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.github/workflows/ci.yml

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@@ -53,7 +53,7 @@ jobs:
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uses: actions/upload-artifact@v4
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if: failure()
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with:
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name: execution-reports
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name: execution-reports-notebooks
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path: _build/jupyter/reports
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- name: Build PDF from LaTeX
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shell: bash -l {0}

lectures/_config.yml

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# false-positive links
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linkcheck_ignore: ['https://online.stat.psu.edu/stat415/book/export/html/834']
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bibtex_reference_style: author_year
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suppress_warnings: ["mystnb.unknown_mime_type"]
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nb_mime_priority_overrides: [
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# HTML
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['html', 'application/vnd.jupyter.widget-view+json', 10],

lectures/back_prop.md

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```{include} _admonition/gpu.md
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```
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```{code-cell} ipython3
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:tags: [skip-execution]
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!pip install --upgrade jax
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```
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```{code-cell} ipython3
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import jax
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## to check that gpu is activated in environment
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print(f"JAX backend: {jax.devices()[0].platform}")
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```
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In addition to what's included in base Anaconda, we need to install the following packages
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```{code-cell} ipython3
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:tags: [hide-output]
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!pip install kaleido
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!conda install -y -c plotly plotly plotly-orca retrying
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!pip install -U kaleido plotly
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!conda install -y -c plotly plotly-orca
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# kaleido needs chrome to build images
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import kaleido
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kaleido.get_chrome_sync()
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```
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```{note}
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has `plotly` installed.
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```
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We also need to install JAX to run this lecture
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```{code-cell} ipython3
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:tags: [skip-execution]
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!pip install --upgrade jax
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```
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```{code-cell} ipython3
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import jax
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print(f"JAX backend: {jax.devices()[0].platform}") # to check that gpu is activated in environment
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```
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## Overview
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Substantial parts of **machine learning** and **artificial intelligence** are about

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