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The following commit(s) were added to refs/heads/main by this push:
new 2d94f8fbd [CI] Fix pre-commit failures from #1497 (#1499)
2d94f8fbd is described below
commit 2d94f8fbdbbb942b4920160a129ac752ccd761fe
Author: Jie-Kai Chang <[email protected]>
AuthorDate: Wed Sep 23 09:03:25 2026 +0800
[CI] Fix pre-commit failures from #1497 (#1499)
Signed-off-by: 400Ping <[email protected]>
---
docs/learning/books-tutorials-and-talks.md | 10 +++++-----
docs/learning/reference-reading.md | 10 +++++-----
.../version-0.6/learning/books-tutorials-and-talks.md | 10 +++++-----
.../versioned_docs/version-0.6/learning/reference-reading.md | 10 +++++-----
4 files changed, 20 insertions(+), 20 deletions(-)
diff --git a/docs/learning/books-tutorials-and-talks.md
b/docs/learning/books-tutorials-and-talks.md
index 78ae7d6b4..4dedb44ff 100644
--- a/docs/learning/books-tutorials-and-talks.md
+++ b/docs/learning/books-tutorials-and-talks.md
@@ -64,7 +64,7 @@ For general background on linear algebra, statistics, and
machine learning, see
### Mahout Classic
-* [Mahout 0.10.x: first Mahout release as a programming
environment](http://www.weatheringthroughtechdays.com/2015/04/mahout-010x-first-mahout-release-as.html)
+* [Mahout 0.10.x: first Mahout release as a programming
environment](http://www.weatheringthroughtechdays.com/2015/04/mahout-010x-first-mahout-release-as.html)
* [Comparing Document Classification Functions of Lucene and
Mahout](http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html)
* [Apache Mahout: Scalable Machine Learning for
Everyone](http://www.ibm.com/developerworks/java/library/j-mahout-scaling/)
* [How to build a spam filter server with
Mahout](http://emmaespina.wordpress.com/2011/04/26/ham-spam-and-elephants-or-how-to-build-a-spam-filter-server-with-mahout/)
- Applying classification on a live server - April 2011
@@ -101,11 +101,11 @@ In reverse chronological order, so that most recent talks
are at the top.
* Distributed Machine Learning with Apache Mahout - Suneel Marthi at Apache
Big Data North America, Vancouver, Canada, May 11, 2016 and MapR Washington DC
Big Data Everywhere, Tysons, VA, June 2 2016
* [Declarative Machine Learning with the Samsara
DSL](http://www.slideshare.net/FlinkForward/sebastian-schelter-distributed-machine-learing-with-the-samsara-dsl)
Sebastian Schelter at Flink Forward Conference, Berlin Germany, October 2015.
* [Bringing Algebraic Semantics to
Mahout](http://www.slideshare.net/sscdotopen/bringing-algebraic-semantics-to-mahout)
Sebastian Schelter at HPI Infolunch, Potsdam Germany, May 2014
-* Mahout Spark and Scala bindings: Bringing Algebraic Semantics
([slides](http://www.slideshare.net/DmitriyLyubimov/mahout-scala-and-spark-bindings)/[video](http://youtu.be/h9dpmvNW1Dw))
- Dmitriy Lyubimov at Mahout Meetup, April 17, 2014.
+* Mahout Spark and Scala bindings: Bringing Algebraic Semantics
([slides](http://www.slideshare.net/DmitriyLyubimov/mahout-scala-and-spark-bindings)/[video](http://youtu.be/h9dpmvNW1Dw))
- Dmitriy Lyubimov at Mahout Meetup, April 17, 2014.
* Mahout Future Directions - Ted Dunning, Suneel Marthi, Sebastian Schelter at
Hadoop Summit Europe 2014, Amsterdam, April 3, 2014
* Building Recommender Systems for Mere-Mortals - Sebastian Schelter at
Researchgate Developer Day, Berlin, November 2013
* Recommendations with Apache Mahout - Sebastian Schelter at IBM Almaden
Research Center, San Jose, September 2013
-* [Next Directions in Mahout’s
Recommenders](http://de.slideshare.net/sscdotopen/next-directions-in-mahouts-recommenders)
- Sebastian Schelter at Bay Area Mahout Meetup, Redwood City, August 2013
+* [Next Directions in Mahout’s
Recommenders](http://de.slideshare.net/sscdotopen/next-directions-in-mahouts-recommenders)
- Sebastian Schelter at Bay Area Mahout Meetup, Redwood City, August 2013
* [New Directions in Mahout’s
Recommenders](http://de.slideshare.net/sscdotopen/new-directions-in-mahouts-recommenders)
- Sebastian Schelter at Recommender Systems Get Together Berlin, April 2013
* [Introduction to Mahout and Machine
Learning](http://www.slideshare.net/VaradMeru/introduction-to-mahout-and-machine-learning)
- Slides by Varad Meru, Software Development Engineer at Orzota. July 27th,
2013.
