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Security and privacy are topics that will always be discussed hand in hand with the emergence of intuitive, new and invasive technology that blur the lines of privacy.

小號 ecurity和隱私，將永遠在手直觀的，新的，微創技術是模糊的隱私線路出現討論主題手。

Security is a national level priority, and privacy is seen as an individual’s fundamental human right.

安全是國家一級的優先事項，而隱私被視為個人的基本人權。

Even though data scientists and machine learning engineers might not be the necessary advocates of the enforcement of security measures, we do owe it to ourselves to have a high-level awareness of the subject matter.

即使數據科學家和機器學習工程師可能不是執行安全措施的必要倡導者，但我們也應歸功于自己對主題的高度了解。

關于這一點，這里是本周的文章，涉及以下內容： (On that note, here are this week’s articles that touch on the following:)

• The wide net of data privacy and its misses

  數據隱私的廣泛網絡及其缺失

• An exhibition into the worthwhile talks, tools, and technologies that readers and security experts should be aware of included in this year’s BlackHat event

  今年的BlackHat活動中包括讀者和安全專家應注意的有關有價值的演講，工具和技術的展覽

• A detailed exploration of popular deep learning approaches to solving Object Detection

  解決對象檢測的流行深度學習方法的詳細探索

• A retrospective account of a machine learning engineer journey. From learner to an educator.

  機器學習工程師歷程的回顧性說明。 從學習者到教育者。

數據收集的道德規范Aren Carpenter (The Ethics Of Data Collection By Aren Carpenter)

Get an overview of the ever-changing face of Data privacy laws, and the prevalent loopholes that exist.

概述數據隱私法不斷變化的面貌以及存在的普遍漏洞。

Aren Carpenter casts a light on the importance of adaptability within data privacy regulation as he showcases notorious misses of the wide net governing bodies throw over the handling of personal and private data.

阿倫·卡彭特(Aren Carpenter)展示了數據隱私法規中適應性的重要性，因為他展示了臭名昭著的廣泛治理機構遺漏了處理個人和私人數據的情況。

Aren’s article delves into the evolution of data privacy laws from HIPAA (1996) Omnibus Final Rule (2013) to the more current GDPR.

Aren的文章深入研究了數據隱私法從HIPAA(1996)Omnibus Final Rule(2013)到最新的GDPR的演變。

Aren makes statements that allude to the ambiguity caused by the blurred lines and vague privacy laws that make it difficult for an organisation to navigate patient privacy boundaries.

Aren的陳述暗示了模糊的線條和模糊的隱私法所導致的歧義，這使組織難以導航患者隱私邊界。

Nonetheless, the first section of this article illustrates an effort by government and regulating bodies to update data privacy laws in synchronicity with the advancement of technology and information gathering.

盡管如此，本文的第一部分說明了政府和監管機構為與技術和信息收集的發展同步而更新數據隱私法的努力。

The second portion of this article exposes the obvious unadaptability of initially proposed data privacy laws that enforced limitation on data held by health organisations.

本文的第二部分揭露了最初提出的數據隱私法律的明顯不適用性，該法律強制限制了衛生組織持有的數據。

Aren discusses the failure of outdated data privacy laws, as these laws fail to consider social media networks and organisations that will emerge decades later to aggregate health data and information of users of these platforms.

Aren討論了過時的數據隱私法的失敗，因為這些法律沒有考慮社交媒體網絡和組織，這些社交媒體網絡和組織將在數十年后出現，以匯總健康數據和這些平臺用戶的信息。

Aren concludes his article by mentioning the inevitable incoming data privacy oversights that are yet to come, which would mainly stem from the ever-evolving nature of technology as opposed to the negligence of governing bodies.

Aren在其文章的結尾提到了不可避免的傳入數據隱私監管，這將主要來自技術的不斷發展，而不是管理機構的疏忽。

The article’s final message to readers is a shift or responsibility of data protection from governing bodies to individuals. Aren provides a set of guidelines that Data Scientists and individuals can employ to create an awareness of ethically sourced data criteria to consider.

