HACARUS Publishes “An Introduction to Sparse Modeling for IT Engineers”

HACARUS Publishes “An Introduction To Sparse Modeling For IT Engineers”

From understanding the basics to real world applications, Sparse Modeling becomes accessible to all engineers in Japan with this introductory book.

September 9, 2021 – Kyoto, Japan – HACARUS, the leading provider of big insights from small data today announced the release of the book An Introduction to Sparse Modeling for IT Engineers. Written by senior data scientists at HACARUS, the new publication offers the perfect starting point for anyone interested in the fundamentals of Sparse Modeling with easy-to-understand use case examples and python code.

An Introduction to Sparse Modeling for IT Engineers allows readers to learn from a wide range of applications of Sparse Modeling and implement it for practical data analysis cases.”
– Professor Kaoru Kawamoto, School of Data Science at Shiga University

“This book is written in a modern style that focuses on ideas and implementation methods that are important in the real world. With the rapid advancement of AI technology, this publication is certainly a ‘must-have’ for all engineers.”
– Professor Masayuki Ohzeki, Graduate School of Information Science at Tohoku University

An Introduction to Sparse Modeling for IT Engineers is currently available in paperback or digital in Japanese. You can also request a copy of the first 2 chapters of the book in English by contacting pr@hacarus.com.

 

About HACARUS

HACARUS INC. provides big insights from small data and has since its founding in 2014 supplied solutions in 100+ AI projects across the Medical and Manufacturing fields. Headquartered in Kyoto, Japan and backed by Osaka Gas and Miyako Capital (Kyoto University) among others, its technology enables humans to make better, faster, and more reliable decisions, based on data-driven insights. HACARUS’ proprietary AI engine is built using Sparse Modeling, a method that understands data like a human would – by its unique key features and is far more resource, time, and energy-efficient when compared to Deep Learning. To learn more, visit https://hacarus.com

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