* [An Introduction to Collaborative Filtering with Apache
Mahout](http://de.slideshare.net/sscdotopen/introduction-to-collaborative-filtering-with-apache-mahout)
- Sebastian Schelter at Recommender Systems Challenge Workshop in conjunction
with ACM RecSys 2012, Dublin, September 2012
@@ -118,7 +118,7 @@ In reverse chronological order, so that most recent talks
are at the top.
* First Mahout Hackathon, Berlin, March 2011
* [Mahout
meetup](http://blog.jteam.nl/2011/01/13/announcement-lucene-nl-mahout-meetup-with-isabel-drost-feb-7/)
- there were two talks at the Apache Mahout meetup at JTeam in Amsterdam,
February 2011. [intro slides](http://isabel-drost.de/hadoop/slides/jteam.pdf)
* [Mahout
clustering](http://www.fosdem.org/2011/schedule/event/mahoutclustering.html) -
Talk on Mahout clustering at data dev room FOSDEM, February 2011.
-* Scaling Data Analysis with Apache Mahout - talk on Mahout at O'Reilly
Strata, February 2011.
+* Scaling Data Analysis with Apache Mahout - talk on Mahout at O'Reilly
Strata, February 2011.
* [Practical Machine
Learning](http://www.slideshare.net/jaganadhg/mahout-tutorial-fossmeet-nitc) -
Slides from Biju B and Jaganadh G, FOSSMEET-NITC, Calicut, India, February 2011.
* [Mahout at AlphaCSPs The Edge 2010
(pdf)](http://www.javaedge.com/jedge/pdf/Mahout.pdf) -
[slideshare](http://www.slideshare.net/arikogan/mahouts-presentation-at-alphacsps-the-edge-2010)
- Slides from [Ariel Kogan](http://il.linkedin.com/in/arielkogan) AlphaCSP's
The Edge, December 2010.
* [Intelligent data analysis with Apache
Mahout](http://isabel-drost.de/hadoop/slides/devoxx.pdf) - Slides from Isabel
Drost, Devoxx Antwerp, November 2010.
@@ -148,7 +148,7 @@ In reverse chronological order, so that most recent talks
are at the top.
* [BI Over Petabytes: Meet Apache
Mahout](http://cwiki.apache.org/confluence/download/attachments/88410/SDForum.pdf)
- Slides from a talk by Jeff Eastman on April 21, 2009 at the Bay Area SD
Forum Business Intelligence SIG meeting at SAP in Palo Alto, CA.
* Lucene Meetup and Apache Barcamp in Amsterdam, March 2009.
* BarCampRDU - (Raleigh) on Aug. 2, 2008
-* Introducing Mahout: Apache Machine Learning - Committer Grant Ingersoll gave
a gentle introduction to Mahout and Machine Learning at ApacheCon in November
(3rd through 7th) in New Orleans, USA.
+* Introducing Mahout: Apache Machine Learning - Committer Grant Ingersoll gave
a gentle introduction to Mahout and Machine Learning at ApacheCon in November
(3rd through 7th) in New Orleans, USA.
* Mahout: Scaling Machine Learning - Introduction to Mahout and machine
learning at FrOSCon in Sankt Augustin/Germany, Isabel Drost, August 2008.
([slides](http://cwiki.apache.org/confluence/download/attachments/88410/froscon.pdf))
* Mahout: Scalable Machine Learning - An introduction to Mahout and machine
learning at the first German Hadoop gathering in newthinking store/ Berlin,
Isabel Drost, July 2008.
* Apache Mahout: Industrial Strength Machine Learning - Committer Jeff Eastman
gave an introduction to Mahout at Yahoo!, May 2008
diff --git a/docs/learning/reference-reading.md
b/docs/learning/reference-reading.md
index 583466aeb..b20b97abd 100644
--- a/docs/learning/reference-reading.md
+++ b/docs/learning/reference-reading.md
@@ -33,7 +33,7 @@ the Mahout community. And unlike learning some particular
software tool,
these are skills that will remain useful decades later.
* [Gilbert Strang](http://www-math.mit.edu/~gs)
's [Introduction to Linear Algebra](http://math.mit.edu/linearalgebra/). His
[lectures](http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)
are also [available online](http://web.mit.edu/18.06/www/)
- and are strongly recommended.
