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new framework diagram
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2 changes: 1 addition & 1 deletion docs/_build/html/_sources/index.rst.txt
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</br></br>


.. image:: _static/images/web/framework_simple.png
.. image:: _static/images/web/framework_nov23.png
:width: 93%
:align: center
:alt: framework
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</a>
<br/>
</center>
</br></br><a class="reference internal image-reference" href="_images/framework_simple.png"><img alt="framework" class="align-center" src="_images/framework_simple.png" style="width: 93%;" /></a>
</br></br><a class="reference internal image-reference" href="_images/framework_nov23.png"><img alt="framework" class="align-center" src="_images/framework_nov23.png" style="width: 93%;" /></a>
<br/></br></br></br>
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4 changes: 2 additions & 2 deletions docs/_build/html/notebooks/api_high_level.html
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/api_high_level.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="0ec2839d10f145349c484e1bc67feb1d Declarative: The High-Level API is declarative. What does that mean? All you have to do is specify the state that you want the data in, and then the backend execute..." />
<meta property="og:description" content="3599a1516e8a496cbe5d75584b1e5719 Declarative: The High-Level API is declarative. What does that mean? All you have to do is specify the state that you want the data in, and then the backend execute..." />
<meta property="og:image" content="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/meta_image_tall_rect.png" />
<meta property="og:image:alt" content="Artificial Intelligence Quality Control" />
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</style>
<section id="API">
<h1>API<a class="headerlink" href="#API" title="Permalink to this headline"></a></h1>
<p><img alt="0ec2839d10f145349c484e1bc67feb1d" class="banner-photo" src="../_images/high-level_api.png" /></p>
<p><img alt="3599a1516e8a496cbe5d75584b1e5719" class="banner-photo" src="../_images/high-level_api.png" /></p>
<section id="Declarative">
<h2>Declarative<a class="headerlink" href="#Declarative" title="Permalink to this headline"></a></h2>
<p>The <em>High-Level API</em> is <em>declarative</em>. What does that mean? All you have to do is specify the <em>state</em> that you want the data in, and then the backend executes all of the tedious data wrangling needed to achieve that state. It’s like Terraform for machine learning.</p>
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4 changes: 2 additions & 2 deletions docs/_build/html/notebooks/api_low_level.html
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/api_low_level.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="e56ad2aa755546c7bb667ab36b65d0cb Object-Relational Model: The Low-Level API is an object-relational model for machine learning. Each class in the ORM maps to a table in a SQLite database that serve..." />
<meta property="og:description" content="14136ebbe80f44f38028ad66a35198b5 Object-Relational Model: The Low-Level API is an object-relational model for machine learning. Each class in the ORM maps to a table in a SQLite database that serve..." />
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</style>
<section id="ORM">
<h1>ORM<a class="headerlink" href="#ORM" title="Permalink to this headline"></a></h1>
<p><img alt="e56ad2aa755546c7bb667ab36b65d0cb" class="banner-photo" src="../_images/sql.png" /></p>
<p><img alt="14136ebbe80f44f38028ad66a35198b5" class="banner-photo" src="../_images/sql.png" /></p>
<section id="Object-Relational-Model">
<h2>Object-Relational Model<a class="headerlink" href="#Object-Relational-Model" title="Permalink to this headline"></a></h2>
<p>The Low-Level API is an <em>object-relational model</em> for machine learning. Each class in the <a class="reference external" href="http://docs.peewee-orm.com/en/latest/peewee/models.html">ORM</a> maps to a table in a SQLite database that serves as a machine learning <em>metastore</em>.</p>
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/datasets.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="bb9cbdbd75024a528f3efee2b43f82a3 Overview: This notebook contains information about the prepackaged datasets that are referenced throughout the documentation. These datasets are either: Included di..." />
<meta property="og:description" content="9983925983254f75a4cde6085dbcf7c3 Overview: This notebook contains information about the prepackaged datasets that are referenced throughout the documentation. These datasets are either: Included di..." />
<meta property="og:image" content="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/meta_image_tall_rect.png" />
<meta property="og:image:alt" content="Artificial Intelligence Quality Control" />
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</style>
<section id="Datasets">
<h1>Datasets<a class="headerlink" href="#Datasets" title="Permalink to this headline"></a></h1>
<p><img alt="bb9cbdbd75024a528f3efee2b43f82a3" class="banner-photo" src="../_images/datums.png" /></p>
<p><img alt="9983925983254f75a4cde6085dbcf7c3" class="banner-photo" src="../_images/datums.png" /></p>
<section id="Overview">
<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline"></a></h2>
