that includes a response to a question

包含對問題的回答的段落

問題1(在線學(xué)習(xí)) (Question 1 (Online Learning))

題： (Question:)

Hi Richmond, I am getting into Computer Vision and would appreciate if you had any course links/articles/ebooks that would help. Thanks!

里士滿，您好，我正在學(xué)習(xí)計算機視覺，如果您有任何幫助的課程鏈接/文章/電子書，將不勝感激。 謝謝！

This is a common question that’s asked by curious individuals who want to gain some knowledge on the basics of machine learning related topics. I tend to direct new learners to free resources that I utilized. I believe it’s best to get a feel of subject areas and topics before putting monetary investments in purchasing courses and undertaking certificate programs.

這是一個常見的問題，好奇的人希望獲得有關(guān)機器學(xué)習(xí)相關(guān)主題基礎(chǔ)知識的知識。 我傾向于引導(dǎo)新學(xué)習(xí)者釋放我所利用的資源。 我認為最好在將金錢投資用于購買課程和進行證書課程之前先了解主題領(lǐng)域和主題。

回答 (Answer)

To learn Computer Vision, you have to understand the basics of Machine Learning, Neural Networks and Image processing.

要學(xué)習(xí)計算機視覺，您必須了解機器學(xué)習(xí)，神經(jīng)網(wǎng)絡(luò)和圖像處理的基礎(chǔ)知識。

神經(jīng)網(wǎng)絡(luò) (Neural Networks)

Within machine learning, it is essential to understand topics such as linear algebra, calculus, and partial differentiation.

在機器學(xué)習(xí)中，必須理解諸如線性代數(shù)，微積分和偏微分等主題。

Within Neural Networks, it is vital to understand the fundamental concepts and ideas. It is also important to understand concepts such as backpropagation, vanishing gradients descent and different neural network architectures such as convolutional neural networks (CNN); deep neural networks(DNN;) and recurrent neural networks(RNN).

在神經(jīng)網(wǎng)絡(luò)中，了解基本概念和想法至關(guān)重要。 了解諸如反向傳播，消失梯度下降和不同的神經(jīng)網(wǎng)絡(luò)架構(gòu)(例如卷積神經(jīng)網(wǎng)絡(luò)(CNN))等概念也很重要； 深度神經(jīng)網(wǎng)絡(luò)(DNN;)和遞歸神經(jīng)網(wǎng)絡(luò)(RNN)。

3Blue1Brown Neural Network Video3Blue1Brown神經(jīng)網(wǎng)絡(luò)視頻

斯坦福大學(xué)計算機視覺講座 (Stanford Computer Vision Lectures)

After understanding the basics of ML and Neural networks, you can dive into some academic content from Stanford that explains some standard Computer vision techniques, theory and algorithms.

了解ML和神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)知識之后，您可以深入斯坦福大學(xué)的一些學(xué)術(shù)內(nèi)容，解釋一些標(biāo)準(zhǔn)的計算機視覺技術(shù)，理論和算法。

Standford Computer Vision Videos斯坦福計算機視覺視頻

深度蜥蜴機器學(xué)習(xí)和深度學(xué)習(xí) (Deep Lizard Machine Learning & Deep Learning)

If you have a solid grasp of the theory and maths behind neural networks and some machine learning algorithms, you can move on to some practical projects and network implementations with a standard machine learning libraries such as PyTorch.

如果您對神經(jīng)網(wǎng)絡(luò)和某些機器學(xué)習(xí)算法背后的理論和數(shù)學(xué)有扎實的了解，則可以使用諸如PyTorch之類的標(biāo)準(zhǔn)機器學(xué)習(xí)庫著手進行一些實際項目和網(wǎng)絡(luò)實現(xiàn)。

Deep Lizard Machine Learning & Deep Learning深度蜥蜴機器學(xué)習(xí)和深度學(xué)習(xí) Neural Network Programming With Deep Lizard用深蜥蜴進行神經(jīng)網(wǎng)絡(luò)編程

With all the knowledge you have accumulated, you should be able to start your personal project and have the necessary expertise to get an entry-level computer vision job position.

借助您所積累的所有知識，您應(yīng)該能夠開始自己的項目并擁有必要的專業(yè)知識才能獲得入門級的計算機視覺工作職位。

I will advise that you create your projects and participate in some Kaggle competition. Also, it would help if you tried to write on Medium to showcase your understanding of topics and concepts in computer vision and machine learning.

我建議您創(chuàng)建項目并參加一些Kaggle比賽。 另外，如果您嘗試在Medium上寫作，以展示您對計算機視覺和機器學(xué)習(xí)中的主題和概念的理解，也會有所幫助。

問題2(博士vs理學(xué)碩士) (Question 2 (PhD vs MSc))

題： (Question:)

I have recently started my first job as a grad in an AI role, specifically in Computer Vision. I have a bachelors degree in computer engineering but am considering starting the part-time Masters in AI at University, which I would do in my spare time outside work hours. Just wondering, have you ever considered a doing a PhD, or do you feel a master’s enough for working in the AI field?

