Автор: Goutam Kumar Bose, Pritam Pain Название: Machine Learning Applications in Non-Conventional Machining Processes ISBN: 179983624X ISBN-13(EAN): 9781799836247 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31046.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Traditional machining has many limitations in today's technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking.
Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today's technology-driven market.
Автор: Martin Held Название: On the Computational Geometry of Pocket Machining ISBN: 3540541039 ISBN-13(EAN): 9783540541035 Издательство: Springer Рейтинг: Цена: 8537.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this monograph the author presents a thorough computational geometry approach to handling theoretical and practical problems arising from numerically controlled pocket machining. Topics of practical importance that are dealt with include the selection of tool sizes, the determination of tool paths, and the optimization of tool paths.
Описание: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.
Автор: Deka, Paresh Chandra, Название: A primer on machine learning applications in civil engineering / ISBN: 113832339X ISBN-13(EAN): 9781138323391 Издательство: Taylor&Francis Рейтинг: Цена: 16078.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a broad overview of the available machine learning techniques for solving civil engineering problems including drought forecasting, river flow forecasting, precipitation forecasting, and significant wave height forecasting. Fundamentals of both theoretical and practical aspects are discussed in varied domains.
Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book--Amazon, Microsoft, Google, and PythonAnywhere.
You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time.
Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.
What You'll Learn
Extend your machine learning models using simple techniques to create compelling and interactive web dashboards
Leverage the Flask web framework for rapid prototyping of your Python models and ideas
Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more
Harness the power of TensorFlow by exporting saved models into web applications
Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content
Create dashboards with paywalls to offer subscription-based access
Access API data such as Google Maps, OpenWeather, etc.
Apply different approaches to make sense of text data and return customized intelligence
Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back
Utilize the freemium offerings of Google Analytics and analyze the results
Take your ideas all the way to your customer's plate using the top serverless cloud providers
Who This Book Is For
Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.
Описание: Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
Автор: David Paper Название: Hands-on Scikit-Learn for Machine Learning Applications ISBN: 1484253728 ISBN-13(EAN): 9781484253724 Издательство: Springer Рейтинг: Цена: 6707.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Intermediate user level
Автор: Mahrishi Mehul, Hiran Kamal Kant, Meena Gaurav Название: Machine Learning and Deep Learning in Real-Time Applications ISBN: 1799830969 ISBN-13(EAN): 9781799830962 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 23199.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science.
Описание: Highlights the marriage between business intelligence and knowledge management through the use of agile methodologies. Through its fifteen chapters, this offers perspectives on the integration between process modeling, agile methodologies, business intelligence, knowledge management, and strategic management.
Автор: Georgios Paliouras; Vangelis Karkaletsis; Constant Название: Machine Learning and Its Applications ISBN: 3540424903 ISBN-13(EAN): 9783540424901 Издательство: Springer Рейтинг: Цена: 6458.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Examining the capabilities of machine learning methods and ideas on how they apply to real-world problems, this text assesses machine learning, then introduces applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, and user modelling.
Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.
The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
Описание: In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries.
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