<p>This notebook contains information about the prepackaged datasets that are referenced throughout the documentation. These datasets are either:</p>
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/evaluation.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="9c66c3f7291f4545b135310114bd9061 Overview: Every training Job automatically generates metrics when evaluated against each split/ fold. All Analyses Loss is every neural network’s measure of overall..." />
<meta property="og:description" content="4b9ca8bb5f114d8fb55eef50fe8e31dd Overview: Every training Job automatically generates metrics when evaluated against each split/ fold. All Analyses Loss is every neural network’s measure of overall..." />
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</style>
<section id="Evaluation">
<h1>Evaluation<a class="headerlink" href="#Evaluation" title="Permalink to this headline"></a></h1>
<p><img alt="9c66c3f7291f4545b135310114bd9061" class="banner-photo" src="../_images/plots.png" /></p>
<p><img alt="4b9ca8bb5f114d8fb55eef50fe8e31dd" class="banner-photo" src="../_images/plots.png" /></p>
<section id="Overview">
<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline"></a></h2>
<p>Every training <code class="docutils literal notranslate"><span class="pre">Job</span></code> automatically generates metrics when evaluated against each split/ fold.</p>
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/gallery/pytorch/bin_class.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="Detecting Naval Mines with Binary Classification of Sonar Data. 9592654a9286480cb36bb592fd65fd12 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: Features= son..." />
<meta property="og:description" content="Detecting Naval Mines with Binary Classification of Sonar Data. fef862f5d18343b28fc704108f174d27 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: Features= son..." />
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<section id="PyTorch:-Tabular-Classify-Binary">
<h1>PyTorch: Tabular Classify Binary<a class="headerlink" href="#PyTorch:-Tabular-Classify-Binary" title="Permalink to this headline"></a></h1>
<p><em>Detecting Naval Mines with Binary Classification of Sonar Data.</em></p>
<p><img alt="9592654a9286480cb36bb592fd65fd12" class="banner-photo" src="../../../_images/mines.png" /></p>
<p><img alt="fef862f5d18343b28fc704108f174d27" class="banner-photo" src="../../../_images/mines.png" /></p>
<section id="💾-Data">
<h2>💾 Data<a class="headerlink" href="#💾-Data" title="Permalink to this headline"></a></h2>
<p>Reference <a class="reference external" href="../../datasets.html">Example Datasets</a> for more information.</p>
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<meta property="og:type" content="website" />
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<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="Brain Tumor Detection via Binary Classification of Magnetic Resonance Imaging (MRI) Scans 42fb8d8cecde4d65b9c0491c93cd9006 Example Data: Reference Example Datasets for more information. This datase..." />
<meta property="og:description" content="Brain Tumor Detection via Binary Classification of Magnetic Resonance Imaging (MRI) Scans 801a93ffbf124bebbb83d4f3f1e5e7a8 Example Data: Reference Example Datasets for more information. This datase..." />
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<section id="PyTorch:-Image-Classify-Binary">
<h1>PyTorch: Image Classify Binary<a class="headerlink" href="#PyTorch:-Image-Classify-Binary" title="Permalink to this headline"></a></h1>
<p><em>Brain Tumor Detection via Binary Classification of Magnetic Resonance Imaging (MRI) Scans</em></p>
<p><img alt="42fb8d8cecde4d65b9c0491c93cd9006" class="banner-photo" src="../../../_images/brains.png" /></p>
<p><img alt="801a93ffbf124bebbb83d4f3f1e5e7a8" class="banner-photo" src="../../../_images/brains.png" /></p>
<section id="Example-Data">
<h2>Example Data<a class="headerlink" href="#Example-Data" title="Permalink to this headline"></a></h2>
<p>Reference <a class="reference external" href="../../datasets.html">Example Datasets</a> for more information.</p>
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/gallery/pytorch/multi_class.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="Categorizing Plant Species with Multi-Label Classification of Phenotypes. 95af74d598154bb29050bce0a34da7f5 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: Lab..." />
<meta property="og:description" content="Categorizing Plant Species with Multi-Label Classification of Phenotypes. 9d24fd08a2a743f4af9d6c294428b71a 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: Lab..." />
<meta property="og:image" content="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/meta_image_tall_rect.png" />
<meta property="og:image:alt" content="Artificial Intelligence Quality Control" />
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<section id="PyTorch:-Tabular-Classify-Multi-Label">
<h1>PyTorch: Tabular Classify Multi-Label<a class="headerlink" href="#PyTorch:-Tabular-Classify-Multi-Label" title="Permalink to this headline"></a></h1>
<p><em>Categorizing Plant Species with Multi-Label Classification of Phenotypes.</em></p>
<p><img alt="95af74d598154bb29050bce0a34da7f5" class="banner-photo" src="../../../_images/plants.png" /></p>
<p><img alt="9d24fd08a2a743f4af9d6c294428b71a" class="banner-photo" src="../../../_images/plants.png" /></p>
<section id="💾-Data">
