Prof. Dr. h.c. Hasso Plattner is a co-founder of SAP AG, where he served as the CEO until 2003 and has since been chairman of the supervisory board. SAP AG is today the leading provider of enterprise software solutions. In his role as chief software advisor, he concentrates on defining the mid- and long- term technology strategy and direction of SAP. Hasso Plattner received his diploma in communications engineering from the University of Karlsruhe. In recent years, he has been focusing on teaching and research in the field of business computing and software engineering at large. In 1998, he founded the Hasso-Plattner-Institute (HPI) in Potsdam, Germany. At the HPI, approximately 480 students are currently pursuing their Bachelors' and Masters' degrees in IT Systems Engineering with the help of roughly 50 professors and lecturers. The HPI currently has about 100 PhD candidates. Hasso Plattner leads one of the research groups at HPI which focuses mainly on In-Memory Data Management for Enterprise Applications and Human-Centered Software Design and Engineering.
Dr. Alexander Zeier graduated from the University of Wuerzburg in business management and successfully completed his studies in information technology at the TU Chemnitz. He worked for a few years as a strategic IT consultant, before gaining his Ph.D. in Supply Chain Management (SCM) at the University of Erlangen-Nuremberg. He has 20 years experience with IT/SAP Systems and started working for SAP in 2002 as product manager with overall responsibility for the SCM Software, SAP's first large In-Memory Application. Since 2006 he has been Deputy Chair Enterprise Platform and Integration Concepts of Prof. Hasso Plattner at the Hasso Plattner Institute in Potsdam, focusing on real-time In-Memory Enterprise Systems. During that time he has also been Executive Director for the European Section of the MIT Forum for Supply Chain Innovation. Since March 2012 Dr. Zeier has been working at the Massachusetts Institute of Technology (MIT) as Visiting Professor, lecturing and conducting research in the area of In-Memory Technology & Applications, and Supply Chain Innovation. He is the author of more than 150 journal articles and papers and has also published six books on IT and SAP.
This book is based on the first online course on the openHPI e-learning platform, which was launched in autumn 2012 with more than 13,000 learners. The book is designed for students of computer science, software engineering, and IT related subjects. However, it addresses business experts, decision makers, software developers, technology experts, and IT analysts alike. Plattner and his group focus on exploring the inner mechanics of a column-oriented dictionary-encoded in-memory database. Covered topics include - amongst others - physical data storage and access, basic database operators, compression mechanisms, and parallel join algorithms. Beyond that, implications for future enterprise applications and their development are discussed. Readers are lead to understand the radical differences and advantages of the new technology over traditional row-oriented disk-based databases.
The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
Hasso Plattner is the co-founder of SAP and the founder of the Hasso Plattner Institute, affiliated with the University of Potsdam, Germany. Bernd Leukert is a member of the SAP Executive Board and the Global Managing Board of SAP.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates