+.. |grid-start| raw:: html +

-.. to add a testimonials, just XXX +.. |grid-split| raw:: html -`J.P.Morgan `_ ------------------------------------------- +

-.. raw:: html +.. |grid-end| raw:: html + +

+ +`J.P.Morgan `_ +----------------------------------------- + +|grid-start| Scikit-learn is an indispensable part of the Python machine learning toolkit at JPMorgan. It is very widely used across all parts of the bank @@ -26,35 +34,22 @@ learning tasks. Its straightforward API, its breadth of algorithms, and the quality of its documentation combine to make scikit-learn simultaneously very approachable and very powerful. -.. raw:: html +.. rst-class:: annotation - + Stephen Simmons, VP, Athena Research, JPMorgan -Stephen Simmons, VP, Athena Research, JPMorgan - -.. raw:: html - - -

+|grid-split| .. image:: images/jpmorgan.png - :width: 120pt - :align: center - :target: https://www.jpmorgan.com + :target: https://www.jpmorgan.com -.. raw:: html +|grid-end| -

`Spotify `_ ------------------------------------ -.. raw:: html - -

+|grid-start| Scikit-learn provides a toolbox with solid implementations of a bunch of state-of-the-art models and makes it easy to plug them into existing @@ -62,39 +57,22 @@ applications. We've been using it quite a lot for music recommendations at Spotify and I think it's the most well-designed ML package I've seen so far. -.. raw:: html - - - -Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify +.. rst-class:: annotation -.. raw:: html + Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify - -

+|grid-split| .. image:: images/spotify.png - :width: 120pt - :align: center - :target: https://www.spotify.com + :target: https://www.spotify.com -.. raw:: html +|grid-end| -

`Inria `_ -------------------------------- -.. raw:: html - -

- -.. title Scikit-learn for efficient and easier machine learning research -.. Author: Gaël Varoquaux - +|grid-start| At INRIA, we use scikit-learn to support leading-edge basic research in many teams: `Parietal `_ for neuroimaging, `Lear @@ -105,37 +83,22 @@ tool to address difficult applications of machine learning in an academic environment as it is performant and versatile, but all easy-to-use and well documented, which makes it well suited to grad students. +.. rst-class:: annotation -.. raw:: html - - - -Gaël Varoquaux, research at Parietal + Gaël Varoquaux, research at Parietal -.. raw:: html - - -

+|grid-split| .. image:: images/inria.png - :width: 120pt - :align: center - :target: https://www.inria.fr/ + :target: https://www.inria.fr/ -.. raw:: html - -

+|grid-end| `betaworks `_ ------------------------------------ -.. raw:: html - -

+|grid-start| Betaworks is a NYC-based startup studio that builds new products, grows companies, and invests in others. Over the past 8 years we've launched a @@ -148,36 +111,22 @@ in `digg's new video recommender system and Poncho's `dynamic heuristic subspace clustering `_. -.. raw:: html - - +.. rst-class:: annotation -Gilad Lotan, Chief Data Scientist + Gilad Lotan, Chief Data Scientist -.. raw:: html - - -

+|grid-split| .. image:: images/betaworks.png - :width: 120pt - :align: center - :target: https://betaworks.com + :target: https://betaworks.com -.. raw:: html - -

+|grid-end| `Hugging Face `_ ---------------------------------------- -.. raw:: html - -

+|grid-start| At Hugging Face we're using NLP and probabilistic models to generate conversational Artificial intelligences that are fun to chat with. Despite using @@ -188,36 +137,22 @@ interface, as well as the straightforward mathematical explanations that are here when you need them, is the killer feature. We use a variety of scikit-learn models in production and they are also operationally very pleasant to work with. -.. raw:: html - - +.. rst-class:: annotation -Julien Chaumond, Chief Technology Officer + Julien Chaumond, Chief Technology Officer -.. raw:: html - - -

