Being an early Facebook executive must have been quite a job. Working for a fast growing company, changing the world, tackling interesting challenges, making big product decisions, and all the while working with Mark Zuckerberg every day.
Not too bad. It’s hard to imagine anyone wanting to leave the company. But that’s exactly what Adam D’Angelo did.
As the first Facebook CTO, D’Angelo left the company in 2009 to start Quora. D’Angelo, along with former Facebook employee Charlie Cheever, launched their company and it quickly appealed to the tech audience. It’s struggled to go mainstream, but nevertheless, given its recent $400 million valuation and impressive traffic numbers, this is a company that’s bound for even more success.
There is something much to learn from this innovative Q&A platform. In this blog post we’ll be covering how Quora masters these important business elements:
• Social media integration and user invites
Time on site is an important metric for determining engagement. For most businesses, increasing it is a goldmine. Currently, Facebook holds the crown with users spending an average of http://reyt.net/google-plus-average-user-spends-5-minutes-per-month-on-site-facebook-7-hours/9221.
If a business can improve their time on site, it generally signals high interest in the product, more page views, and increased ad revenue (if applicable).
It can mean that users are having a difficult time navigating the site or not able to find what they are looking for. If your time on site is long, you may want to install a tool like KISSinsights to see if users are finding what they’re looking for.
What Quora does to improve time on site:
There are over 600,000 questions on Quora. Part of Quora’s job is to help users discover new questions.
By seeing more questions, users will:
• Follow more questions
• Follow more topics
• Follow more people
All of this leads to more time on site and more content for the Quora user.
In order to help facilitate the discovery of more questions, Quora made a “Related Questions” section on every question page.
The COVID pandemic has massively escalated the surge of cyberattacks and data breaches despite having robust security controls, software, and solutions abundantly available in the market. A lot of this could be attributed to the vulnerability businesses offer the cybercriminals to take advantage of the situation quickly. While the conventional cybersecurity approach has benefited many, having cybersecurity without cyber-intelligence and necessary awareness can put the security professionals off-guarded to more complicated and novel threats.
Furthermore, with limited cybersecurity resources, businesses need to prioritise their efforts to strengthen cyber posture effectively; however, many organisations do not have an anchor point or a guiding principle, to begin with. With cyber-intelligence inputs missing from cybersecurity capabilities like incident management, vulnerability management, risk assessment and brand monitoring, businesses end up running their security practice in silos instead of an integrated approach.
And, thus, in an attempt to revolutionise the cyber threat visibility and intelligence market, CYFIRMA, a cyber analytics startup assists businesses to understand the relevance of the current threat landscape. Not only it provides insights on threat actors and indicators, emerging threats and digital risks, but also automatically applies intelligence into cyber posture management. To dig deeper, Analytics India Magazine got in touch with the chairman and CEO of the company, Kumar Ritesh, to understand how the company uses a predictive intelligence-driven approach to discover cyber threats.
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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These are the 10 highest paying jobs you can learn without needing a college degree. Jobs that pay $75,000 and higher.
📺 The video in this post was made by Andrei Jikh
The origin of the article: https://www.youtube.com/watch?v=QIGDA2JRz8w
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?
Figure 1: Portrait of Larry Roberts.
The field of Computer Vision dates back to the 1960s when Larry Roberts, who is now widely considered as the “Father of Computer Vision”, published his paper _Machine Perception of Three-Dimensional Solids _detailing how a computer can infer 3D shapes from a 2D image (Roberts, 1995). Since then, other researchers have made amazing contributions to the field. These advances, however, have not changed the underlaying goal of Computer Vision which is to mimic the human visual system. From an engineering point of view, this means being able to build autonomous systems that can do things a human visual system can do such as detecting and recognizing objects, recognizing faces and facial expressions, etc. (Huang, 1996). Traditionally, many approaches in Computer Vision involves manual feature extraction. This means manually finding some unique features/characteristics (edges, shapes, etc) that are only present in an object to be able to detect and recognize what that object is. Unfortunately, one major issue arises when trying to detect and recognize variations (sizes, lightning conditions, etc) of that same object. It is difficult to find features that can uniquely identify an object across all variations. Fortunately, this problem is now solved with the introduction of Machine Learning, particularly a sub-field of Machine Learning called Deep Learning. Deep Learning utilizes a form of Neural Networks called Convolutional Neural Networks (CNNs). Unlike the traditional methods, methods that utilize CNNs are able to extract features automatically. Instead of trying to figure out which features can represent an object manually, a CNN can learn those features automatically by looking at many variations of that same object. As result, many recent advancements in the field of Computer Vision involves the use of CNNs.
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