Sjardin / Boschetti / Massaron Large Scale Machine Learning with Python

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
1. Auflage 2016
ISBN: 978-1-78588-802-1
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 420 Seiten

No detailed description available for "Large Scale Machine Learning with Python".

Autoren/Hrsg.

Sjardin, Bastiaan

Boschetti, Alberto

Massaron, Luca

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - First Steps to Scalability
- Scalable Learning in Scikit Learn
- Fast learning SVM
- Neural Networks & Deep Learning
- Deep learning with Tensorflow
- CART at scale
- Unsupervised Learning at Scale
- Distributed environments: Hadoop and Spark

- Practical Machine Learning with Spark and Python

Über Autor(innen)

Sjardin Bastiaan:

Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.Boschetti Alberto:

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.