+|grid-split| .. image:: images/huggingface.png - :width: 120pt - :align: center - :target: https://huggingface.co + :target: https://huggingface.co -.. raw:: html - -

+|grid-end| `Evernote `_ ---------------------------------- -.. raw:: html - -

+|grid-start| Building a classifier is typically an iterative process of exploring the data, selecting the features (the attributes of the data believed @@ -225,37 +160,24 @@ to be predictive in some way), training the models, and finally evaluating them. For many of these tasks, we relied on the excellent scikit-learn package for Python. -`Read more `_ - -.. raw:: html +`Read more... `_ - +.. rst-class:: annotation -Mark Ayzenshtat, VP, Augmented Intelligence + Mark Ayzenshtat, VP, Augmented Intelligence -.. raw:: html - - -

+|grid-split| .. image:: images/evernote.png - :width: 120pt - :align: center - :target: https://evernote.com + :target: https://evernote.com -.. raw:: html +|grid-end| -

`Télécom ParisTech `_ -------------------------------------------------------- -.. raw:: html - -

+|grid-start| At Telecom ParisTech, scikit-learn is used for hands-on sessions and home assignments in introductory and advanced machine learning courses. The classes @@ -263,35 +185,22 @@ are for undergrads and masters students. The great benefit of scikit-learn is its fast learning curve that allows students to quickly start working on interesting and motivating problems. -.. raw:: html +.. rst-class:: annotation - + Alexandre Gramfort, Assistant Professor -Alexandre Gramfort, Assistant Professor - -.. raw:: html - - -

+|grid-split| .. image:: images/telecomparistech.jpg - :width: 120pt - :align: center - :target: https://www.telecom-paristech.fr/ - -.. raw:: html + :target: https://www.telecom-paristech.fr/ -

+|grid-end| `Booking.com `_ ------------------------------------------ -.. raw:: html +---------------------------------------- -

+|grid-start| At Booking.com, we use machine learning algorithms for many different applications, such as recommending hotels and destinations to our customers, @@ -303,36 +212,22 @@ the art implementations and new algorithms into the package. Thus, scikit-learn provides convenient access to a wide spectrum of algorithms, and allows us to readily find the right tool for the right job. +.. rst-class:: annotation -.. raw:: html + Melanie Mueller, Data Scientist - - -Melanie Mueller, Data Scientist - -.. raw:: html - - -

+|grid-split| .. image:: images/booking.png - :width: 120pt - :align: center - :target: https://www.booking.com + :target: https://www.booking.com -.. raw:: html +|grid-end| -

`AWeber `_ ------------------------------------------- - -.. raw:: html +----------------------------------- -

+|grid-start| The scikit-learn toolkit is indispensable for the Data Analysis and Management team at AWeber. It allows us to do AWesome stuff we would not otherwise have @@ -347,35 +242,22 @@ Anyone interested in learning more about how AWeber deploys scikit-learn in a production environment should check out talks from PyData Boston by AWeber's Michael Becker available at https://github.com/mdbecker/pydata_2013 -.. raw:: html - - - -Michael Becker, Software Engineer, Data Analysis and Management Ninjas +.. rst-class:: annotation -.. raw:: html + Michael Becker, Software Engineer, Data Analysis and Management Ninjas - -

+|grid-split| .. image:: images/aweber.png - :width: 120pt - :align: center - :target: https://www.aweber.com/ + :target: https://www.aweber.com/ -.. raw:: html +|grid-end| -

`Yhat `_ ------------------------------------------- +------------------------------ -.. raw:: html - -

+|grid-start| The combination of consistent APIs, thorough documentation, and top notch implementation make scikit-learn our favorite machine learning package in @@ -384,36 +266,22 @@ anyone. At Yhat, we make it easy to integrate these models into your production applications. Thus eliminating the unnecessary dev time encountered productionizing analytical work. +.. rst-class:: annotation -.. raw:: html - - - -Greg Lamp, Co-founder Yhat + Greg Lamp, Co-founder Yhat -.. raw:: html - - -

