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Fix two typos in the install guide #206

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Mar 27, 2020
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Fix two typos in the install guide #206

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12 changes: 6 additions & 6 deletions content/en/installing-python-and-numpy-guide.md
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Expand Up @@ -37,7 +37,7 @@ On all of Windows, macOS and Linux:
#### Windows or macOS

- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
- Keep the `base` conda environment minimal, and use one or more
- Keep the `base` conda environment minimal, and use one or more
[conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)
to install the package you need for the task or project you're working on.
- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge`
Expand All @@ -54,15 +54,15 @@ able to use the latest versions of libraries:

If you use a GPU:
- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
- Keep the `base` conda environment minimal, and use one or more
- Keep the `base` conda environment minimal, and use one or more
[conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)
to install the package you need for the task or project you're working on.
- Use the `defaults` conda channel (`conda-forge` doesn't have good support for
GPU packages yet)

Otherwise:
- Install [Miniforge](https://github.com/conda-forge/miniforge)
- Keep the `base` conda environment minimal, and use one or more
- Keep the `base` conda environment minimal, and use one or more
[conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)
to install the package you need for the task or project you're working on.

Expand Down Expand Up @@ -173,7 +173,7 @@ now have two copies of OpenBLAS on disk.
In the Conda defaults channel, NumPy is built against Intel MKL. MKL is a
separate package that will be installed in the user's environment when she
installs NumPy. That MKL package is a lot larger than OpenBLAS, several hundred
MB. MKL is typically a little faster and more robust than OpenBLAS.
MB. MKL is typically a little faster and more robust than OpenBLAS.

In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When
the user install NumPy from conda-forge, that BLAS package then gets installed
Expand All @@ -189,8 +189,8 @@ consider:
- Both MKL and OpenBLAS will use multi-threading for function calls like
`np.dot`, with the number of threads being determined by both a build-time
option and an environment variable. Often all CPU cores will be used. This is
sometimes unexpected for users; NumPy itself doesn't auto-parallize any
sometimes unexpected for users; NumPy itself doesn't auto-parallelize any
function calls. It can also be harmful for performance, for example when
using another level of parallellization manually or with, e.g. Dask or
using another level of parallelization manually or with, e.g. Dask or
scikit-learn functionality.