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Articles related to "different"


The CSS Standardization Process

  • The most recent snapshot can always be found at this URL: http://www.w3.org/TR/CSS/ Implementers interested in the experimental features, and everybody who wants to help develop CSS, may also find the CSS current work page useful: it shows the current status and a short description of all existing parts of CSS.
  • Implementers interested in the experimental features, and everybody who wants to help develop CSS, may also find the CSS current work page useful: it shows the current status and a short description of all existing parts of CSS.

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Tableau: Unleashing the Power of Visual Analytics

  • Confused, I consulted multiple online resources on how to use Tableau and I came across Ben Jones’ Communicating Data with Tableau: Designing, Developing, and Delivering Data Visualizations book.
  • Moreover, I learnt how to use features like filters, pages, marks and others to build high quality worksheets.
  • Finally, I got the hang of utilizing the dashboard feature to build interactive visual dashboards that can be used to tell stories to people.
  • Below is a short summary of how Tableau is used to build beautiful interactive visual interfaces.
  • Another aspect that I love about Tableau is the fact that I can easily build my charts by dragging fields from the table section and integrating them with the features used to create the worksheets.
  • I used my knowledge of Tableau to build a scatter plot of Life Expectancy vs Income and make an interactive dashboard.

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Regularization — Part 3

  • At the input of the layer, you start measuring the mean and the standard deviation of the batch.
  • So what you do is you compute over the mini-batch, the current mean and standard deviation, and then you use that to normalize the activations of the input.
  • If you want to move on towards test time, you compute — after you finish the training — the mean and the standard deviation in the batch normalization layer once for the entire training set and you keep it and constant for all future applications of the network.
  • But if you want to have independent features that allow you to recognize different things, then you somehow have to break the correlation between the features and this can actually be performed by dropout.

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Deep Learning in Healthcare — X-Ray Imaging (Part 4-The Class Imbalance problem)

  • Also, it should be noted, while dealing with medical images, the final accuracy (both train accuracy or validation accuracy) of the model is not the right parameter to base the model’s performance on.
  • Important Note — Oversampling should be done on train data, and not on test data as if test data contains artificially generated images, the classifier results we will see would not be a proper interpretation of how much the network actually learned.
  • This function, creates all the artificially augmented images for normal and viral pneumonia images, till they reach the difference in values from the total bacterial pneumonia images.
  • Now that the class imbalance problem is dealt with in the next part we will look into image normalization and data augmentation using Keras and TensorFlow.

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Tableau: Unleashing the Power of Visual Analytics

  • Confused, I consulted multiple online resources on how to use Tableau and I came across Ben Jones’ Communicating Data with Tableau: Designing, Developing, and Delivering Data Visualizations book.
  • Moreover, I learnt how to use features like filters, pages, marks and others to build high quality worksheets.
  • Finally, I got the hang of utilizing the dashboard feature to build interactive visual dashboards that can be used to tell stories to people.
  • Below is a short summary of how Tableau is used to build beautiful interactive visual interfaces.
  • Another aspect that I love about Tableau is the fact that I can easily build my charts by dragging fields from the table section and integrating them with the features used to create the worksheets.
  • I used my knowledge of Tableau to build a scatter plot of Life Expectancy vs Income and make an interactive dashboard.

save | comments | report | share on


Regularization — Part 3

  • At the input of the layer, you start measuring the mean and the standard deviation of the batch.
  • So what you do is you compute over the mini-batch, the current mean and standard deviation, and then you use that to normalize the activations of the input.
  • If you want to move on towards test time, you compute — after you finish the training — the mean and the standard deviation in the batch normalization layer once for the entire training set and you keep it and constant for all future applications of the network.
  • But if you want to have independent features that allow you to recognize different things, then you somehow have to break the correlation between the features and this can actually be performed by dropout.

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The Tricky Math of Covid-19 Herd Immunity

  • That means the virus will spread at an accelerating rate until, on average across different places, 60% of the population becomes immune.
  • So how much lower is the herd immunity threshold when you’re talking about a virus spreading in the wild, like the current pandemic?
  • Another new study takes a different approach to estimating differences in susceptibility to COVID-19 and puts the herd immunity threshold even lower.
  • The paper’s 10 authors, who include Gomes and Langwig, estimate that the threshold for naturally acquired herd immunity to COVID-19 could be as low as 20% of the population.
  • In the meantime, to prevent the spread of the virus and lower that R0 value as much as possible, distancing, masks, testing and contact tracing are the order of the day everywhere, regardless of where you place the herd immunity threshold.

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How Ford is making sure its F150 remains the most popular truck ever

  • This article was originally published by Michael Coates on Clean Fleet Report, a publication that gives its readers the information they need to move to cars and trucks with best fuel economy, including electric cars, fuel cells, plug-in hybrids, hybrids and advanced diesel and gasoline engines.
  • It’s only been five years since the last major redesign of the Ford F-150, not a long time historically in the truck world, but with competition heating up both in the traditional competitors and newcomers, Ford is pushing forward with the introduction of the all-new 2021 F-150, a remake of its best-selling and most lucrative model.
  • Highlights of the introduction (some detail below) included the presentation of a new full hybrid model and confirmation that a full-electric version is coming.

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From context collapse to content collapse

  • The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly.” Zuckerberg praised context collapse as a force for moral cleanliness: “Having two identities for yourself is an example of a lack of integrity.” Facebook forces us to be pure.
  • Context collapse remains an important conceptual lens, but what’s becoming clear now is that a very different kind of collapse — content collapse — will be the more consequential legacy of social media.
  • Content collapse, as I define it, is the tendency of social media to blur traditional distinctions among once distinct types of information — distinctions of form, register, sense, and importance.

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Regularization — Part 2

  • So instead if you want to produce curves like the ones that I’m showing here you may want to use a validation set that you take off the training data set.
  • Now with a positive λ, we can identify this by the way as the lagrangian function of minimizing the loss function subject to constrained L2 norm of w being smaller than α with some unknown data-dependent α.
  • So, this is the right-hand part of the loss that we already computed with the learning rate η and in addition, you apply this kind of shrinkage to w.
  • So here, we then again end up in a Lagrangian formulation where we have the original loss function subject to the L1 norm being smaller than some value α with an unknown data-dependent α.

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