+|grid-split| .. image:: images/yhat.png - :width: 120pt - :align: center - :target: https://www.yhat.com + :target: https://www.yhat.com -.. raw:: html +|grid-end| -

`Rangespan `_ ----------------------------------------- - -.. raw:: html +--------------------------------------- -

+|grid-start| The Python scikit-learn toolkit is a core tool in the data science group at Rangespan. Its large collection of well documented models and @@ -423,35 +291,22 @@ We find that scikit-learn is not only the right tool for prototyping, but its careful and well tested implementation give us the confidence to run scikit-learn models in production. -.. raw:: html - - +.. rst-class:: annotation -Jurgen Van Gael, Data Science Director at Rangespan Ltd + Jurgen Van Gael, Data Science Director at Rangespan Ltd -.. raw:: html - - -

+|grid-split| .. image:: images/rangespan.png - :width: 120pt - :align: center - :target: http://www.rangespan.com + :target: http://www.rangespan.com -.. raw:: html +|grid-end| -

`Birchbox `_ ------------------------------------------- - -.. raw:: html +-------------------------------------- -

+|grid-start| At Birchbox, we face a range of machine learning problems typical to E-commerce: product recommendation, user clustering, inventory prediction, @@ -462,36 +317,22 @@ informed of new developments from the pattern discovery research community. Scikit-learn is an important tool for our team, built the right way in the right language. -.. raw:: html +.. rst-class:: annotation - + Thierry Bertin-Mahieux, Birchbox, Data Scientist -Thierry Bertin-Mahieux, Birchbox, Data Scientist - -.. raw:: html - - -

+|grid-split| .. image:: images/birchbox.jpg - :width: 120pt - :align: center - :target: https://www.birchbox.com - -.. raw:: html + :target: https://www.birchbox.com -

+|grid-end| `Bestofmedia Group `_ --------------------------------------------------- +------------------------------------------------- -.. raw:: html - -

+|grid-start| Scikit-learn is our #1 toolkit for all things machine learning at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting, @@ -501,35 +342,22 @@ In the lab it accelerates prototyping of complex pipelines. In production I can say it has proven to be robust and efficient enough to be deployed for business critical components. -.. raw:: html +.. rst-class:: annotation - + Eustache Diemert, Lead Scientist Bestofmedia Group -Eustache Diemert, Lead Scientist Bestofmedia Group - -.. raw:: html - - -

+|grid-split| .. image:: images/bestofmedia-logo.png - :width: 120pt - :align: center - :target: http://www.bestofmedia.com + :target: http://www.bestofmedia.com -.. raw:: html +|grid-end| -

`Change.org `_ -------------------------------------- -.. raw:: html - -

+|grid-start| At change.org we automate the use of scikit-learn's RandomForestClassifier in our production systems to drive email targeting that reaches millions @@ -537,35 +365,22 @@ of users across the world each week. In the lab, scikit-learn's ease-of-use, performance, and overall variety of algorithms implemented has proved invaluable in giving us a single reliable source to turn to for our machine-learning needs. -.. raw:: html - - +.. rst-class:: annotation -Vijay Ramesh, Software Engineer in Data/science at Change.org + Vijay Ramesh, Software Engineer in Data/science at Change.org -.. raw:: html - - -

+|grid-split| .. image:: images/change-logo.png - :width: 120pt - :align: center - :target: https://www.change.org + :target: https://www.change.org -.. raw:: html +|grid-end| -

`PHIMECA Engineering `_ ----------------------------------------------------------- - -.. raw:: html +--------------------------------------------------------- -

