The.Hottest

Monday, February 26, 2018 , , ,
Giuseppe C. Calafiore,‎ Laurent El Ghaoui ... 650 pages - Publisher: Cambridge Univ. Press; 1st edition (October, 2014) ... Language: English ... ISBN-10: 1107050871 - ISBN-13: 978-1107050877 ...

Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data science, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.
Read

Sunday, February 25, 2018 , , ,
Luis Rabelo,‎ Sayli Bhide,‎ Edgar Gutierrez ... 350 pages - Publisher: Nova Science; (February, 2018) ... Language: English - ISBN-10: 1536126772 - ISBN-13: 978-1536126778.

After decades of basic research and more promises than impressive applications, artificial intelligence (AI) is starting to deliver benefits. Organizations around the world have been realizing that there are substantial performance gains and increases in productivity for the use of AI and predictive analytics techniques. Their use is bringing a new era of breakthrough innovation and opportunities. This book, compiles research insights and applications in diverse areas such as manufacturing, supply chain management, pricing, autonomous vehicles, healthcare, ecommerce, and aeronautics. Using classical and advanced tools in AI such as deep learning, particle swarm optimization, support vector machines and genetic programming among others. This is a very distinctive book which discusses important applications using a variety of paradigms from AI and outlines some of the research to be performed. The work supersedes similar books that do not cover as diversified a set of sophisticated applications. The authors present a comprehensive and articulated view of recent developments, identifies the applications gap by quoting from the experience of experts, and details suggested research areas. Artificial Intelligence: Advances in Research and Applications guides the reader through an intuitive understanding of the methodologies and tools for building and modeling intelligent systems. The book's coverage is broad, starting with clustering techniques with unsupervised ensemble learning, where the optimal combination strategy of individual partitions is robust in comparison to the selection of an algorithmic clustering pool. The book discusses how the use of AI can allow for productivity development and other benefits not just for businesses, but also for economies. Finally, you can find an interesting investigation of the "transhuman" dimension of AI. (Nova)
Read

Sunday, February 25, 2018 , , ,
Josh Patterson, Adam Gibson ... 532 pages - Publisher: O'Reilly Media; 1st edition (August, 2017) - Language: English - ISBN-10: 1491914254 - ISBN-13: 978-1491914250 ...

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular + Understand how deep networks evolved from neural network fundamentals + Explore the major deep network architectures, including Convolutional and Recurrent + Learn how to map specific deep networks to the right problem + Walk through the fundamentals of tuning general neural networks and specific deep network architectures + Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool + Learn how to use DL4J natively on Spark and Hadoop.
Read
Subscribe to: Posts (Atom)
Geo.Engineerings © since June 10, 2015.
Believe in Power of ENGINEERINGS.

Contact Form

Name

Email *

Message *

Powered by Blogger.