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content/09-packages/python-packages.md

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of tools is list here: https://packaging.python.org/en/latest/guides/tool-recommendations/
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```
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![xkcd](python_environment.png)
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![xkcd comic on python packaging chaos](python_environment.png)
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(from https://xkcd.com)
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content/11-machine-learning/neural-net-basics.md

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We can visualize a neural network as:
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![NN diagram](nn_fig.png)
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![neural network diagram showing nodes in layers linked](nn_fig.png)
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* Neural networks are divided into _layers_
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content/11-machine-learning/neural-net-hidden.md

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We can get better performance from a neural network by adding a hidden layer:
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![hidden layers](nn_fig_hidden.png)
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![neutron network diagram showing a single hidden layer](nn_fig_hidden.png)
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The size of the hidden layer is independent of the size of the input and output
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layers. In this case, we have a hidden layer that is larger

content/Introduction.md

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# PHY 546: Python for Scientific Computing
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![xkcd](01-python/python.png)
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![xkcd python cartoon](01-python/python.png)
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(from https://xkcd.com)
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requirements.txt

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# docs
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jupyter-book<2
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git+https://github.com/executablebooks/sphinx-book-theme.git
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sphinx-copybutton
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sphinx-prompt
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jupyterlab_myst
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jupyterbook_patches
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# core python
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matplotlib
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#basemap
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numpy
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sympy
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scipy
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# ML
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keras
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#tensorflow
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torch
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pydot
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graphviz
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scikit-learn
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sphinx-copybutton
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sphinx-prompt
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jupyterlab_myst
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jupyterbook_patches
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