+|grid-start| At PHIMECA Engineering, we use scikit-learn estimators as surrogates for expensive-to-evaluate numerical models (mostly but not exclusively @@ -577,35 +392,22 @@ also using scikit-learn for illustrating concepts in our training sessions. Trainees are always impressed by the ease-of-use of scikit-learn despite the apparent theoretical complexity of machine learning. -.. raw:: html +.. rst-class:: annotation - + Vincent Dubourg, PHIMECA Engineering, PhD Engineer -Vincent Dubourg, PHIMECA Engineering, PhD Engineer - -.. raw:: html - - -

+|grid-split| .. image:: images/phimeca.png - :width: 120pt - :align: center - :target: https://www.phimeca.com/?lang=en + :target: https://www.phimeca.com/?lang=en -.. raw:: html +|grid-end| -

`HowAboutWe `_ ----------------------------------------------------------- - -.. raw:: html +------------------------------------------ -

+|grid-start| At HowAboutWe, scikit-learn lets us implement a wide array of machine learning techniques in analysis and in production, despite having a small team. We use @@ -616,36 +418,22 @@ primarily unstructured data (answers to open-ended questions), so we use scikit-learn's feature extraction and dimensionality reduction tools to translate these unstructured data into inputs for our matchmaking system. -.. raw:: html +.. rst-class:: annotation - + Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe -Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe - -.. raw:: html - - -

+|grid-split| .. image:: images/howaboutwe.png - :width: 120pt - :align: center - :target: http://www.howaboutwe.com/ - -.. raw:: html + :target: http://www.howaboutwe.com/ -

+|grid-end| `PeerIndex `_ ------------------------------------------------------------------ -.. raw:: html - -

+|grid-start| At PeerIndex we use scientific methodology to build the Influence Graph - a unique dataset that allows us to identify who's really influential and in which @@ -657,69 +445,48 @@ learn proved useful in a variety of applications. Its very intuitive interface and excellent compatibility with other python tools makes it and indispensable tool in our daily research efforts. -.. raw:: html +.. rst-class:: annotation - + Ferenc Huszar - Senior Data Scientist at Peerindex -Ferenc Huszar - Senior Data Scientist at Peerindex - -.. raw:: html - - -

+|grid-split| .. image:: images/peerindex.png - :width: 120pt - :align: center - :target: https://www.brandwatch.com/peerindex-and-brandwatch - -.. raw:: html + :target: https://www.brandwatch.com/peerindex-and-brandwatch -

+|grid-end| `DataRobot `_ ---------------------------------------- -.. raw:: html - -

- -DataRobot is building next generation predictive analytics software to make data scientists more productive, and scikit-learn is an integral part of our system. The variety of machine learning techniques in combination with the solid implementations that scikit-learn offers makes it a one-stop-shopping library for machine learning in Python. Moreover, its consistent API, well-tested code and permissive licensing allow us to use it in a production environment. Scikit-learn has literally saved us years of work we would have had to do ourselves to bring our product to market. - -.. raw:: html +|grid-start| - +DataRobot is building next generation predictive analytics software to make data +scientists more productive, and scikit-learn is an integral part of our system. +The variety of machine learning techniques in combination with the solid +implementations that scikit-learn offers makes it a one-stop-shopping library for +machine learning in Python. Moreover, its consistent API, well-tested code and +permissive licensing allow us to use it in a production environment. Scikit-learn has +literally saved us years of work we would have had to do ourselves to bring our +product to market. -Jeremy Achin, CEO & Co-founder DataRobot Inc. +.. rst-class:: annotation -.. raw:: html + Jeremy Achin, CEO & Co-founder DataRobot Inc. - -

+|grid-split| .. image:: images/datarobot.png - :width: 120pt - :align: center - :target: https://www.datarobot.com - -.. raw:: html + :target: https://www.datarobot.com -

+|grid-end| `OkCupid `_ --------------------------------------- +------------------------------------- -.. raw:: html - -