<h2>💾 Data<a class="headerlink" href="#💾-Data" title="Permalink to this headline"></a></h2>
<p>Reference <a class="reference external" href="../../datasets.html">Example Datasets</a> for more information.</p>
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/gallery/pytorch/reg.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="Predicting Exoplanet Surface Temperature Using Kepler Satellite Sensor Data. 608f8898e4d34534b9eb5a575693104c 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: ..." />
<meta property="og:description" content="Predicting Exoplanet Surface Temperature Using Kepler Satellite Sensor Data. a03289f415f541258fc284d7c36c5081 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: ..." />
<meta property="og:image" content="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/meta_image_tall_rect.png" />
<meta property="og:image:alt" content="Artificial Intelligence Quality Control" />
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<section id="PyTorch:-Tabular-Regression">
<h1>PyTorch: Tabular Regression<a class="headerlink" href="#PyTorch:-Tabular-Regression" title="Permalink to this headline"></a></h1>
<p><em>Predicting Exoplanet Surface Temperature Using Kepler Satellite Sensor Data.</em></p>
<p><img alt="608f8898e4d34534b9eb5a575693104c" class="banner-photo" src="../../../_images/planets.png" /></p>
<p><img alt="a03289f415f541258fc284d7c36c5081" class="banner-photo" src="../../../_images/planets.png" /></p>
<section id="💾-Data">
<h2>💾 Data<a class="headerlink" href="#💾-Data" title="Permalink to this headline"></a></h2>
<p>Reference <a class="reference external" href="../../datasets.html">Example Datasets</a> for more information.</p>
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/gallery/pytorch/seq_class.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="Binary Detection of Epileptic Seizures Using a Cohort of Sequence of Electroencephalography (EEG) Readings. ff1c087ae5514f26b5b951be15e8a58d Sequence data structures contain many observations (rows..." />
<meta property="og:description" content="Binary Detection of Epileptic Seizures Using a Cohort of Sequence of Electroencephalography (EEG) Readings. 708cc90e2e1f4e788f257eb5d638e047 Sequence data structures contain many observations (rows..." />
<meta property="og:image" content="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/meta_image_tall_rect.png" />
<meta property="og:image:alt" content="Artificial Intelligence Quality Control" />
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<section id="PyTorch:-Times-Series-Classify-Binary">
<h1>PyTorch: Times Series Classify Binary<a class="headerlink" href="#PyTorch:-Times-Series-Classify-Binary" title="Permalink to this headline"></a></h1>
<p><em>Binary Detection of Epileptic Seizures Using a Cohort of Sequence of Electroencephalography (EEG) Readings.</em></p>
<p><img alt="ff1c087ae5514f26b5b951be15e8a58d" class="banner-photo" src="../../../_images/waves.png" /></p>
<p><img alt="708cc90e2e1f4e788f257eb5d638e047" class="banner-photo" src="../../../_images/waves.png" /></p>
<p>Sequence data structures contain many observations (rows) for each sample (e.g. site, sensor, or patient). They are often used for grouping time-based observations into what is called a time series. However, sequences can also represent biological sequences like DNA and RNA.</p>
<p>The cardinality of <em>many observations per sample</em> changes the dimensionality of the data from 2D to 3D. This effectively adds an additional layer of complexity to all aspects of data preparation. In this notebook, you’ll see that, once a <code class="docutils literal notranslate"><span class="pre">Dataset.Sequence</span></code> has been ingested, the AIQC API allows you to work with multivariate 3D data as easily as if it were 2D. As an example, you can still apply encoders by dtype and column_name.</p>
<hr class="docutils" />
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://docs.aiqc.io/notebooks/gallery/tensorflow/bin_class.html" />
<meta property="og:site_name" content="AIQC" />
<meta property="og:description" content="Detecting Naval Mines with Binary Classification of Sonar Data. d208833b63b64dd39e21a6bf3f8f4d37 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: Features= son..." />
<meta property="og:description" content="Detecting Naval Mines with Binary Classification of Sonar Data. de43342ec5604acba78ed4d4fda6976e 💾 Data: Reference Example Datasets for more information. This dataset is comprised of: Features= son..." />
<meta property="og:image" content="https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/meta_image_tall_rect.png" />
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<section id="TensorFlow:-Tabular-Classify-Binary">
<h1>TensorFlow: Tabular Classify Binary<a class="headerlink" href="#TensorFlow:-Tabular-Classify-Binary" title="Permalink to this headline"></a></h1>
<p><em>Detecting Naval Mines with Binary Classification of Sonar Data.</em></p>
<p><img alt="d208833b63b64dd39e21a6bf3f8f4d37" class="banner-photo" src="../../../_images/mines.png" /></p>
<p><img alt="de43342ec5604acba78ed4d4fda6976e" class="banner-photo" src="../../../_images/mines.png" /></p>
<section id="💾-Data">
<h2>💾 Data<a class="headerlink" href="#💾-Data" title="Permalink to this headline"></a></h2>
<p>Reference <a class="reference external" href="../../datasets.html">Example Datasets</a> for more information.</p>
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