Supervised Machine Learning, Kolosova, Tatiana , Berestizhevsky, Samuel
Автор: Sundaram Suresh; Narasimhan Sundararajan; Ramasamy Название: Supervised Learning with Complex-valued Neural Networks ISBN: 3642426794 ISBN-13(EAN): 9783642426797 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A new generation of neural networks is needed in telecommunications, medical imaging and signal processing as signals become more complex and nonlinear. This survey of the latest complex-valued networks includes learning algorithms and new architectures.
Автор: Jurney Russell Название: Weakly Supervised Learning: Doing More with Less Data ISBN: 1492077062 ISBN-13(EAN): 9781492077060 Издательство: Wiley Рейтинг: Цена: 11403.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive. There`s a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.
Описание: The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system.
Автор: Dr. Dhoot, Dhoot Название: Supervised machine learning for kids (tinker toddlers) ISBN: 1950491072 ISBN-13(EAN): 9781950491070 Издательство: Неизвестно Рейтинг: Цена: 3492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Tinker Toddlers is a series designed to introduce first nonfiction emerging STEM concepts to babies, toddlers, and preschoolers. Dion is not an ordinary machine. He has a superpower - he can learn. And Aria knows exactly what to teach him. Follow along as she teaches Dion all about cats and dogs.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems
Key Features
Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
Master the art of data-driven problem-solving with hands-on examples
Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms
Book Description
Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
What you will learn
Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithm's hyper parameters
Discover how to use neural networks for classification and regression
Build, evaluate, and deploy your machine learning solutions to production
Who this book is for
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Автор: Vendan S. Arungalai, Kamal Rajeev, Karan Abhinav Название: Welding and Cutting Case Studies with Supervised Machine Learning ISBN: 9811393818 ISBN-13(EAN): 9789811393815 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes.
Описание: The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system.
Автор: Sebasti?n Ventura; Jos? Mar?a Luna Название: Supervised Descriptive Pattern Mining ISBN: 3030074560 ISBN-13(EAN): 9783030074562 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those based on heuristics. It provides some formal definitions and a general idea about patterns, pattern mining, the usefulness of patterns in the knowledge discovery process, as well as a brief summary on the tasks related to supervised descriptive pattern mining. It also includes a detailed description on the tasks usually grouped under the term supervised descriptive pattern mining: subgroups discovery, contrast sets and emerging patterns. Additionally, this book includes two tasks, class association rules and exceptional models, that are also considered within this field.A major feature of this book is that it provides a general overview (formal definitions and algorithms) of all the tasks included under the term supervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies for any of these tasks. This book also illustrates how important these techniques are in different fields, a set of real-world applications are described.Last but not least, some related tasks are also considered and analyzed. The final aim of this book is to provide a general review of the supervised descriptive pattern mining field, describing its tasks, its algorithms, its applications, and related tasks (those that share some common features).This book targets developers, engineers and computer scientists aiming to apply classic and heuristic-based algorithms to solve different kinds of pattern mining problems and apply them to real issues. Students and researchers working in this field, can use this comprehensive book (which includes its methods and tools) as a secondary textbook.
Автор: Michael W. Berry; Azlinah Mohamed; Bee Wah Yap Название: Supervised and Unsupervised Learning for Data Science ISBN: 3030224740 ISBN-13(EAN): 9783030224745 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018).Includes new advances in clustering and classification using semi-supervised and unsupervised learning;Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
Автор: Ramasubramanian Karthik, Moolayil Jojo Название: Applied Supervised Learning with R ISBN: 1838556338 ISBN-13(EAN): 9781838556334 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.
Автор: Maria Schuld; Francesco Petruccione Название: Supervised Learning with Quantum Computers ISBN: 303007188X ISBN-13(EAN): 9783030071882 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Автор: Bateman Blaine, Jha Ashish Ranjan, Johnston Benjamin Название: The Supervised Learning Workshop, Second Edition ISBN: 1800209045 ISBN-13(EAN): 9781800209046 Издательство: Неизвестно Рейтинг: Цена: 7171.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms
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