+|grid-start| We're using scikit-learn at OkCupid to evaluate and improve our matchmaking system. The range of features it has, especially preprocessing utilities, means @@ -727,36 +494,22 @@ we can use it for a wide variety of projects, and it's performant enough to handle the volume of data that we need to sort through. The documentation is really thorough, as well, which makes the library quite easy to use. -.. raw:: html - - +.. rst-class:: annotation -David Koh - Senior Data Scientist at OkCupid + David Koh - Senior Data Scientist at OkCupid -.. raw:: html - - -

+|grid-split| .. image:: images/okcupid.png - :width: 120pt - :align: center - :target: https://www.okcupid.com + :target: https://www.okcupid.com -.. raw:: html - -

+|grid-end| `Lovely `_ ----------------------------------- -.. raw:: html - -

+|grid-start| At Lovely, we strive to deliver the best apartment marketplace, with respect to our users and our listings. From understanding user behavior, improving data @@ -766,75 +519,48 @@ documentation and intuitive architecture of the API makes machine learning both explorable and accessible to a wide range of python developers. I'm constantly recommending that more developers and scientists try scikit-learn. -.. raw:: html - - +.. rst-class:: annotation -Simon Frid - Data Scientist, Lead at Lovely + Simon Frid - Data Scientist, Lead at Lovely -.. raw:: html - - -

+|grid-split| .. image:: images/lovely.png - :width: 120pt - :align: center - :target: https://livelovely.com - -.. raw:: html - -

+ :target: https://livelovely.com +|grid-end| `Data Publica `_ ---------------------------------------------- -.. raw:: html - -

+|grid-start| -Data Publica builds a new predictive sales tool for commercial and marketing teams called C-Radar. -We extensively use scikit-learn to build segmentations of customers through clustering, and to predict future customers based on past partnerships success or failure. -We also categorize companies using their website communication thanks to scikit-learn and its machine learning algorithm implementations. -Eventually, machine learning makes it possible to detect weak signals that traditional tools cannot see. -All these complex tasks are performed in an easy and straightforward way thanks to the great quality of the scikit-learn framework. +Data Publica builds a new predictive sales tool for commercial and marketing teams +called C-Radar. We extensively use scikit-learn to build segmentations of customers +through clustering, and to predict future customers based on past partnerships success +or failure. We also categorize companies using their website communication thanks to +scikit-learn and its machine learning algorithm implementations. Eventually, machine +learning makes it possible to detect weak signals that traditional tools cannot see. +All these complex tasks are performed in an easy and straightforward way thanks to the +great quality of the scikit-learn framework. -.. raw:: html +.. rst-class:: annotation - + Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica -Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica - -.. raw:: html - - -

+|grid-split| .. image:: images/datapublica.png - :width: 120pt - :align: center - :target: http://www.data-publica.com/ - -.. raw:: html - -

+ :target: http://www.data-publica.com/ +|grid-end| `Machinalis `_ ------------------------------------------- -.. raw:: html - -

+|grid-start| Scikit-learn is the cornerstone of all the machine learning projects carried at Machinalis. It has a consistent API, a wide selection of algorithms and lots @@ -848,36 +574,22 @@ into Python packages, some of them open-sourced, like `FeatureForge `_ . Scikit-learn in one word: Awesome. -.. raw:: html - - +.. rst-class:: annotation -Rafael Carrascosa, Lead developer + Rafael Carrascosa, Lead developer at Machinalis -.. raw:: html - - -

+|grid-split| .. image:: images/machinalis.png - :width: 120pt - :align: center - :target: https://www.machinalis.com/ + :target: https://www.machinalis.com/ -.. raw:: html - -

+|grid-end| `solido `_ ----------------------------------------- -.. raw:: html - -

