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Merge pull request numpy#153 from numpy/feature/86-tabs
Feature/86 cleaner style for Ecosystem tabs widget
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‎config.yaml

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- title: Open source
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text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained publicly on GitHub by a vibrant, responsive and diverse community.
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tabs:
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title: Ecosystem
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subtitle:
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tiles:
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- title: App builder
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icon: mouse-globe
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text: This is some explanatory text that is on two rows
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url: /
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buttonText: Free trial
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- title: Cloud integration
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icon: laptop-cloud
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text: This is some explanatory text that is on two rows
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url: /
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buttonText: Get started
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- title: Add-ons & plugins
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icon: plug-cloud
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text: This is some explanatory text that is on two rows
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url: /
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buttonText: Get started
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title: ECOSYSTEM
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section5: false
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languages:

‎layouts/partials/array-libraries.html

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<!-- Array libraries Tab Content -->
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<li class="array-libraries">
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<p>
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Numpy array forms the core of the organically growing numeric
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Python <b>array library</b> ecosystem that now supports GPUs, sparse,
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distributed arrays and more.
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</p>
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<p>
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Several of these newer libraries such as CuPy, Sparse and Dask,
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implement the NumPy API adding support for modern user cases,
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newer hardware and higher scalability of array computing. Other
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array libraries such as Xarray, Tensor.ly consume NumPy API and
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build newer functionality on top of it, thus enhancing array
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computing in Python beyond Numpy capabilities.
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</p>
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<table>
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<tr class="highlight-th">
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<td class="bold-text"></td>
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<td class="bold-text">Array Library</td>
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<td class="bold-text">Capabilities & Application areas</td>
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</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/xarray.png" alt="xarray"></td>
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<td class="full-center-text"><a href="https://xarray.pydata.org/en/stable/index.html">Xarray</a></td>
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<td class="left-text">Labeled, indexed multidimensional arrays for advanced analytics and visualization</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/uarray.png" alt="uarray"></td>
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<td class="full-center-text"><a href="https://uarray.org/en/latest/">uarray</a></td>
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<td class="left-text">Python backend-system that decouples array computing library API definitions from
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implementation to enhance NumPy code capabilities.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/xnd.png" alt="xnd"></td>
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<td class="full-center-text"><a href="https://xnd.io">XND</a></td>
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<td class="left-text">Container type that maps Python values to typed memory to accelerate financial,
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insurance and scientific computing.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/xframes.png" alt="xframe"></td>
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<td class="full-center-text"><a href="https://xframe.readthedocs.io/en/latest/index.html">xframe</a></td>
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<td class="left-text">Multi-dimensional labelled array expressions and powerful abstractions for operating,
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analyzing large datasets.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/sparse.png" alt="sparse"></td>
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<td class="full-center-text"><a href="https://sparse.pydata.org/en/latest/">Sparse</a></td>
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<td class="left-text">Multi-dimensional arrays that integrate with SciPy-sparse, CuPy and Dask for Machine
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Learning and data analytics.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/dask.png" alt="Dask"></td>
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<td class="full-center-text"><a href="https://dask.org/">Dask</a></td>
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<td class="left-text">Distributed arrays, parallel computing for data analytics in geo-science, banking,
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astronomy, satellite imagery and mobile network modeling.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/CuPy.png" alt="CuPy"></td>
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<td class="full-center-text"><a href="https://cupy.chainer.org">CuPy</a></td>
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<td class="left-text">NumPy-compatible matrix library accelerated by CUDA used to implement Neural Networks
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for Deep Learning.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/tensorly.png" alt="tensorly"></td>
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<td class="full-center-text"><a href="http://tensorly.org/stable/home.html">Tensor.ly</a></td>
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<td class="left-text">Tensor learning, algebra and backend to seamlessly use NumPy, MXNet, PyTorch,
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TensorFlow or CuPy for ML recommendation systems.</td>
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</tr>
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<tr>
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<td class="left-text"><img class="first-column-layout" src="images/content_images/arlib/cuDF.png"
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alt="cudf"></td>
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<td class="full-center-text"><a href="https://github.com/rapidsai/cudf">CuDF</a></td>
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<td class="left-text">GPU DataFrame library built on the Apache Arrow columnar memory format for loading,
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joining, aggregating, filtering, and data wrangling.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/arrow.png" alt="arrow"></td>
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<td class="full-center-text"><a href="https://github.com/apache/arrow">Arrow</a></td>
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<td class="left-text">Cross-language platform that combines columnar data structure with in-memory data for
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streaming analytics and interactive visualizations.</td>
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</tr>
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<tr>
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<td><img class="first-column-layout" src="images/content_images/arlib/xtensor.png" alt="xtensor"></td>
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<td class="full-center-text"><a href="" https://github.com/xtensor-stack/xtensor-python>xtensor </a> </td>
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<td class="left-text">Multi-dimensional arrays with broadcasting and lazy computing for numerical
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analysis.</td>
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</tr>
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<tr style="border-bottom:2px solid #dddddd">
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<td colspan="80%"></td>
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</tr>
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</table>
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</li>