+ and are strongly recommended.
* [Mathematical Tools for Applied Mulitvariate
Analysis](http://www.amazon.com/Mathematical-Tools-Applied-Multivariate-Analysis/dp/0121609553/ref=sr_1_1?ie=UTF8&qid=1299602805&sr=8-1)
by J.Douglass
Carroll.
* [Stanford Machine Learning online
courseware](http://www.stanford.edu/class/cs229/)
@@ -48,22 +48,22 @@ Some good introductory alternatives here are:
Once you have a grasp of the basics then there are a slew of great texts that
you might consult:
* [Statistical
Inference](http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126),
Casell and Berger, Duxbury/Thomson Learning.
-* [Introduction to Bayesian
Statistics](http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202),
William H. Bolstad, Wiley.
+* [Introduction to Bayesian
Statistics](http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202),
William H. Bolstad, Wiley.
* [Understanding Computational Bayesian
Statistics](http://www.amazon.com/Understanding-Computational-Bayesian-Statistics-Wiley/dp/0470046090),
Bolstadt
* [Bayesian Data Analysis, Gelman et
al.](http://www.stat.columbia.edu/~gelman/book/)
### For statistics related to machine learning, these are particularly helpful:
* [Pattern Recognition and Machine Learning by Chris
Bishop](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
-* [Elements of Statistical
Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) by Trevor Hastie,
Robert Tibshirani, Jerome Friedman
+* [Elements of Statistical
Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) by Trevor Hastie,
Robert Tibshirani, Jerome Friedman
*
[http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
-
+
### For matrix computations/decomposition/factorization etc.:
* Peter V. O'Neil [Introduction to Linear
Algebra](http://www.amazon.com/Introduction-Linear-Algebra-Theory-Applications/dp/053400606X),
great book for beginners (with some knowledge in calculus). It is not
comprehensive, but, it will be a good place to start and the author starts by
explaining the concepts with regards to vector spaces which I found to be a
more natural way of explaining.
* David S. Watkins [Fundamentals of Matrix
Computations](http://www.amazon.com/Fundamentals-Matrix-Computations-Applied-Mathematics/dp/0470528338/)
-* [Matrix
Computations](http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan)
is the classic text for numerical linear algebra. Can't go wrong with it -
great for researchers.
+* [Matrix
Computations](http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan)
is the classic text for numerical linear algebra. Can't go wrong with it -
great for researchers.
* Nick Trefethen's [Numerical Linear
Algebra](http://people.maths.ox.ac.uk/trefethen/books.html). It's a bit more
approachable for practitioners. Many chapters on SVD, there are even chapters
on Lanczos.
### Books specifically on R:
diff --git
a/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md
b/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md
index 78ae7d6b4..4dedb44ff 100644
--- a/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md
+++ b/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md
@@ -64,7 +64,7 @@ For general background on linear algebra, statistics, and
machine learning, see
### Mahout Classic
-* [Mahout 0.10.x: first Mahout release as a programming
environment](http://www.weatheringthroughtechdays.com/2015/04/mahout-010x-first-mahout-release-as.html)
+* [Mahout 0.10.x: first Mahout release as a programming
environment](http://www.weatheringthroughtechdays.com/2015/04/mahout-010x-first-mahout-release-as.html)
* [Comparing Document Classification Functions of Lucene and
Mahout](http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html)
* [Apache Mahout: Scalable Machine Learning for
Everyone](http://www.ibm.com/developerworks/java/library/j-mahout-scaling/)
* [How to build a spam filter server with
Mahout](http://emmaespina.wordpress.com/2011/04/26/ham-spam-and-elephants-or-how-to-build-a-spam-filter-server-with-mahout/)
- Applying classification on a live server - April 2011
@@ -101,11 +101,11 @@ In reverse chronological order, so that most recent talks
are at the top.
* Distributed Machine Learning with Apache Mahout - Suneel Marthi at Apache
Big Data North America, Vancouver, Canada, May 11, 2016 and MapR Washington DC
Big Data Everywhere, Tysons, VA, June 2 2016
* [Declarative Machine Learning with the Samsara
DSL](http://www.slideshare.net/FlinkForward/sebastian-schelter-distributed-machine-learing-with-the-samsara-dsl)
Sebastian Schelter at Flink Forward Conference, Berlin Germany, October 2015.
