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.

小号 ecurity和隐私，将永远在手直观的，新的，微创技术是模糊的隐私线路出现讨论主题手。

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

安全是国家一级的优先事项，而隐私被视为个人的基本人权。

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.

即使数据科学家和机器学习工程师可能不是执行安全措施的必要倡导者，但我们也应归功于自己对主题的高度了解。

关于这一点，这里是本周的文章，涉及以下内容： (On that note, here are this week’s articles that touch on the following:)

• The wide net of data privacy and its misses

  数据隐私的广泛网络及其缺失

• 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活动中包括读者和安全专家应注意的有关有价值的演讲，工具和技术的展览

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

  解决对象检测的流行深度学习方法的详细探索

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

  机器学习工程师历程的回顾性说明。 从学习者到教育者。

数据收集的道德规范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.

概述数据隐私法不断变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.

阿伦·卡彭特(Aren Carpenter)展示了数据隐私法规中适应性的重要性，因为他展示了臭名昭著的广泛治理机构遗漏了处理个人和私人数据的情况。

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

Aren的文章深入研究了数据隐私法从HIPAA(1996)Omnibus Final Rule(2013)到最新的GDPR的演变。

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的陈述暗示了模糊的线条和模糊的隐私法所导致的歧义，这使组织难以导航患者隐私边界。

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.

尽管如此，本文的第一部分说明了政府和监管机构为与技术和信息收集的发展同步而更新数据隐私法的努力。

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数据隐私法律的明显不适用性，该法律强制限制了卫生组织持有的数据。

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讨论了过时的数据隐私法的失败，因为这些法律没有考虑社交媒体网络和组织，这些社交媒体网络和组织将在数十年后出现，以汇总健康数据和这些平台用户的信息。

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在其文章的结尾提到了不可避免的传入数据隐私监管，这将主要来自技术的不断发展，而不是管理机构的疏忽。

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.

本文对读者的最后信息是数据保护从管理机构到个人的转变或责任。 Aren提供了一组指南，数据科学家和个人可以使用这些指南来提高对要考虑的道德来源数据标准的认识。

出色的读物： (An excellent read for:)

• Individuals interested in Data privacy

  对数据隐私感兴趣的个人

• Data Scientists / Data Analysts

  数据科学家/数据分析师

最可怕的事情，我们在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.

此次展览的一种文章形式，涉及读者和安全专家应注意的有价值的演讲，工具和技术，该展览形式已包含在今年的BlackHat活动中。

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

安全和隐私始终是随着更先进的技术和工具的发展而变化的主题。

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，我们观察到了基于国家安全和数据隐私问题对大量使用的移动应用程序的审查。

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，年度黑帽安全会议在网上广泛举行。 PCMag撰写了一篇文章，提供了有关此次活动中脱颖而出的演讲，技术和工具的简短详细信息。

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.

准备阅读有关可逆转被跟踪工具和跟踪器之间的角色的工具汇编； 或探索启用卫星的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汇编文章还包括安全专家的一系列演讲，在国家层面上传达了他们的担忧和安全经验。 其中一些谈话包括在美利坚合众国选举期间对与安全有关的主题的探讨。

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.

我从PCMag的汇编清单中脱颖而出的是揭开了间谍类设备的面纱，这些设备具有日常用品的形状，例如灯。

出色的读物： (An excellent read for:)

• Security enthusiasts

  安全爱好者

• Security experts

  安全专家

您应该阅读以理解深度学习时代中的对象检测的12篇论文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

解决对象检测的流行深度学习方法的详细探索

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

对象检测是基于计算机视觉的重要任务之一，研究人员和学者已设计出算法和基于启发式的方法。

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.

在最近的时间里，用于解决对象检测的大多数解决方案都植根于深度学习技术和方法的利用。

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撰写了一篇文章，探讨了十年来解决对象检测的深度学习方法的发展。

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的文章首先简要介绍了对象检测问题。 还包括必要的知识，才能正确地遵循本文的内容。

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.

每个包含的深度学习方法都配备了以下内容：引入的年份； 与该技术的相应研究论文的链接，更重要的是对该方法的工作方式和实施方式进行了较为详细的说明。

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.

我对Ethan能够压缩有关提出的技术的重要信息而无需创建冗长的技术分析(使大多数休闲读者感到厌烦)的水平给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.

每种引入的方法都附加有图像，这些图像说明了对象检测或算法过程的技术方法。

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

本文中包括的对象检测技术示例包括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.

总结这篇写得不错的文章，Ethan包括一些深度学习方法的引人注目的内容，这些内容对前面提到的技术进行了补充或对对象检测和建议的解决方案问题提供了进一步的了解。

出色的读物： (An excellent read for:)

• Data Scientists

  数据科学家

• Deep Learning Practitioners

  深度学习从业者

我将如何再次学习机器学习(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.

对从学习者到教育者的机器学习工程师历程的回顾性描述。

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成为在线机器学习社区中一个熟悉的名字。 多年来，Daniel发布了文章，视频，课程和材料，为机器学习的学生和从业人员提供了有关机器学习行业不同方面的见解。

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.

丹尼尔(Daniel)经历了相当多的机器学习旅程的三年，写了一篇文章，回顾性地分析了他的旅程和经验的关键事件。

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.

通过讲故事的方式告诉本文的上半部分，中级普通读者将不胜感激。 丹尼尔(Daniel)提到了旅程的开始，从谦虚的学习到专业的经历。

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讨论了想要移入下一个闪亮的框架，构建更多工具以及进行研究的感觉。 他还讨论了有关累积与机器学习相关的证书的问题。

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个蓝图，其中描述了Daniel如何利用他在机器学习相关主题中学习和获得知识的全部经验。

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.

该蓝图对于初学者来说是有用的资源，但对于Internet上可用的资源数量却不知所措。 下半部分充斥着资源链接，下半部分着重于通过时间衡量指标指导读者如何进行机器学习研究，他提到，应该辅之以个人项目。

出色的读物： (An excellent read for:)

• Data Scientist

  数据科学家

• Machine Learning Engineers

  机器学习工程师

我希望您觉得这篇文章有用。 (I hope you found the article useful.)

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

要与我联系或查找更多类似于本文的内容，请执行以下操作：

1. Subscribe to my Email List for weekly newsletters

   订阅我的电子邮件列表以获取每周新闻

2. Follow me on Medium

   跟我来中

3. Connect and reach me on LinkedIn

   在LinkedIn上联系并联系我