+|grid-start| Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates computer-aided design tools used by the majority of top-20 semiconductor @@ -887,40 +599,25 @@ rare-event estimation, worst-case verification, optimization, and more. At Solido, we are particularly fond of scikit-learn's libraries for Gaussian Process models, large-scale regularized linear regression, and classification. Scikit-learn has increased our productivity, because for many ML problems we no -longer need to “roll our own” code. `This PyData 2014 talk `_ has details. - - -.. raw:: html +longer need to “roll our own” code. +`This PyData 2014 talk `_ has details. - +.. rst-class:: annotation -Trent McConaghy, founder, Solido Design Automation Inc. + Trent McConaghy, founder, Solido Design Automation Inc. -.. raw:: html - - -

+|grid-split| .. image:: images/solido_logo.png - :width: 120pt - :align: center - :target: https://www.solidodesign.com/ - -.. raw:: html - -

+ :target: https://www.solidodesign.com/ +|grid-end| `INFONEA `_ ------------------------------------------ - -.. raw:: html +--------------------------------------- -

+|grid-start| We employ scikit-learn for rapid prototyping and custom-made Data Science solutions within our in-memory based Business Intelligence Software @@ -930,36 +627,22 @@ us to provide flexible and scalable scientific analysis solutions. Thus, scikit-learn is immensely valuable in realizing a powerful integration of Data Science technology within self-service business analytics. -.. raw:: html - - +.. rst-class:: annotation -Thorsten Kranz, Data Scientist, Coma Soft AG. + Thorsten Kranz, Data Scientist, Coma Soft AG. -.. raw:: html - - -

+|grid-split| .. image:: images/infonea.jpg - :width: 120pt - :align: center - :target: http://www.infonea.com/en/ + :target: http://www.infonea.com/en/ -.. raw:: html - -

+|grid-end| `Dataiku `_ ------------------------------------------ +------------------------------------- -.. raw:: html - -

+|grid-start| Our software, Data Science Studio (DSS), enables users to create data services that combine `ETL `_ with @@ -970,36 +653,22 @@ is to offer a transparent and flexible tool that makes it easier to optimize time consuming aspects of building a data service, preparing data, and training machine learning algorithms on all types of data. +.. rst-class:: annotation -.. raw:: html - - - -Florian Douetteau, CEO, Dataiku + Florian Douetteau, CEO, Dataiku -.. raw:: html - - -

+|grid-split| .. image:: images/dataiku_logo.png - :width: 120pt - :align: center - :target: https://www.dataiku.com/ + :target: https://www.dataiku.com/ -.. raw:: html +|grid-end| -

`Otto Group `_ ------------------------------------------ - -.. raw:: html +-------------------------------------- -

+|grid-start| Here at Otto Group, one of global Big Five B2C online retailers, we are using scikit-learn in all aspects of our daily work from data exploration to development @@ -1009,36 +678,22 @@ It consistent APIs enabled us to build the `Palladium REST-API framework `_ around it and continuously deliver scikit-learn based services. +.. rst-class:: annotation -.. raw:: html + Christian Rammig, Head of Data Science, Otto Group - - -Christian Rammig, Head of Data Science, Otto Group - -.. raw:: html - - -

+|grid-split| .. image:: images/ottogroup_logo.png - :width: 120pt - :align: center - :target: https://ottogroup.com + :target: https://ottogroup.com -.. raw:: html +|grid-end| -

`Zopa `_ ------------------------------------------ - -.. raw:: html +--------------------------- -

+|grid-start| At Zopa, the first ever Peer-to-Peer lending platform, we extensively use scikit-learn to run the business and optimize our users' experience. It powers our @@ -1048,35 +703,22 @@ It is very well documented, powerful, and simple to use. We are grateful for the capabilities it has provided, and for allowing us to deliver on our mission of making money simple and fair. -.. raw:: html - - +.. rst-class:: annotation -Vlasios Vasileiou, Head of Data Science, Zopa + Vlasios Vasileiou, Head of Data Science, Zopa -.. raw:: html - - -

