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Articles related to "additional"


Salesforce names Vlocity founder David Schmaier CEO of new Salesforce Industries division

  • Today, the company announced that the deal has closed and Vlocity CEO David Schmaier has been named CEO of a new division called Salesforce Industries.
  • While Salesforce has developed some of its own industry solutions, having a division devoted to verticalized tools creates additional market opportunities for the company.
  • Writing in a blog post announcing the new division, Taylor said that like so many aspects of technology solutions these days, the industry focus is about helping companies with digital transformation.
  • It’s likely that Salesforce will continue to build on the new division and add additional applications over time given the platform is already in place.
  • The company does not rest on its laurels though and having a division in place like Salesforce Industries provides a more focused way of dealing with verticals and another possible source of revenue.

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How to Build a Data Science Portfolio Website

  • A month ago, I decided to create a website from scratch – without WordPress or Squarespace or any other template – to showcase my projects and my profile all in one place.
  • I wasn’t sure where or how to begin, especially because as data scientists, we don’t work with HTML, CSS, JavaScript or Flask that often.
  • So I wanted to provide a guide that will hopefully help you create your own data science portfolio website as well.
  • You can also take a look at my website for inspiration (you may want to use a Chrome browser so that it’s automatically translated because the site is in German).
  • You can also use your website to host your actual projects, whether it’s machine learning models or Plotly Dash implementations.
  • Feel free to check out the code for my website to get an idea of how you could implement the points above.

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Working with 3D data — fastai2

  • Working with 3D data or sequences of images is useful for a wide range of applications.
  • In this story, I will start by covering a basic example of fastai DataBlock — the building blocks to create dataloaders.
  • Then I will show how to code additional features to allow creating dataloaders with 3D data using the DataBlock, including augmentations for sequences of images.
  • The code below shows an example of the fastai DataBlock class for a typical image-based dataset.
  • For more advanced applications, it may be necessary to define custom block types and functions to create the batches.
  • As our batches have an “extra” dimension — corresponding to the sequence length — we can’t apply the fastai image augmentations out-of-the-box.
  • In this story, I covered how to work with 3D data for deep learning applications using fastai DataBlock.

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Working with 3D data — fastai2

  • Working with 3D data or sequences of images is useful for a wide range of applications.
  • In this story, I will start by covering a basic example of fastai DataBlock — the building blocks to create dataloaders.
  • Then I will show how to code additional features to allow creating dataloaders with 3D data using the DataBlock, including augmentations for sequences of images.
  • The code below shows an example of the fastai DataBlock class for a typical image-based dataset.
  • For more advanced applications, it may be necessary to define custom block types and functions to create the batches.
  • As our batches have an “extra” dimension — corresponding to the sequence length — we can’t apply the fastai image augmentations out-of-the-box.
  • In this story, I covered how to work with 3D data for deep learning applications using fastai DataBlock.

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Working with 3D data — fastai2

  • Working with 3D data or sequences of images is useful for a wide range of applications.
  • In this story, I will start by covering a basic example of fastai DataBlock — the building blocks to create dataloaders.
  • Then I will show how to code additional features to allow creating dataloaders with 3D data using the DataBlock, including augmentations for sequences of images.
  • The code below shows an example of the fastai DataBlock class for a typical image-based dataset.
  • For more advanced applications, it may be necessary to define custom block types and functions to create the batches.
  • As our batches have an “extra” dimension — corresponding to the sequence length — we can’t apply the fastai image augmentations out-of-the-box.
  • In this story, I covered how to work with 3D data for deep learning applications using fastai DataBlock.

save | comments | report | share on


Working with 3D data — fastai2

  • Working with 3D data or sequences of images is useful for a wide range of applications.
  • In this story, I will start by covering a basic example of fastai DataBlock — the building blocks to create dataloaders.
  • Then I will show how to code additional features to allow creating dataloaders with 3D data using the DataBlock, including augmentations for sequences of images.
  • The code below shows an example of the fastai DataBlock class for a typical image-based dataset.
  • For more advanced applications, it may be necessary to define custom block types and functions to create the batches.
  • As our batches have an “extra” dimension — corresponding to the sequence length — we can’t apply the fastai image augmentations out-of-the-box.
  • In this story, I covered how to work with 3D data for deep learning applications using fastai DataBlock.

save | comments | report | share on


Working with 3D data — fastai2

  • Working with 3D data or sequences of images is useful for a wide range of applications.
  • In this story, I will start by covering a basic example of fastai DataBlock — the building blocks to create dataloaders.
  • Then I will show how to code additional features to allow creating dataloaders with 3D data using the DataBlock, including augmentations for sequences of images.
  • The code below shows an example of the fastai DataBlock class for a typical image-based dataset.
  • For more advanced applications, it may be necessary to define custom block types and functions to create the batches.
  • As our batches have an “extra” dimension — corresponding to the sequence length — we can’t apply the fastai image augmentations out-of-the-box.
  • In this story, I covered how to work with 3D data for deep learning applications using fastai DataBlock.

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7 forecasting techniques you’ll never use, but should know them anyway

  • Let’s first implement three techniques: Naive, Mean and Drift models.
  • Naive forecast acts much like a null hypothesis against which to compare an alternative hypothesis — sales revenue will be different tomorrow because of such and such reasons.
  • Mean model, in contrast, takes all the past observations, makes an average, and uses this average as the forecast value.
  • If data is randomly distributed, without clear patterns and trends (also known as the white noise), a mean model works as a better benchmark than a naive model.
  • Mean model described above is a horizontal, constant line that doesn’t change over time because it works on training data without a trend.
  • In Geometric Random Walk, the forecast for the next value will be equal to the last value plus a constant change (e.g. a percentage monthly increase in revenue).

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7 forecasting techniques you’ll never use, but should know them anyway

  • Let’s first implement three techniques: Naive, Mean and Drift models.
  • Naive forecast acts much like a null hypothesis against which to compare an alternative hypothesis — sales revenue will be different tomorrow because of such and such reasons.
  • Mean model, in contrast, takes all the past observations, makes an average, and uses this average as the forecast value.
  • If data is randomly distributed, without clear patterns and trends (also known as the white noise), a mean model works as a better benchmark than a naive model.
  • Mean model described above is a horizontal, constant line that doesn’t change over time because it works on training data without a trend.
  • In Geometric Random Walk, the forecast for the next value will be equal to the last value plus a constant change (e.g. a percentage monthly increase in revenue).

save | comments | report | share on


7 forecasting techniques you’ll never use, but should know them anyway

  • Let’s first implement three techniques: Naive, Mean and Drift models.
  • Naive forecast acts much like a null hypothesis against which to compare an alternative hypothesis — sales revenue will be different tomorrow because of such and such reasons.
  • Mean model, in contrast, takes all the past observations, makes an average, and uses this average as the forecast value.
  • If data is randomly distributed, without clear patterns and trends (also known as the white noise), a mean model works as a better benchmark than a naive model.
  • Mean model described above is a horizontal, constant line that doesn’t change over time because it works on training data without a trend.
  • In Geometric Random Walk, the forecast for the next value will be equal to the last value plus a constant change (e.g. a percentage monthly increase in revenue).

save | comments | report | share on