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  1. Train-model-subsets-R.Rmd

    --- title: "Infogram Train Subset Models Demo Notebook" output: html_document: df_print: paged --- ```{r} library(h2o) h2o.init() # Import HMDA dataset f <- "https://erin-data.s3.amazonaws.com/admi...
    docs.h2o.ai/h2o/latest-stable/h2o-docs/admissibleml-code-examples/Train-model-subsets-R.Rmd
    Registered: Mon Aug 25 03:25:57 UTC 2025
    - Last Modified: Thu Mar 27 17:18:08 UTC 2025
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  2. bootstrap.js

    /*! For license information please see bootstrap.js.LICENSE.txt */ (()=>{"use strict";var t={d:(e,i)=>{for(var n in i)t.o(i,n)&&!t.o(e,n)&&Object.defineProperty(e,n,{enumerable:!0,get:i[n]})},o:(t,...
    pandas.pydata.org/pandas-docs/stable/_static/scripts/bootstrap.js Similar Results (1)
    Registered: Mon Aug 25 09:20:15 UTC 2025
    - Last Modified: Mon Nov 27 10:50:02 UTC 2023
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  3. fa-regular-400.woff2

    24028
    pandas.pydata.org/pandas-docs/stable/_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2 Similar Results (1)
    Registered: Mon Aug 25 09:20:00 UTC 2025
    - Last Modified: Mon Nov 27 10:50:02 UTC 2023
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  4. kind-gcr.sh

    #!/bin/sh set -o errexit # desired cluster name; default is "kind" KIND_CLUSTER_NAME="${KIND_CLUSTER_NAME:-kind}" # create a temp file for the docker config echo "Creating temporary docker client c...
    kind.sigs.k8s.io/examples/kind-gcr.sh
    Registered: Mon Aug 25 06:58:27 UTC 2025
    - 1.5K bytes
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  5. kind-with-registry.sh

    #!/bin/sh set -o errexit # 1. Create registry container unless it already exists reg_name='kind-registry' reg_port='5001' if [ "$(docker inspect -f '{{.State.Running}}' "${reg_name}" 2>/dev/null ||...
    kind.sigs.k8s.io/examples/kind-with-registry.sh
    Registered: Mon Aug 25 06:58:30 UTC 2025
    - 2.4K bytes
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  6. stateless-model-evaluation.html.md

    # Stateless Model Evaluation Vespa's speciality is evaluating machine-learned models quickly over large numbers of data points. However, it can also be used to evaluate models once on request in st...
    docs.vespa.ai/en/stateless-model-evaluation.html.md
    Registered: Mon Aug 25 04:52:54 UTC 2025
    - Last Modified: Fri Aug 22 21:21:16 UTC 2025
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  7. model-hub.html.md

    # Using machine-learned models from Vespa Cloud Vespa Cloud provides a set of machine-learned models that you can use in your applications. These models will always be available on Vespa Cloud and ...
    docs.vespa.ai/en/cloud/model-hub.html.md
    Registered: Mon Aug 25 04:53:47 UTC 2025
    - Last Modified: Fri Aug 22 21:21:16 UTC 2025
    - 13.2K bytes
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  8. feature-tuning.html.md

    # Vespa Serving Tuning This document describes how to tune certain features of an application for high query serving performance, where the main focus is on content cluster search features; see [Co...
    docs.vespa.ai/en/performance/feature-tuning.html.md
    Registered: Mon Aug 25 04:53:50 UTC 2025
    - Last Modified: Fri Aug 22 21:21:16 UTC 2025
    - 35.1K bytes
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  9. result-rendering.html.md

    # Result Renderers Vespa provides a default JSON format for query results. _Renderers_ can be configured to implement custom formats, like binary and text format. Renderers should not be used to im...
    docs.vespa.ai/en/result-rendering.html.md
    Registered: Mon Aug 25 04:54:28 UTC 2025
    - Last Modified: Fri Aug 22 21:21:16 UTC 2025
    - 9.9K bytes
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  10. ranking.html.md

    # Ranking Vespa ranks documents retrieved by a query by performing computations or inference that produces a score for each document. The documents are sorted in descending order by this score, and...
    docs.vespa.ai/en/ranking.html.md
    Registered: Mon Aug 25 04:53:31 UTC 2025
    - Last Modified: Fri Aug 22 21:21:16 UTC 2025
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