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Hyperparameter tuning for machine and deep learning with R : A practical guide
Equips readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms.
High Performance SQL Server " Consistent Response for Mission-Critical Applications
An excellent complement to query performance tuning books and provides the other half of what you need to know by focusing on configuring the instances on which mission-critical queries are executed. You will: Understand SQL Server's database engine and how it processes queries / Configure instances in support of high-throughput applications / Provide consistent response times to varying user numbers and query volumes / Design databases for high-throughput applications with focus on performance / Record performance baselines and monitor SQL Server instances against them / Troubleshot and fix performance problems
From brows to trust : Evaluating embodied conversational agents
This book will help experts and designers in the specification and development of applications incorporating ECAs. Part 1 provides guidelines for evaluation methodologies and the identification of design and evaluation parameters. Part 2 demonstrates the importance of considering the user's perspective and interaction experience. Part 3 addresses issues in fine-tuning design parameters of ECAs and verifying the perceived effect. Finally, in Part 4 lessons learned from a number of application case studies are presented. The book is intended for both ECA researchers in academia and industry, and developers and designers interested in applying the technology.
Formal Modeling and Analysis of Timed Systems ; 6th International Conference, FORMATS 2008, Saint Malo, France, September 15-17, 2008. Proceedings
This book constitutes the refereed proceedings of the 6th International Conference on Formal Modeling and Analysis of Timed Systems, FORMATS 2008, held in Saint Malo, France, September 2008.The 17 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on extensions of timed automata and semantics; timed games and logic; case studies; model-checking of probabilistic systems; verification and test; timed petri nets.
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics ; International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings
Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-specific background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, artificial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.
Digital self-tuning controllers : Algorithms, implementation and applications
Digital Self-tuning Controllers presents you with a complete course in self-tuning control, beginning with a survey of adaptive control and the formulation of adaptive control problems. Modelling and identification are dealt with before passing on to algebraic design methods and particular PID and linear-quadratic forms of self-tuning control. Finally, laboratory verification and experimentation will show you how to ground your theoretical knowledge in real plant control.
Designing virtual reality systems : The structured approach
Virtual Reality (VR) is a field of study that aims to create a system that provides a synthetic experience for its users. Developing and maintaining a VR system is a very difficult task, requiring in-depth knowledge in many different disciplines, such as sensing and tracking technologies, stereoscopic displays, multimodal interaction and processing, computer graphics and geometric modeling, dynamics and physical simulation, performance tuning, etc. The difficulty lies in the complexity of having to simultaneously consider many system goals, some of which are conflicting.
Database performance at scale: a practical guide
Optimizing database performance at the scale required for today’s data-intensive applications often requires more than performance tuning and scaling out. This book shares commonly overlooked considerations, pitfalls, and opportunities that have helped many teams break through database performance plateaus. It’s neither a definitive guide to distributed databases nor a beginner’s resource. Rather, it’s a look at the many different factors that impact performance, and our top field-tested recommendations for navigating them. Chapter 1 provides two (fun and fanciful) tales that surface some of the many roadblocks you might face and highlight the range of strategies for navigating around them.
Computer Vision Metrics : Survey, Taxonomy, and Analysis
Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more.
Beginning ASP.NET 2.0 in C# 2005 : From novice to professional
This book provides thorough coverage of ASP.NET, guiding you from beginning to advanced techniques, such as querying databases from within a web page and performance-tuning your site. You'll find tips for best practices and comprehensive discussions of key database and XML principles. The book also emphasizes the invaluable coding techniques of object orientation and code-behind, which will enable you to build real-world websites instead of just scraping by with simplified coding practices. By the time you finish this book, you will have mastered the core techniques essential to professional ASP.NET developers.
Autotuning of PID Controllers : A Relay Feedback Approach
Recognising the benefits of improved control, the second edition of Autotuning of PID Controllers provides simple yet effective methods for improving PID controller performance. The practical issues of controller tuning are examined using numerous worked examples and case studies in association with specially written autotuning MATLAB® programs to bridge the gap between conventional tuning practice and novel autotuning methods. Autotuning of PID Controllers is more than just a monograph, it is an independent learning tool applicable to the work of academic control engineers and of their counterparts in industry looking for more effective process control and automation.
Automated machine learning : Methods, systems, challenges
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.
Applied Deep Learning with TensorFlow 2 : Learn to Implement Advanced Deep Learning Techniques with Python
Focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: Understand the fundamental concepts of how neural networks work / Learn the fundamental ideas behind autoencoders and generative adversarial networks / Be able to try all the examples with complete code examples that you can expand for your own projects / Have available a complete online companion book with examples and tutorials.
Advanced technique and future perspective for next generation optical fiber communications
Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology.
Advanced data mining and applications ; Vol. 4093 : 2nd International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China in August 2006. The 41 revised full papers and 74 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 515 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