我最近開始了我的第一份工作，擔(dān)任AI職位的畢業(yè)生，尤其是Computer Vision。 我擁有計算機工程學(xué)士學(xué)位，但正在考慮在大學(xué)開設(shè)兼職的AI碩士學(xué)位，我會在工作時間以外的業(yè)余時間做這些工作。 只是想知道，您是否曾經(jīng)考慮過要攻讀博士學(xué)位，或者您覺得足以勝任AI領(lǐng)域的工作？

回答： (Answer:)

To answer your question directly, I think a Masters is more than enough for working in the AI field.

要直接回答您的問題，我認為碩士足以勝任AI領(lǐng)域的工作。

The benefit of an advanced qualification is that it provides academic authority and also shows to potential employers that you have taken the time out to specialize within a particular field.

高級資格證書的好處在于，它不僅可以提供學(xué)術(shù)權(quán)威，還可以向潛在的雇主表明您已經(jīng)花了一些時間專門研究某個特定領(lǐng)域。

When conducting my job searches, I saw a few machine learning and AI roles that requested PhD degrees from an applicant, but most of these roles were either very specialized and within research departments of companies.

在進行求職時，我看到了一些需要申請人申請博士學(xué)位的機器學(xué)習(xí)和AI角色，但是其中大多數(shù)角色要么非常專業(yè)，要么在公司的研究部門內(nèi)。

A PhD shows employers that you can dedicate time to one specific area of a field and become somewhat of an expert and make contributions to the advancement of the field. This is not entirely necessary for the majority of Machine learning roles in the industry.

博士學(xué)位向雇主表明，您可以將時間專用于某個領(lǐng)域的某個特定領(lǐng)域，并成為專家，可以為該領(lǐng)域的發(fā)展做出貢獻。 對于行業(yè)中的大多數(shù)機器學(xué)習(xí)角色來說，這并不是完全必要的。

I would say you should go for the masters and give it your all and perhaps if you find an area within Machine learning that you are passionate about, then you can pursue a PhD.

我想說的是，您應(yīng)該去找碩士學(xué)位，并全力以赴，也許如果您在機器學(xué)習(xí)中找到了自己熱衷的領(lǐng)域，那么就可以攻讀博士學(xué)位。

Currently, I am not considering a PhD as I want to build a solid five-year career track within Machine Learning and Computer Vision.

目前，我不考慮博士學(xué)位，因為我想在機器學(xué)習(xí)和計算機視覺領(lǐng)域建立扎實的五年職業(yè)生涯。

Perhaps I will evaluate my decision in 7 years or so.

也許我會在7年左右的時間內(nèi)評估我的決定。

問題3(機器學(xué)習(xí)研究與工程) (Question 3 (Machine Learning Research vs Engineering))

題： (Question:)

I would wish to work in a position which is 50% researching on new, exciting stuff, reading, writing papers and 50% coding while also managing people, solving real-world problems(I understand that solving real-world problems is a part of research). I’m not sure if such a role exists. I feel I am at an important stage of my career where I need to take important life-shaping decisions. Considering your experience, it would be amazing if you could advise me on the above.

我希望工作的職位是50％的人研究新穎的東西，閱讀，撰寫論文和50％的編碼，同時還要管理人員，解決現(xiàn)實問題(我知道解決現(xiàn)實問題是其中的一部分研究)。 我不確定是否存在這樣的角色。 我覺得自己處于職業(yè)生涯的重要階段，需要做出重要的人生決定。 考慮到您的經(jīng)驗，如果您能在上述方面給我建議，那將是驚人的。

回答： (Answer:)

In regards to finding a role that has a 50/50 balance between engineering and research, I’ll be honest with you and will tell you bluntly that not a lot of job roles can provide that balance or even define the right balance in the job role description.

關(guān)于找到在工程和研究之間保持50/50平衡的角色，我將對您誠實，直言不諱地告訴您，沒有很多工作角色可以提供這種平衡，甚至無法在工作中定義正確的平衡角色描述。

As I mentioned in my article, there are a lot of engineers that conduct research and vice versa. But one crucial thing I didn’t mention is that the balance of ML researchers role and Engineering roles can be based on the current needs and requirement of the business.

正如我在文章中提到的，有很多工程師從事研究，反之亦然。 但是我沒有提到的關(guān)鍵一件事是ML研究人員角色和工程角色之間的平衡可以基于業(yè)務(wù)的當(dāng)前需求和要求。

I find the balance between engineering and researching changes during your time at a company and is based on the current goals and focus of the company.

我在公司任職期間發(fā)現(xiàn)工程變更和研究變更之間的平衡，并且該平衡基于公司當(dāng)前的目標(biāo)和重點。

Take me, for example, I applied for my current role, and in the job description and interview, it wasn’t stated that I would be conducting any in-depth research. But during the first two months, I did more research than engineering, but after the first two months till now, I do more engineering than research.

