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11 días - Traducciones

Introducing Keras 3 for R We are thrilled to introduce {keras3}, the next version of the Keras R package. {keras3} is a ground-up rebuild of {keras}, maintaining the beloved features of the original while refining and simplifying the API based on valuable insights gathered over the past few years.
https://blogs.rstudio.com/tens....orflow/posts/2024-05

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Discover the world at Altruu, The Discovery Engine
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1 mes - Traducciones

News from the sparkly-verse Highlights to the most recent updates to `sparklyr` and friends
https://blogs.rstudio.com/tens....orflow/posts/2024-04

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Discover the world at Altruu, The Discovery Engine
    Tensorflow profile picture
2 meses - Traducciones

Chat with AI in RStudio Interact with Github Copilot and OpenAI's GPT (ChatGPT) models directly in RStudio. The `chattr` Shiny add-in makes it easy for you to interact with these and other Large Language Models (LLMs).
https://blogs.rstudio.com/tens....orflow/posts/2024-04

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Discover the world at Altruu, The Discovery Engine
    Tensorflow profile picture
3 años - Traducciones

torch: Just-in-time compilation (JIT) for R-less model deployment Using the torch just-in-time (JIT) compiler, it is possible to query a model trained in R from a different language, provided that language can make use of the low-level libtorch library. This post shows how. In addition, we try to untangle a bit of the terminological jumble surrounding the topic.
https://blogs.rstudio.com/tens....orflow/posts/2021-08


Discover the world at Altruu, The Discovery Engine
    Tensorflow profile picture
3 años - Traducciones

Starting to think about AI Fairness The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they're talking about. This text hopes to counteract some of that confusion by starting from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy.
https://blogs.rstudio.com/tens....orflow/posts/2021-07


Discover the world at Altruu, The Discovery Engine