Haskell Data Analysis Cookbook: Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

Nishant Shukla

ISBN 10: 1783286334 / ISBN 13: 9781783286331

Editore: Packt Publishing, 2014

Riguardo questo articolo

A tight, bright, and clean copy with no inscriptions and no annotations/notes. No creasing to spine/cover or foxing to pages. Excellent condition book. 315pp. (19x23.5cm). Please contact us for any more information.

Codice articolo 8764

Dati bibliografici

• Titolo: Haskell Data Analysis Cookbook: Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes
• Editore: Packt Publishing
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1783286334
• ISBN 13: 9781783286331
• Rilegatura: Soft cover
• Edizione: 1st Edition
• Condizione: Fine
• Cataloghi dei venditori:
  • Computing
  • Technology

Descrizione articolo

Riassunto

Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.

L'autore

Nishant Shukla

Nishant Shukla is a computer scientist with a passion for mathematics. Throughout the years, he has worked for a handful of start-ups and large corporations including WillowTree Apps, Microsoft, Facebook, and Foursquare. Stepping into the world of Haskell was his excuse for better understanding Category Theory at first, but eventually, he found himself immersed in the language. His semester-long introductory Haskell course in the engineering school at the University of Virginia (http://shuklan.com/haskell) has been accessed by individuals from over 154 countries around the world, gathering over 45,000 unique visitors. Besides Haskell, he is a proponent of decentralized Internet and open source software. His academic research in the fields of Machine Learning, Neural Networks, and Computer Vision aim to supply a fundamental contribution to the world of computing.