以我為例，我申請了目前的職位，在職位描述和面試中，并沒有說我會進行任何深入的研究。 但是在最初的兩個月中，我所做的研究比工程學(xué)要多，但是直到現(xiàn)在的頭兩個月，我所做的工程學(xué)比研究要多。

My advice is for you to carry on doing what you can to stand out from the crowd. Engineer tools that use state of the art machine learning approaches and also cultivate the skills of reading and writing research papers.

我的建議是讓您繼續(xù)做自己能從人群中脫穎而出的事情。 使用最先進的機器學(xué)習(xí)方法并培養(yǎng)閱讀和撰寫研究論文技能的工程師工具。

When you get to the stage where you are applying for roles, always ask for what the balance between engineering and research is.

當(dāng)您進入申請職位的階段時，請始終詢問工程和研究之間的平衡是什么。

You’ll find that it is tough to get a role with the perfect 50/50 balance, but there are some job opportunities where you get to define the type of balance you want, although they are rare, they are still out there.

您會發(fā)現(xiàn)很難擁有完美的50/50平衡的角色，但是在一些工作機會中，您可以定義所需的平衡類型，盡管它們很少見，但仍然存在。

問題4(機器學(xué)習(xí)的職業(yè)可能性) (Question 4 (Machine Learning Career Possibility))

題： (Question:)

I always think about whether it’d be possible for me to become a computer vision engineer. I am not a PhD and don’t have a masters degree, I’m just a graduate. What would you suggest?

我一直在思考是否有可能成為一名計算機視覺工程師。 我不是博士學(xué)位，也沒有碩士學(xué)位，我只是研究生。 你有什么建議？

回答 (Answer)

I would suggest looking at the requirement of job roles for Computer Vision Engineers where you are located.

我建議您查看您所在的計算機視覺工程師的職位要求。

Identify what skills, techniques, algorithms, programming languages and tools that you are expected to have an awareness of and ensure that you are on a path to acquiring them.

確定您期望了解哪些技能，技術(shù)，算法，編程語言和工具，并確保您正在逐步掌握它們。

Next, to make up for the lack of qualification, I would suggest looking into taking a masters degree if you can. But if you can’t pursue an advance degree in machine learning, then you can consider the following option:

接下來，為了彌補資格的不足，我建議您盡可能地考慮攻讀碩士學(xué)位。 但是，如果您不能攻讀機器學(xué)習(xí)的高級學(xué)位，那么可以考慮以下選擇：

Taking online computer vision-related courses with a provided certificate upon completion. Udacity is suitable for this:

完成時參加帶有提供的證書的在線計算機視覺相關(guān)課程。 Udacity適合于此：

2. Have a project portfolio of 3–5 impressive computer vision/machine learning projects.

2.擁有3-5個令人印象深刻的計算機視覺/機器學(xué)習(xí)項目的項目組合。

3. Take a look at completing Kaggle challenges.

3.看一下完成Kaggle挑戰(zhàn)的過程 。

結(jié)論 (Conclusion)

I hope that you have found some value from the content within this article.

我希望您從本文的內(nèi)容中發(fā)現(xiàn)了一些價值。

It’s very humbling that there are people that view my experience and expertise as a learning point. If you have any questions that you would like to ask me, or perhaps you would prefer if I elaborated on answers to some question in more detail, then you can reach me through LinkedIn as usual.

有人將我的經(jīng)驗和專業(yè)知識視為學(xué)習(xí)要點，這是非常令人感到羞恥的。 如果您有任何疑問想問我，或者您更希望我詳細說明某個問題的答案，那么您可以像往常一樣通過LinkedIn與我聯(lián)系。

I am not reluctant to answering machine learning related questions or queries as I know how hard and challenging the field can be, so please don’t be shy to ask any pressing questions. I’ll try my best to provide suitable answers.

我不愿意回答與機器學(xué)習(xí)相關(guān)的問題或疑問，因為我知道該領(lǐng)域可能有多么艱巨和挑戰(zhàn)，所以請不要害羞地提出任何緊迫的問題。 我會盡力提供適當(dāng)?shù)拇鸢浮?

我希望您覺得這篇文章有用。 (I hope you found the article useful.)

To connect with me or find more content similar to this article, do the following:

要與我聯(lián)系或查找更多類似于本文的內(nèi)容，請執(zhí)行以下操作：

Subscribe to my Email List for weekly newsletters

訂閱我的電子郵件列表以獲取每周新聞

Follow me on Medium

跟我來中

Connect and reach me on LinkedIn

在LinkedIn上聯(lián)系并聯(lián)系我

翻譯自: https://towardsdatascience.com/common-questions-machine-learning-students-and-practitioners-ask-on-linkedin-51bedcdea82c

linkedin爬蟲

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