* [Bringing Algebraic Semantics to
Mahout](http://www.slideshare.net/sscdotopen/bringing-algebraic-semantics-to-mahout)
Sebastian Schelter at HPI Infolunch, Potsdam Germany, May 2014
-* Mahout Spark and Scala bindings: Bringing Algebraic Semantics
([slides](http://www.slideshare.net/DmitriyLyubimov/mahout-scala-and-spark-bindings)/[video](http://youtu.be/h9dpmvNW1Dw))
- Dmitriy Lyubimov at Mahout Meetup, April 17, 2014.
+* Mahout Spark and Scala bindings: Bringing Algebraic Semantics
([slides](http://www.slideshare.net/DmitriyLyubimov/mahout-scala-and-spark-bindings)/[video](http://youtu.be/h9dpmvNW1Dw))
- Dmitriy Lyubimov at Mahout Meetup, April 17, 2014.
* Mahout Future Directions - Ted Dunning, Suneel Marthi, Sebastian Schelter at
Hadoop Summit Europe 2014, Amsterdam, April 3, 2014
* Building Recommender Systems for Mere-Mortals - Sebastian Schelter at
Researchgate Developer Day, Berlin, November 2013
* Recommendations with Apache Mahout - Sebastian Schelter at IBM Almaden
Research Center, San Jose, September 2013
-* [Next Directions in Mahout’s
Recommenders](http://de.slideshare.net/sscdotopen/next-directions-in-mahouts-recommenders)
- Sebastian Schelter at Bay Area Mahout Meetup, Redwood City, August 2013
+* [Next Directions in Mahout’s
Recommenders](http://de.slideshare.net/sscdotopen/next-directions-in-mahouts-recommenders)
- Sebastian Schelter at Bay Area Mahout Meetup, Redwood City, August 2013
* [New Directions in Mahout’s
Recommenders](http://de.slideshare.net/sscdotopen/new-directions-in-mahouts-recommenders)
- Sebastian Schelter at Recommender Systems Get Together Berlin, April 2013
* [Introduction to Mahout and Machine
Learning](http://www.slideshare.net/VaradMeru/introduction-to-mahout-and-machine-learning)
- Slides by Varad Meru, Software Development Engineer at Orzota. July 27th,
2013.
* [An Introduction to Collaborative Filtering with Apache
Mahout](http://de.slideshare.net/sscdotopen/introduction-to-collaborative-filtering-with-apache-mahout)
- Sebastian Schelter at Recommender Systems Challenge Workshop in conjunction
with ACM RecSys 2012, Dublin, September 2012
@@ -118,7 +118,7 @@ In reverse chronological order, so that most recent talks
are at the top.
* First Mahout Hackathon, Berlin, March 2011
* [Mahout
meetup](http://blog.jteam.nl/2011/01/13/announcement-lucene-nl-mahout-meetup-with-isabel-drost-feb-7/)
- there were two talks at the Apache Mahout meetup at JTeam in Amsterdam,
February 2011. [intro slides](http://isabel-drost.de/hadoop/slides/jteam.pdf)
* [Mahout
clustering](http://www.fosdem.org/2011/schedule/event/mahoutclustering.html) -
Talk on Mahout clustering at data dev room FOSDEM, February 2011.
-* Scaling Data Analysis with Apache Mahout - talk on Mahout at O'Reilly
Strata, February 2011.
+* Scaling Data Analysis with Apache Mahout - talk on Mahout at O'Reilly
Strata, February 2011.
* [Practical Machine
Learning](http://www.slideshare.net/jaganadhg/mahout-tutorial-fossmeet-nitc) -
Slides from Biju B and Jaganadh G, FOSSMEET-NITC, Calicut, India, February 2011.
* [Mahout at AlphaCSPs The Edge 2010
(pdf)](http://www.javaedge.com/jedge/pdf/Mahout.pdf) -
[slideshare](http://www.slideshare.net/arikogan/mahouts-presentation-at-alphacsps-the-edge-2010)
- Slides from [Ariel Kogan](http://il.linkedin.com/in/arielkogan) AlphaCSP's
The Edge, December 2010.
* [Intelligent data analysis with Apache
Mahout](http://isabel-drost.de/hadoop/slides/devoxx.pdf) - Slides from Isabel
Drost, Devoxx Antwerp, November 2010.
@@ -148,7 +148,7 @@ In reverse chronological order, so that most recent talks
are at the top.
* [BI Over Petabytes: Meet Apache
Mahout](http://cwiki.apache.org/confluence/download/attachments/88410/SDForum.pdf)
- Slides from a talk by Jeff Eastman on April 21, 2009 at the Bay Area SD
Forum Business Intelligence SIG meeting at SAP in Palo Alto, CA.
