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Fix spelling typos
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advanced/mathematical_optimization/index.rst

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See also :func:`scipy.optimize.approx_fprime` to find your errors.
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Synthetic exercices
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Synthetic exercises
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-------------------
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.. |flat_min_0| image:: auto_examples/images/sphx_glr_plot_exercise_flat_minimum_001.png
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:target: auto_examples/plot_exercise_ill_conditioned.html
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:align: right
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.. topic:: **Exercice: A simple (?) quadratic function**
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.. topic:: **Exercise: A simple (?) quadratic function**
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:class: green
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Optimize the following function, using K[0] as a starting point::
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Time your approach. Find the fastest approach. Why is BFGS not
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working well?
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.. topic:: **Exercice: A locally flat minimum**
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.. topic:: **Exercise: A locally flat minimum**
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:class: green
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Consider the function `exp(-1/(.1*x**2 + y**2)`. This function admits

intro/numpy/operations.rst

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The remainder of this chapter is not necessary to follow the rest of
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the intro part. But be sure to come back and finish this chapter, as
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well as to do some more :ref:`exercices <numpy_exercises>`.
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well as to do some more :ref:`exercises <numpy_exercises>`.

packages/statistics/examples/plot_wage_data.py

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This example uses seaborn to quickly plot various factors relating wages,
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experience and eduction.
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experience, and education.
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Seaborn (https://seaborn.pydata.org) is a library that combines
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visualization and statistical fits to show trends in data.

packages/statistics/index.rst

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A regression capturing the relation between one variable and another, eg
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wage and eduction, can be plotted using :func:`seaborn.lmplot`::
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wage, and education, can be plotted using :func:`seaborn.lmplot`::
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>>> seaborn.lmplot(y='WAGE', x='EDUCATION', data=data) # doctest: +SKIP
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