Practical Machine Learning For Data Analysis Using Python, Subasi, Abdulhamit
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 66520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Darren Cook Название: Practical Machine Learning with H2O ISBN: 149196460X ISBN-13(EAN): 9781491964606 Издательство: Wiley Рейтинг: Цена: 42230.00 T 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: Berrar Daniel P., Dubitzky Werner, Granzow Martin Название: A Practical Approach to Microarray Data Analysis ISBN: 1402072600 ISBN-13(EAN): 9781402072604 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть Описание: A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science. Key topics covered include: -Format of result from data analysis, analytical modeling/experimentation; -Validation of analytical results; -Data analysis/Modeling task; -Analysis/modeling tools; -Scientific questions, goals, and tasks; -Application; -Data analysis methods; -Criteria for assessing analysis methodologies, models, and tools.
Write algorithms and program in the new field of quantum computing. This book covers major topics such as the physical components of a quantum computer: qubits, entanglement, logic gates, circuits, and how they differ from a traditional computer. Also, Practical Quantum Computing for Developers discusses quantum computing in the cloud using IBM Q Experience including: the composer, quantum scores, experiments, circuits, simulators, real quantum devices, and more. You’ll be able to run experiments in the cloud on a real quantum device.
Furthermore, this book shows you how to do quantum programming using the QISKit (Quantum Information Software Kit), Python SDK, and other APIs such as QASM (Quantum Assembly). You’ll learn to write code using these languages and execute it against simulators (local or remote) or a real quantum computer provided by IBM’s Q Experience. Finally, you’ll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others. You’ll peak inside the inner workings of the Bell states for entanglement, Grover’s algorithm for linear search, Shor’s algorithm for integer factorization, and other algorithms in the fields of optimization, and more.
Along the way you’ll also cover game theory with the Magic Square, an example of quantum pseudo-telepathy where parties sharing entangled states can be observed to have some kind of communication between them. In this game Alice and Bob play against a referee. Quantum mechanics allows Alice and Bob to always win!
By the end of this book, you will understand how this emerging technology provides massive parallelism and significant computational speedups over classical computers, and will be prepared to program quantum computers which are expected to replace traditional computers in the data center.
What You Will Learn
Use the Q Experience Composer, the first-of-its-kind web console to create visual programs/experiments and submit them to a quantum simulator or real device on the cloudRun programs remotely using the Q Experience REST API Write algorithms that provide superior performance over their classical counterpartsBuild a Node.js REST client for authenticating, listing remote devices, querying information about quantum processors, and listing or running experiments remotely in the cloudCreate a quantum number generator: The quintessential coin flip with a quantum twistDiscover quantum teleportation: This algorithm demonstrates how the exact state of a qubit (quantum information) can be transmitted from one location to another, with the help of classical communication and quantum entanglement between the sender and receiverPeek into single qubit operations with the classic game of Battleships with a quantum twistHandle the counterfeit coin problem: a classic puzzle that consists of finding a counterfeit coin in a beam balance among eight coins in only two turns
Who This Book Is For
Developers and programmers interested in this new field of computing.
Автор: Koul Anirudh, Ganju Siddha, Kasam Meher Название: Practical Deep Learning for Cloud and Mobile: Hands-On Computer Vision Projects Using Python, Keras & Tensorflow ISBN: 149203486X ISBN-13(EAN): 9781492034865 Издательство: Wiley Рейтинг: Цена: 76020.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach.
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
Автор: Ian H. Witten Название: Data Mining: Practical Machine Learning Tools and Techniques, ISBN: 0123748569 ISBN-13(EAN): 9780123748560 Издательство: Elsevier Science Рейтинг: Цена: 57970.00 T Наличие на складе: Поставка под заказ. Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Автор: Steven L. Brunton, J. Nathan Kutz Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control ISBN: 1108422098 ISBN-13(EAN): 9781108422093 Издательство: Amazon Internet Рейтинг: Цена: 0.00 T Наличие на складе: Невозможна поставка. Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.
Автор: Simovici, Dan A (univ Of Massachusetts At Boston, Usa) Название: Mathematical analysis for machine learning and data mining ISBN: 9813229683 ISBN-13(EAN): 9789813229686 Издательство: World Scientific Publishing Рейтинг: Цена: 343200.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The mathematical analysis component of the typical mathematical curriculum for computer science students omits these very important ideas and techniques which are indispensable for approaching specialized area of machine learning centered around optimization such as support vector machines, neural networks, various types of regression, feature selection, and clustering. The book is of special interest to researchers and graduate students who will benefit from these application areas discussed in the book. Related Link(s)
Автор: Amaral Turkman Maria Antуnia Название: Institute of Mathematical Statistics Textbooks ISBN: 1108703747 ISBN-13(EAN): 9781108703741 Издательство: Cambridge Academ Рейтинг: Цена: 40130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user`s guide for researchers and graduate students from beyond statistics.
Автор: Wainwright Martin J Название: Cambridge Series in Statistical and Probabilistic Mathematic ISBN: 1108498027 ISBN-13(EAN): 9781108498029 Издательство: Cambridge Academ Рейтинг: Цена: 71810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.
Автор: Bouveyron, Charles Celeux, Gilles Murphy, T. Brendan (university College Dublin) Raftery, Adrian E. (university Of Washington) Название: Cambridge series in statistical and probabilistic mathematics ISBN: 110849420X ISBN-13(EAN): 9781108494205 Издательство: Cambridge Academ Рейтинг: Цена: 77090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.
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