‎layouts/partials/css.html

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<link rel="stylesheet" type="text/css" href="{{ $css.RelPermalink }}" integrity="{{ $css.Data.Integrity }}">
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{{- end }}
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<link rel="stylesheet" type="text/css" href="{{ "css/icons.css" | relURL }}">
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<!-- Custom CSS -->
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<link rel="stylesheet" type="text/css" href="{{ "css/styles.css" | relURL }}" />
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<link rel="stylesheet" type="text/css" href="{{ "css/shell.css" | relURL }}" />
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<link rel="stylesheet" type="text/css" href="{{ "css/section2.css" | relURL }}" />
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<link rel="stylesheet" type="text/css" href="{{ "css/content.css" | relURL }}" />
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<!-- Numpy Favicon -->
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<link rel="icon" type="image/png" href="{{ "images/favicon.png" | relURL }}" />
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<!-- Google Fonts - Ubuntu -->
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<link href="https://fonts.googleapis.com/css?family=Ubuntu&display=swap" rel="stylesheet">
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<link href="https://fonts.googleapis.com/css?family=Rubik&display=swap" rel="stylesheet">
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<link href="https://fonts.googleapis.com/css?family=Lato:400,900&display=swap" rel="stylesheet">
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<link href="https://fonts.googleapis.com/css?family=Source+Code+Pro&display=swap" rel="stylesheet">
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<!-- Tabs CSS -->
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<!-- Custom CSS -->
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<link rel="stylesheet" type="text/css" href="{{ "css/styles.css" | relURL }}" />
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<link rel="stylesheet" type="text/css" href="{{ "css/shell.css" | relURL }}" />
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<link rel="stylesheet" type="text/css" href="{{ "css/tabs.css" | relURL }}" />
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<link rel="stylesheet" type="text/css" href="{{ "css/content.css" | relURL }}" />

‎layouts/partials/data-science.html

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<!-- Data Science Tab Content -->
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<li class="data-science">
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<div class="grid-container">
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<div>
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<img src="images/content_images/ds-landscape.png" alt="ds-landscape" align="left">
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</div>
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<div>
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<p>
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Data Science makes it possible to analyze massive amounts of data
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and gain meaningful insights. A typical data science workflow involves
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various techniques and tools such as:
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<ul class="content-tab">
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<li><b>Extract, Transform, Load (ETL):</b> Pandas, Beautiful Soup, Intake</li>
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<li><b>Explore:</b> Seaborn, Matplotlib</li>
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<li><b>Model:</b> Scikit-learn, SciPy, statsmodels</li>
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<li><b>Evaluate:</b> NumPy, TensorFlow </li>
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<li>
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<b>Presentation:</b>
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<a href="./index.html/#tab-visual"> Data Visualization Tools</a>
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</li>
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</ul>
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</p>
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</div>
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</div>
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<div class="grid-container">
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<div>
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<p>
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Python has a rich ecosystem of libraries that enable Data Science workflows
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. <b> NumPy</b> is the foundation of almost all of these tools.
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<a href="https://pandas.pydata.org">
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</p>
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<p>
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<a href="https://pandas.pydata.org">Pandas</a>helps in data discovery and handling,
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<a href="https://intake.readthedocs.io/en/latest/"> Intake</a> helps with
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data access and distribution, while
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<a href="https://www.crummy.com/software/BeautifulSoup/">Beautiful Soup</a>
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is widely used for web-scraping and gathering data sets.
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<a href="https://seaborn.pydata.org"> Seaborn</a> is well known for its
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<a href="https://towardsdatascience.com/how-to-perform-exploratory-data-analysis-with-seaborn-97e3413e841d">exploratory data analysis (EDA)</a>
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capabilities, <a href="https://scikit-learn.org">Scikit-learn</a> and
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<a href="https://www.scipy.org">Scipy</a> (statistical computing) serve some
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of the backbone processes required for machine learning (regression methods,
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classification, clustering, model validation and selection).
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Statistical data exploration, estimation of various statistical models
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and conducting statistical tests are some of the functions offered by
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<a href="https://www.statsmodels.org/stable/index.html"> statsmodels</a>.
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</p>
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</div>
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<div>
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<img src="images/content_images/data-science.png" alt="data science" align="centre" width="75%">
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</div>
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</div>
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<p>
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Effective data analytics require deep knowledge of the data domain (e.g.,
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Retail, Healthcare, Marketing, Finance, Social Media, Automation, Sales, Travel,
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etc.) as well as other core disciplines of Data Science, Data Engineering and
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Data Visualization. Tools such as <a href="https://mlflow.org">MLFlow</a> address
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experiment hyper-parameter and result tracking needs, while
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<a href="https://dvc.org"> DVC</a> provide data version control for data science
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and machine learning workflows.
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</p>
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</li>

