Weakly Supervised Learning: Doing More with Less Data, Jurney Russell
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
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: 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.
Автор: Fr?d?ric Ros; Serge Guillaume Название: Sampling Techniques for Supervised or Unsupervised Tasks ISBN: 3030293483 ISBN-13(EAN): 9783030293482 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the ?eld and discusses the state of the art concerning sampling techniques for supervised and unsupervised task.Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks;Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality;Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. 'This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge.'M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas
'In science the difficulty is not to have ideas, but it is to make them work'From Carlo Rovelli
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.
Описание: Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you'll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn
Employ deep learning using C++ and CUDA C
Work with supervised feedforward networks
Implement restricted Boltzmann machines
Use generative samplings
Discover why these are important
Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
Автор: Kolosova, Tatiana , Berestizhevsky, Samuel Название: Supervised Machine Learning ISBN: 0367277328 ISBN-13(EAN): 9780367277321 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing data sets, design and analysis of statistical experiments and optimal hyper-parameters for ML methods.
Описание: Expanding upon presentations at last year`s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.
Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.
Автор: Sebasti?n Ventura; Jos? Mar?a Luna Название: Supervised Descriptive Pattern Mining ISBN: 3319981390 ISBN-13(EAN): 9783319981390 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.
Описание: Chapter 1: Introduction to Supervised LearningChapter Goal: Start the journey of the readers on supervised learning No of pages: 30-40 Sub -Topics 1. Machine learning and how is it different from software engineering? 2. Discuss reasons for machine learning being popular 3. Compare between supervised, semi-supervised and unsupervised algorithms 4. Statistical methods to get significant variables 5. The use cases of machine learning and respective use cases for each of supervised, semi-supervised and unsupervised algorithms Chapter 2: Supervised Learning for Regression AnalysisChapter Goal: Embrace the core concepts of supervised learning to predict continuous variables No of pages: 40-50 Sub - Topics 1. Supervised learning algorithms for predicting continuous variables 2. Explain mathematics behind the algorithms 3. Develop Python solution using linear regression, decision tree, random forest, SVM and neural network 4. Measure the performance of the algorithms using r square, RMSE etc. 5. Compare and contrast the performance of all the algorithms 6. Discuss the best practices and the common issues faced like data cleaning, null values etc. Chapter 3: Supervised Learning for Classification ProblemsChapter Goal: Discuss the concepts of supervised learning for solving classification problems No of pages: 30-40 Sub - Topics: 1. Discuss classification problems for supervised learning 2. Examine logistic regression, decision tree, random forest, knn and naпve Bayes. Understand the statistics and mathematics behind each 3. Discuss ROC curve, akike value, confusion matrix, precision/recall etc 4. Compare the performance of all the algorithms 5. Discuss the tips and tricks, best practices and common pitfalls like a bias-variance tradeoff, data imbalance etc. Chapter 4: Supervised Learning for Classification Problems-Advanced Chapter Goal: cover advanced classification algorithms for supervised learning algorithms No of pages:30-40 Sub - Topics: 1. Refresh classification problems for supervised learning 2. Examine gradient boosting and extreme gradient boosting, support vector machine and neural network 3. Compare the performance of all the algorithms 4. Discuss the best practices and common pitfalls, tips and tricks Chapter 5: End-to-End Model DeploymentChapter Goal: guide the reader on the end-to-end process of deploying a supervised learning model in production No of pages:25-30 1. Meaning of model deployment 2. Various steps in the model deployment process 3. Preparations to be made like settings, environment etc. 4. Various use cases in the deployment 5. Practical tips in model deployment
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.
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