From 47a8b420296afb465147b3f61d8ee95c53663c6b Mon Sep 17 00:00:00 2001 From: jlaehne Date: Mon, 18 Mar 2024 22:26:33 +0100 Subject: [PATCH] replace github by doc links --- content/en/tabcontents.yaml | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/en/tabcontents.yaml b/content/en/tabcontents.yaml index b47c24a129..7c00aadeaf 100644 --- a/content/en/tabcontents.yaml +++ b/content/en/tabcontents.yaml @@ -1,8 +1,8 @@ params: machinelearning: paras: - - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://github.com/apache/incubator-mxnet) is another AI package, providing blueprints and templates for deep learning. - para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://mxnet.apache.org/) is another AI package, providing blueprints and templates for deep learning. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: @@ -27,7 +27,7 @@ params: text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://github.com/google/jax + url: https://jax.readthedocs.io/ - title: Xarray text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization img: /images/content_images/arlib/xarray.png @@ -57,7 +57,7 @@ params: text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png alttext: arrow - url: https://github.com/apache/arrow + url: https://arrow.apache.org/ - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. img: /images/content_images/arlib/xtensor.png @@ -103,11 +103,11 @@ params: links: - url: https://pandas.pydata.org/ label: Pandas - - url: https://github.com/statsmodels/statsmodels + - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - - url: https://github.com/mwaskom/seaborn + - url: https://seaborn.pydata.org/ label: Seaborn - title: Signal Processing alttext: A bar chart with positive and negative values. @@ -147,9 +147,9 @@ params: links: - url: https://www.astropy.org/ label: AstroPy - - url: https://github.com/sunpy/sunpy + - url: https://sunpy.org/ label: SunPy - - url: https://github.com/spacepy/spacepy + - url: https://spacepy.github.io/ label: SpacePy - title: Cognitive Psychology alttext: A human head with gears. @@ -189,7 +189,7 @@ params: label: SciPy - url: https://www.sympy.org/ label: SymPy - - url: https://github.com/cvxgrp/cvxpy + - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS @@ -254,7 +254,7 @@ params: - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" content: - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). @@ -292,7 +292,7 @@ params: which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), - [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), - and [PyVista](https://github.com/pyvista/pyvista), to name a few. + [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), + and [PyVista](https://docs.pyvista.org/), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.