本文對讀者的最后信息是數據保護從管理機構到個人的轉變或責任。 Aren提供了一組指南，數據科學家和個人可以使用這些指南來提高對要考慮的道德來源數據標準的認識。

出色的讀物： (An excellent read for:)

• Individuals interested in Data privacy

  對數據隱私感興趣的個人

• Data Scientists / Data Analysts

  數據科學家/數據分析師

最可怕的事情，我們在Black Hat 2020看到了PCMAG (The Scariest Things We Saw at Black Hat 2020 By PCMag)

An article form of an exhibition into the worthwhile talks, tools, and technologies that readers and security experts should be aware of included in this year’s BlackHat event.

此次展覽的一種文章形式，涉及讀者和安全專家應注意的有價值的演講，工具和技術，該展覽形式已包含在今年的BlackHat活動中。

Security and privacy is always a topic that moves in accordance with the development of more advanced technologies and tools.

安全和隱私始終是隨著更先進的技術和工具的發展而變化的主題。

In 2020 we have observed the scrutiny of heavily used mobile applications based on the matter of national security and data privacy.

在2020年，我們觀察到了基于國家安全和數據隱私問題對大量使用的移動應用程序的審查。

The annual black hat security conference was held widely online due to Covid-19 restrictions and safety measures. PCMag has written an article providing brief details on talks, technologies and tools that stood out during the event.

由于Covid-19的限制和安全措施，年度黑帽安全會議在網上廣泛舉行。 PCMag撰寫了一篇文章，提供了有關此次活動中脫穎而出的演講，技術和工具的簡短詳細信息。

Be prepared to read about a compilation of tools that reverse the role between those that are tracked and the trackers; or tools that explore the security flaws of satellite-enabled wifi.

準備閱讀有關可逆轉被跟蹤工具和跟蹤器之間的角色的工具匯編； 或探索啟用衛星的wifi的安全漏洞的工具。

PCMag compilation article also includes a series of talks by security experts that convey their worries and experiences of security at a national level. Some of these talks include an exploration of security-related topics during the election periods within the United States of America.

PCMag匯編文章還包括安全專家的一系列演講，在國家層面上傳達了他們的擔憂和安全經驗。 其中一些談話包括在美利堅合眾國選舉期間對與安全有關的主題的探討。

What stood out to me from PCMag’s compiled list is the unveiling of the spy-like devices that take the shape of an everyday object such as lamps.

我從PCMag的匯編清單中脫穎而出的是揭開了間諜類設備的面紗，這些設備具有日常用品的形狀，例如燈。

出色的讀物： (An excellent read for:)

• Security enthusiasts

  安全愛好者

• Security experts

  安全專家

您應該閱讀以理解深度學習時代中的對象檢測的12篇論文Ethan Yanjia Li (12 Papers You Should Read to Understand Object Detection in the Deep Learning Era By Ethan Yanjia Li)

A detailed exploration of popular deep learning approaches to solving Object Detection

解決對象檢測的流行深度學習方法的詳細探索

Object Detection is one of the prominent computer vision-based tasks where researchers and academics have devised algorithms and heuristics-based approaches.

對象檢測是基于計算機視覺的重要任務之一，研究人員和學者已設計出算法和基于啟發式的方法。

In more recent times, the majority of the solutions used to solve object detection are rooted in the utilisation of Deep learning techniques and approaches.

在最近的時間里，用于解決對象檢測的大多數解決方案都植根于深度學習技術和方法的利用。

Ethan Yanjia Li has composed an article that provides an exploration of the development of deep learning approaches that solve object detection over the decade.

Ethan Yanjia Li撰寫了一篇文章，探討了十年來解決對象檢測的深度學習方法的發展。

Ethan’s article starts with a brief explanation of the object detection problems; there’s also an inclusion of prerequisite knowledge required to follow the content of the article correctly.

Ethan的文章首先簡要介紹了對象檢測問題。 還包括必要的知識，才能正確地遵循本文的內容。

Each included deep learning approach is equipped with the following: the year it was introduced; a link to the corresponding research paper of the technique, and more importantly a rather detailed explanation of how the method works and implemented.