* Lucene Meetup and Apache Barcamp in Amsterdam, March 2009.
* BarCampRDU - (Raleigh) on Aug. 2, 2008
-* Introducing Mahout: Apache Machine Learning - Committer Grant Ingersoll gave
a gentle introduction to Mahout and Machine Learning at ApacheCon in November
(3rd through 7th) in New Orleans, USA.
+* Introducing Mahout: Apache Machine Learning - Committer Grant Ingersoll gave
a gentle introduction to Mahout and Machine Learning at ApacheCon in November
(3rd through 7th) in New Orleans, USA.
* Mahout: Scaling Machine Learning - Introduction to Mahout and machine
learning at FrOSCon in Sankt Augustin/Germany, Isabel Drost, August 2008.
([slides](http://cwiki.apache.org/confluence/download/attachments/88410/froscon.pdf))
* Mahout: Scalable Machine Learning - An introduction to Mahout and machine
learning at the first German Hadoop gathering in newthinking store/ Berlin,
Isabel Drost, July 2008.
* Apache Mahout: Industrial Strength Machine Learning - Committer Jeff Eastman
gave an introduction to Mahout at Yahoo!, May 2008
diff --git a/website/versioned_docs/version-0.6/learning/reference-reading.md
b/website/versioned_docs/version-0.6/learning/reference-reading.md
index 583466aeb..b20b97abd 100644
--- a/website/versioned_docs/version-0.6/learning/reference-reading.md
+++ b/website/versioned_docs/version-0.6/learning/reference-reading.md
@@ -33,7 +33,7 @@ the Mahout community. And unlike learning some particular
software tool,
these are skills that will remain useful decades later.
* [Gilbert Strang](http://www-math.mit.edu/~gs)
's [Introduction to Linear Algebra](http://math.mit.edu/linearalgebra/). His
[lectures](http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)
are also [available online](http://web.mit.edu/18.06/www/)
- and are strongly recommended.
+ and are strongly recommended.
* [Mathematical Tools for Applied Mulitvariate
Analysis](http://www.amazon.com/Mathematical-Tools-Applied-Multivariate-Analysis/dp/0121609553/ref=sr_1_1?ie=UTF8&qid=1299602805&sr=8-1)
by J.Douglass
Carroll.
* [Stanford Machine Learning online
courseware](http://www.stanford.edu/class/cs229/)
@@ -48,22 +48,22 @@ Some good introductory alternatives here are:
Once you have a grasp of the basics then there are a slew of great texts that
you might consult:
* [Statistical
Inference](http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126),
Casell and Berger, Duxbury/Thomson Learning.
-* [Introduction to Bayesian
Statistics](http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202),
William H. Bolstad, Wiley.
+* [Introduction to Bayesian
Statistics](http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202),
William H. Bolstad, Wiley.
* [Understanding Computational Bayesian
Statistics](http://www.amazon.com/Understanding-Computational-Bayesian-Statistics-Wiley/dp/0470046090),
Bolstadt
* [Bayesian Data Analysis, Gelman et
al.](http://www.stat.columbia.edu/~gelman/book/)
### For statistics related to machine learning, these are particularly helpful:
* [Pattern Recognition and Machine Learning by Chris
Bishop](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
-* [Elements of Statistical
Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) by Trevor Hastie,
Robert Tibshirani, Jerome Friedman
+* [Elements of Statistical
Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) by Trevor Hastie,
Robert Tibshirani, Jerome Friedman
*
[http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
-
+
### For matrix computations/decomposition/factorization etc.:
* Peter V. O'Neil [Introduction to Linear
Algebra](http://www.amazon.com/Introduction-Linear-Algebra-Theory-Applications/dp/053400606X),
great book for beginners (with some knowledge in calculus). It is not
comprehensive, but, it will be a good place to start and the author starts by
explaining the concepts with regards to vector spaces which I found to be a
more natural way of explaining.
* David S. Watkins [Fundamentals of Matrix
Computations](http://www.amazon.com/Fundamentals-Matrix-Computations-Applied-Mathematics/dp/0470528338/)
-* [Matrix
Computations](http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan)
is the classic text for numerical linear algebra. Can't go wrong with it -
great for researchers.
+* [Matrix
Computations](http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan)
is the classic text for numerical linear algebra. Can't go wrong with it -
great for researchers.
* Nick Trefethen's [Numerical Linear
Algebra](http://people.maths.ox.ac.uk/trefethen/books.html). It's a bit more
approachable for practitioners. Many chapters on SVD, there are even chapters
on Lanczos.
### Books specifically on R:
