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Articles related to "feature"


Discord, a quarantine tech darling, raises another $100 million

  • New York (CNN Business) - One of the fastest growing communications apps is Discord, known primarily within the gaming community.
  • Discord has a feature that lets gamers share their screens and livestream the game they're playing to friends in a voice call.
  • At the start of the growing pandemic in March, Discord increased the number of people who can tune into a livestream to 50 users.
  • In a test conducted by CNN Business in May, an online birthday party of 19 people quickly ran into technical issues, as new users had to download the Discord app or encountered audio issues when speaking via the web browser page.
  • And when trying a new video game during a media Discord event, CNN Business observed other users using the wrong channel to ask for tech support and having difficulty getting the mic and audio to work.

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How I grew my Shopify micro-SaaS to $25k MRR and 20k users in 14 months

  • We wanted to enable our users to start using the app with the least amount of setup time or steps.
  • During initial days, we found it useful to let our users contact us on WhatsApp. It lead to tons of conversations which shaped our understanding of the user’s needs and how we can better solve them.
  • Until March 2020, users would install the app and directly land on the chat settings page, which is the starting point of the user’s journey.
  • When we added one screen which asked the user upfront whether they want to continue with the Free plan, or start a trial for the Paid plan, daily paid trials DOUBLED overnight, while trial-to-paid conversion rate remained steady at 50%.
  • If there’s copy on your website, or a specific sign up flow that might be holding you back from getting 2x the users, you need to unblock it early and realise compounded gains over time.

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Witnessing the Progression in Semantic Segmentation: DeepLab Series from V1 to V3+

  • Instead of trying to understand the boundary of an object through visual signals like contrast and sharpness, a deep convolutional neural network converts this task into a classification problem: if we know the class of every pixel in the image, we will get the boundary of objects for free.
  • DeepLabV1 and FCN both use VGG-16 as the backbone network to extract features before some fine-grained classification over pixels.
  • Because semantic segmentation usually needs very fine-grained details and high-level global features in the meantime, merging features from multi-scale is a very common method to combat coarse classification prediction.
  • DeepLab aggregates feature from many intermediate convolutional layers and the input, then interpolate the element-wise summed value back to original resolution as an output mask.
  • Finally, DeepLab V1 also uses a module called Fully-connected Conditional Random Field (CRF) to further polish the segmentation mask.

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Witnessing the Progression in Semantic Segmentation: DeepLab Series from V1 to V3+

  • Instead of trying to understand the boundary of an object through visual signals like contrast and sharpness, a deep convolutional neural network converts this task into a classification problem: if we know the class of every pixel in the image, we will get the boundary of objects for free.
  • DeepLabV1 and FCN both use VGG-16 as the backbone network to extract features before some fine-grained classification over pixels.
  • Because semantic segmentation usually needs very fine-grained details and high-level global features in the meantime, merging features from multi-scale is a very common method to combat coarse classification prediction.
  • DeepLab aggregates feature from many intermediate convolutional layers and the input, then interpolate the element-wise summed value back to original resolution as an output mask.
  • Finally, DeepLab V1 also uses a module called Fully-connected Conditional Random Field (CRF) to further polish the segmentation mask.

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Witnessing the Progression in Semantic Segmentation: DeepLab Series from V1 to V3+

  • Instead of trying to understand the boundary of an object through visual signals like contrast and sharpness, a deep convolutional neural network converts this task into a classification problem: if we know the class of every pixel in the image, we will get the boundary of objects for free.
  • DeepLabV1 and FCN both use VGG-16 as the backbone network to extract features before some fine-grained classification over pixels.
  • Because semantic segmentation usually needs very fine-grained details and high-level global features in the meantime, merging features from multi-scale is a very common method to combat coarse classification prediction.
  • DeepLab aggregates feature from many intermediate convolutional layers and the input, then interpolate the element-wise summed value back to original resolution as an output mask.
  • Finally, DeepLab V1 also uses a module called Fully-connected Conditional Random Field (CRF) to further polish the segmentation mask.

save | comments | report | share on


Witnessing the Progression in Semantic Segmentation: DeepLab Series from V1 to V3+

  • Instead of trying to understand the boundary of an object through visual signals like contrast and sharpness, a deep convolutional neural network converts this task into a classification problem: if we know the class of every pixel in the image, we will get the boundary of objects for free.
  • This idea comes from a paper called Fully Convolutional Network for Semantic Segmentation (FCN), and suddenly every researcher started to follow the suit.
  • DeepLabV1 and FCN both use VGG-16 as the backbone network to extract features before some fine-grained classification over pixels.
  • Because semantic segmentation usually needs very fine-grained details and high-level global features in the meantime, merging features from multi-scale is a very common method to combat coarse classification prediction.
  • Finally, DeepLab V1 also uses a module called Fully-connected Conditional Random Field (CRF) to further polish the segmentation mask.

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Discovering New Data

  • Remember that before trying to get data to solve a problem you need to get the context of a business and the project.
  • Remember that getting new data has to be done in a systematic fashion, it’s not just getting data out of nowhere, we have to do it consistently, plan it, create a process to do it, and this depends in engineering, architect, DataOps and more things that I’ll be discussing in other articles.
  • At this point the system it’s not only bringing new data from external sources but also creating new features based on our existing columns.
  • Remember that we are interested in finding more information about the businesses, so I’ll pick some columns coming from the external datasets and add them to my original data.

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Discovering New Data

  • Remember that before trying to get data to solve a problem you need to get the context of a business and the project.
  • Remember that getting new data has to be done in a systematic fashion, it’s not just getting data out of nowhere, we have to do it consistently, plan it, create a process to do it, and this depends in engineering, architect, DataOps and more things that I’ll be discussing in other articles.
  • At this point the system it’s not only bringing new data from external sources but also creating new features based on our existing columns.
  • Remember that we are interested in finding more information about the businesses, so I’ll pick some columns coming from the external datasets and add them to my original data.

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QuickBasic64: Basic for the Modern Era

  • Popular as a beginner programming language in the 80’s and evolving into a powerful professional tool in 90’s, BASIC (and its successor QBasic), helped many people develop a love for programming.
  • Compatible with most QBasic 4.5 code, QB64 adds a number of extensions, such as OpenGL and other modern features, providing the perfect blend of classic and modern program development.
  • Need to share a code snippet but don’t need all the fuss of traditional online tools?BASBin is what you’re after.Just paste code, hit the button, and share the link away.Files are wiped regularly, so treat it as the temporary storage it is, OK?
  • If that’s the case, BINBin is for you.Select the file to share, hit the button, and share the link.Files are wiped regularly, so treat it as the temporary storage it is, OK?

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Discovering New Data

  • Remember that before trying to get data to solve a problem you need to get the context of a business and the project.
  • Remember that getting new data has to be done in a systematic fashion, it’s not just getting data out of nowhere, we have to do it consistently, plan it, create a process to do it, and this depends in engineering, architect, DataOps and more things that I’ll be discussing in other articles.
  • At this point the system it’s not only bringing new data from external sources but also creating new features based on our existing columns.
  • Remember that we are interested in finding more information about the businesses, so I’ll pick some columns coming from the external datasets and add them to my original data.

save | comments | report | share on