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Autocoaching - How to Get the Best from Yourself (Eng)

Autocoaching is a journey inside yourself. A journey aimed at getting to know yourself better and to enjoy life more with those who appreciate you

It's time to get busy. Are you ready? Are you thinking of excuses because you're
not prepared? For instance: I can't do it now because I'm busy finishing some
business; I'll take it up when I return from my holiday; I'm involved in a family affair
at ...

The Master Algorithm

How the Quest for the Ultimate Learning Machine Will Remake Our World

In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Nutrition and Brain

This volume provides a survey of the links between nutrition and the brain. It examines many of the mechanisms by which diet and individual nutrients are known to modify brain development, biochemistry and function, and evaluates current practices in the use of the diet for the prevention and treatment of disorders affecting brain function. It also highlights the need to consider issues related to brain function in the development and evolution of national policies for treating nutritional deficiencies and excesses. Written by leading investigators and clinicians, this publication will help practitioners, clinical investigators and scientists appreciate the broad opportunities awaiting investigation, and ultimately, clinical applications, in this dynamic and expanding area of investigation.

Nestec Ltd., 55 Avenue Nestl ́e, CH–1800 Vevey (Switzerland) S. Karger AG,
P.O. Box, CH–4009 Basel (Switzerland) www.karger.com 2001 Nestec Ltd.,
Vevey (Switzerland) and S. Karger AG, Basel (Switzerland). All rights reserved.

Information Systems Research and Exploring Social Artifacts: Approaches and Methodologies

Approaches and Methodologies

Centered on the impact of information and communication technology in socio-technical environments and its support of human activity systems, the study of information systems remains a distinctive focus in the area of computer science research. Information Systems Research and Exploring Social Artifacts: Approaches and Methodologies discusses the approaches and methodologies currently being used in the field on information systems. This reference source covers a wide variety of socio-technical aspects of the design of IS artifacts as well as the study of their use. This book aims to be useful for researchers, scholars and students interested in expanding their knowledge on the assortment of research on information systems.

Guo Chao Peng University of Sheffield, UK Fenio Annansingh University of
Plymouth, UK ABSTRACT Mixed-methods research, which comprises both
quantitative and qualitative components, is widely perceived as a means to
resolve the ...

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Gu, Q., Li, Z., Han, J.: Joint feature selection and subspace learning. In: IJCAI
Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p.
1294 (2011) Han, L., Zhang, Y.: Learning multi-level task groups in multi-task
learning ...