‎layouts/partials/javascript.html

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<script src="https://cdnjs.cloudflare.com/ajax/libs/modernizr/2.8.3/modernizr.min.js"></script>
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<!-- Font Awesome -->
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<script src="https://kit.fontawesome.com/9e9265cc6d.js"></script>
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<!-- Tabs.js -->
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<script src="{{ "js/tabs-main.js" | relURL }}"></script>
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<script src="{{ "js/tabs-utils.js" | relURL }}"></script>
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<!-- UIkit -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/uikit.min.js"></script>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/uikit.min.css" />
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<!-- Thebe Configuration -->
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<script type="text/x-thebe-config">
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{

‎layouts/partials/machine-learning.html

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<!-- Machine Learning Tab Content -->
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<li class="machine-learning">
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<div class="grid-container">
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<div class="animation-holder">
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<a href="https://ai.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.html">
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<img src="images/content_images/ml_img/tensorflow-ml-anim.gif" alt="Tensorflow-embedding-projector" align="middle" class="animation-img">
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</a>
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<p>
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<i style="font-size:80%;">
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<a href="https://ai.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.html">Source: Google AI Blog</a>
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</i>
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</p>
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</div>
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<div>
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<p>
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<b>Machine learning</b> (ML) enables computers to learn using
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data, without having to be explicitly programmed.
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<b>NumPy</b> is the foundation of all data pre-processing
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that happens in the implementation of several ML Algorithms.
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</p>
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<p>
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Python’s rich machine language and deep learning ecosystem
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provides powerful tools such as
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<a href="https://scikit-learn.org/stable/">Scikit-learn</a>
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that is built on top of NumPy and
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<a href="https://www.scipy.org">SciPy</a> and offers data
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mining and analytics using classical ML algorithms.
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</p>
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<p>
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<a href="https://www.tensorflow.org">Tensorflow’s</a>
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deep learning capabilities help to define and run
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computations involving tensors that have broad
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applications in Speech and image recognition, Text-based
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applications, Time-Series analysis and Video Detection.
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<a href="https://pytorch.org">PyTorch </a> is another deep
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learning library that is very popular among researchers for
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computer vision and NLP applications. <a href="https://github.com/apache/incubator-mxnet">MXNet</a>
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is another AI package that provides blueprints and
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templates for deep learning.
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</p>
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</div>
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</div>
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<div>
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<div>
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<p>
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Statistical techniques called
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<a href="https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205">Ensemble</a>
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methods such as binning,
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bagging, stacking and boosting are widely used in various ML
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algorithms implemented by tools such as
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<a href="https://github.com/dmlc/xgboost">XGBoost</a>,
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<a href="https://lightgbm.readthedocs.io/en/latest/">LightGBM</a>,
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<a href="https://catboost.ai">CatBoost</a> - one of the
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fastest inference engines.
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<a href="https://www.scikit-yb.org/en/latest/">Yellowbrick</a>,
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<a href="https://eli5.readthedocs.io/en/latest/">Eli5</a>
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offer machine learning visualizations.
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</p>
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</div>
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</div>
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</li>

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