On: Introduction to Machine Learning for Developers

[mi100hael]

Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?

[esfandia → mi100hael]

Weka is a popular machine learning toolkit in Java: http://www.cs.waikato.ac.nz/ml/weka/

and they have a textbook to go with it: http://www.cs.waikato.ac.nz/ml/weka/book.html

as well as an online course: https://weka.waikato.ac.nz/dataminingwithweka/preview

[agibsonccc → mi100hael]

Hi, skymind cofounder here. To offer some context on our book there: We have appendixes covering some of the fundamental concepts such as linear algebra and statistics.

For other machine learning libraries in java:

https://github.com/haifengl/smile

http://knime.org

http://rapidminer.com

[marcinzm → mi100hael]

If you're interested in the big data side of things there's Spark (http://spark.apache.org/) and MLlib for it (http://spark.apache.org/docs/latest/ml-guide.html). H20 (http://www.h2o.ai/) also provides ML algorithms on top of Spark (and I think independent of Spark as well, not sure of the current status). These are all written on the JVM either in Scala (Spark) or Java (H20).

[otoburb → mi100hael]

The Skymind.io co-founders wrote a book that references their open-source "deep learning for Java and Scala framework"[1]. They are in the YC16 batch.

[1] https://deeplearning4j.org/about

[adamnemecek → mi100hael]

I haven't read it but this book looks reasonable https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...