每個包含的深度學習方法都配備了以下內容：引入的年份； 與該技術的相應研究論文的鏈接，更重要的是對該方法的工作方式和實施方式進行了較為詳細的說明。

I’m very impressed at the level Ethan is able to compress vital information concerning the presented technique without creating a lengthy technical analysis that would bore most casual readers.

我對Ethan能夠壓縮有關提出的技術的重要信息而無需創建冗長的技術分析(使大多數休閑讀者感到厭煩)的水平給我留下了深刻的印象。

Each introduced method is supplemented with images that illustrate a technique’s methodology of object detection or algorithmic process.

每種引入的方法都附加有圖像，這些圖像說明了對象檢測或算法過程的技術方法。

Examples of Object detection techniques included in this article are Yolo, RCNN, Overfeat and RetinaNet.

本文中包括的對象檢測技術示例包括Yolo，RCNN，Overfeat和RetinaNet。

To conclude this well-written article, Ethan includes some notable mentions of deep learning approached that have supplemented the previously mentioned techniques or provided further insight into the problem of object detection and proposed solutions.

總結這篇寫得不錯的文章，Ethan包括一些深度學習方法的引人注目的內容，這些內容對前面提到的技術進行了補充或對對象檢測和建議的解決方案問題提供了進一步的了解。

出色的讀物： (An excellent read for:)

• Data Scientists

  數據科學家

• Deep Learning Practitioners

  深度學習從業者

我將如何再次學習機器學習(3年以上)作者： Daniel Bourke (How I’d start learning machine learning again (3-years in) By Daniel Bourke)

A retrospective account of a machine learning engineer journey, from learner to educator.

對從學習者到教育者的機器學習工程師歷程的回顧性描述。

Daniel Bourke is fast becoming a familiar name within the online machine learning community. Over the years, Daniel has released articles, videos, courses and materials that provide machine learning students and practitioners with an insight into different aspects of the machine learning industry.

Daniel BourkeSwift成為在線機器學習社區中一個熟悉的名字。 多年來，Daniel發布了文章，視頻，課程和材料，為機器學習的學生和從業人員提供了有關機器學習行業不同方面的見解。

Three years into his rather eventful machine learning journey, Daniel has written an article that retrospectively analyses the key events of his journey and experience.

丹尼爾(Daniel)經歷了相當多的機器學習旅程的三年，寫了一篇文章，回顧性地分析了他的旅程和經驗的關鍵事件。

The first half of this article is told in a storytelling manner that Medium regular readers will appreciate. Daniel mentions the very beginning of his journey, from his humble learnings to his professional experience.

通過講故事的方式告訴本文的上半部分，中級普通讀者將不勝感激。 丹尼爾(Daniel)提到了旅程的開始，從謙虛的學習到專業的經歷。

Daniel discusses the feeling of wanting to move onto the next shiny framework, build more tools and also conduct research. He also includes a discourse on the matter of accumulating machine learning-related certificate.

Daniel討論了想要移入下一個閃亮的框架，構建更多工具以及進行研究的感覺。 他還討論了有關累積與機器學習相關的證書的問題。

The second half of the article is a blueprint of how Daniel would approach his whole experience of learning and obtaining knowledge within machine learning related topics.

本文的下半部分是一個藍圖，其中描述了Daniel如何利用他在機器學習相關主題中學習和獲得知識的全部經驗。

This blueprint is a useful resource for beginners who can be overwhelmed by the number of resources available on the internet. Riddled with resource link, the second half focuses on guiding readers with time measured indicator as to how to approach machine learning studies, which he mentions, ought to be supplemented with personal projects.

該藍圖對于初學者來說是有用的資源，但對于Internet上可用的資源數量卻不知所措。 下半部分充斥著資源鏈接，下半部分著重于通過時間衡量指標指導讀者如何進行機器學習研究，他提到，應該輔之以個人項目。

出色的讀物： (An excellent read for:)

• Data Scientist

  數據科學家

• Machine Learning Engineers

  機器學習工程師

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:

要與我聯系或查找更多類似于本文的內容，請執行以下操作：

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上聯系并聯系我

翻譯自: https://towardsdatascience.com/interesting-ai-ml-articles-you-should-read-this-week-aug-15-a050217b1c42

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