Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: Random House (USA) Рейтинг: Цена: 17243.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Описание: Implement a Java application portfolio modernization strategy that saves time, eliminates risk, and maximizes benefits
Автор: Michael M?ller Название: Practical JSF in Java EE 8 ISBN: 1484230299 ISBN-13(EAN): 9781484230299 Издательство: Springer Рейтинг: Цена: 4268.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Master the Java EE 8 and JSF (JavaServer Faces) APIs and web framework with this practical, projects-driven guide to web development. This book combines theoretical background with a practical approach by building four real-world applications. By developing these JSF web applications, you'll take a tour through the other Java EE technologies such as JPA, CDI, Security, WebSockets, and more.
In Practical JSF in Java EE 8, you will learn to use the JavaServer Faces web framework in Java EE 8 to easily construct a web-based user interface from a set of reusable components. Next, you add JSF event handling and then link to a database, persist data, and add security and the other bells and whistles that the Java EE 8 platform has to offer.
After reading this book you will have a good foundation in Java-based web development and will have increased your proficiency in sophisticated Java EE 8 web development using the JSF framework.
What You Will Learn
Use the Java EE 8 and the JavaServer Faces APIs to build Java-based web applications through four practical real-world case studies
Process user input with JSF and the expression language by building a calculator application
Persist data using JSF templating and Java Persistence to manage a book store inventory
Build and manage a music library with the JSF lifecycle, BeanValidation, and more
Create and manage an alumni database and mailing list using JSF, Ajax, web services and Java EE 8's security features.
Who This Book Is For
Those new to Java EE 8 and JSF. Some prior experience with Java is recommended.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 8458.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Автор: Darren Cook Название: Practical Machine Learning with H2O ISBN: 149196460X ISBN-13(EAN): 9781491964606 Издательство: Wiley Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.
Автор: Dipanjan Sarkar; Raghav Bali; Tushar Sharma Название: Practical Machine Learning with Python ISBN: 1484232062 ISBN-13(EAN): 9781484232064 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.
Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.
Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.
Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.
Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today
What You'll Learn
Execute end-to-end machine learning projects and systems
Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
Apply a wide range of machine learning models including regression, classification, and clustering.
Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.
Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more.
This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.
Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction
Explains how to apply machine learning techniques to EEG, ECG and EMG signals
Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series
Описание: This textbook gives an in-depth discussion of basic principles and practical techniques of data mining and data warehousing. Theoretical concepts are discussed in detail with the help of practical examples. It covers data mining tools and language such as Weka and R language.
Автор: Singh, Himanshu Название: Practical machine learning and image processing ISBN: 1484241487 ISBN-13(EAN): 9781484241486 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing.
The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools.
All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application.
What You Will Learn
Discover image-processing algorithms and their applications using PythonExplore image processing using the OpenCV libraryUse TensorFlow, scikit-learn, NumPy, and other librariesWork with machine learning and deep learning algorithms for image processingApply image-processing techniques to five real-time projects
Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.
Discover The Incredible World Of Machine Learning With This Amazing Guide
Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above questions, you have come to the right place.
Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it?
Apart from this, you will also learn more about:
The Different Types Of Learning Algorithm That You Can Expect To Encounter
The Numerous Applications Of Machine Learning And Deep Learning
The Best Practices For Picking Up Neural Networks
What Are The Best Languages And Libraries To Work With
The Various Problems That You Can Solve With Machine Learning Algorithms
And much more...
Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network?
So, what are you waiting for? Grab a copy of this book now
Описание: Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above questions, you have come to the right place.
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