+|grid-split| .. image:: images/zopa.png - :width: 120pt - :align: center - :target: https://zopa.com + :target: https://zopa.com -.. raw:: html +|grid-end| -

`MARS `_ --------------------------------------- +------------------------------------- -.. raw:: html - -

+|grid-start| Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether we're designing better recipes for petfood or closely analysing our cocoa @@ -1086,36 +728,22 @@ the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is easy to use and equips our associates with the capabilities they need to solve the business challenges they face every day. -.. raw:: html - - +.. rst-class:: annotation -Michael Fitzke Next Generation Technologies Sr Leader, Mars Inc. + Michael Fitzke, Next Generation Technologies Sr Leader, Mars Inc. -.. raw:: html - - -

+|grid-split| .. image:: images/mars.png - :width: 120pt - :align: center - :target: https://www.mars.com/global + :target: https://www.mars.com/global -.. raw:: html - -

+|grid-end| `BNP Paribas Cardif `_ --------------------------------------------------------- -.. raw:: html - -

+|grid-start| BNP Paribas Cardif uses scikit-learn for several of its machine learning models in production. Our internal community of developers and data scientists has @@ -1128,24 +756,13 @@ support open source software development and in particular scikit-learn project, we decided to participate to scikit-learn's consortium at La Fondation Inria since its creation in 2018. -.. raw:: html - - +.. rst-class:: annotation -Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif + Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif -.. raw:: html - - -

+|grid-split| .. image:: images/bnp_paribas_cardif.png - :width: 120pt - :align: center - :target: https://www.bnpparibascardif.com/ + :target: https://www.bnpparibascardif.com/ -.. raw:: html - -

+|grid-end| diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css index 774c25f18e7cc..f4f48861c8241 100644 --- a/doc/themes/scikit-learn-modern/static/css/theme.css +++ b/doc/themes/scikit-learn-modern/static/css/theme.css @@ -1148,34 +1148,6 @@ table.dataframe tbody tr:hover { font-size: 0.9rem; } - -/* testimonial */ - -div.testimonial h2 { - background-color: transparent; - color: #008EB2; - padding: 0; - height: 26px; - line-height: 1.1em; - font-size: 22px; - font-weight: bold; - text-align: left; -} - -div.testimonial p { - color: #1c1c1c; -} - -div.testimonial span.testimonial-author p { - font-size: 0.8em; - font-style: italic; - color: #808080; -} - -div.testimonial p { - color: #1c1c1c; -} - /* Installation quickstart */ /* This quickstart installation is a hack of the awesome https://spacy.io/usage/#quickstart page. @@ -1309,25 +1281,37 @@ div.sk-sponsor-div, div.sk-testimonial-div { align-items: center; } -div.sk-sponsor-div-box, div.sk-testimonial-div-box { +div.sk-sponsor-div-text-box, div.sk-testimonial-div-text-box, +div.sk-sponsor-div-image-box, div.sk-testimonial-div-image-box { width: 100%; } @media screen and (min-width: 500px) { - div.sk-sponsor-div-box, div.sk-testimonial-div-box { + div.sk-sponsor-div-text-box, div.sk-testimonial-div-text-box, + div.sk-sponsor-div-image-box, div.sk-testimonial-div-image-box { width: 50%; } } -table.sk-sponsor-table tr, table.sk-sponsor-table tr:nth-child(odd) { - border-style: none; - background-color: white; - vertical-align: middle; +div.sk-sponsor-div-text-box .annotation, +div.sk-testimonial-div-text-box .annotation { + font-size: 0.8em; + font-style: italic; + color: #808080; +} + +div.sk-sponsor-div-image-box, div.sk-testimonial-div-image-box { text-align: center; } -table.sk-sponsor-table td { - padding: 0.30rem; +div.sk-sponsor-div-image-box img { + max-height: 60px; + max-width: 50%; +} + +div.sk-testimonial-div-image-box img { + max-height: 100px; + max-width: 50%; } .caption {