Fundamentals of Machine Learning and Deep Learning in Medicine, Borhani
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: Random House (USA) Рейтинг: Цена: 13794.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Описание: This updated second edition describes the intuition behind deep learning innovations without jargon or complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own.
Автор: Singh, Pardeep Название: Fundamentals and methods of machine and deep learning ISBN: 1119821258 ISBN-13(EAN): 9781119821250 Издательство: Wiley Рейтинг: Цена: 29613.00 р. Наличие на складе: Поставка под заказ.
Описание: Aimed at obstetric and pediatric anesthesiologists, this practical book explores the different defects treated during pregnancy, providing the knowledge and clinical pearls to care for mother and fetus during these procedures. It covers the nuances of the diagnoses, pathophysiology and anesthetic management of patients presenting for fetal surgery.
Автор: Jiang Hui Название: Machine Learning Fundamentals: A Concise Introduction ISBN: 1108837042 ISBN-13(EAN): 9781108837040 Издательство: Cambridge University Press Рейтинг: Цена: 22864.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used traditional methods plus recently popular deep learning methods.
Автор: Yao Название: Nanophotonics and Machine Learning ISBN: 3031204727 ISBN-13(EAN): 9783031204722 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, the first of its kind, bridges the gap between the increasingly interlinked fields of nanophotonics and artificial intelligence (AI). While artificial intelligence techniques, machine learning in particular, have revolutionized many different areas of scientific research, nanophotonics holds a special position as it simultaneously benefits from AI-assisted device design whilst providing novel computing platforms for AI. This book is aimed at both researchers in nanophotonics who want to utilize AI techniques and researchers in the computing community in search of new photonics-based hardware. The book guides the reader through the general concepts and specific topics of relevance from both nanophotonics and AI, including optical antennas, metamaterials, metasurfaces, and other photonic devices on the one hand, and different machine learning paradigms and deep learning algorithms on the other. It goes on to comprehensively survey inverse techniques for device design, AI-enabled applications in nanophotonics, and nanophotonic platforms for AI. This book will be essential reading for graduate students, academic researchers, and industry professionals from either side of this fast-developing, interdisciplinary field.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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: 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: Hui Jiang Название: Machine Learning Fundamentals: A Concise Introduction ISBN: 1108940021 ISBN-13(EAN): 9781108940023 Издательство: Cambridge Academ Рейтинг: Цена: 7128.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used traditional methods plus recently popular deep learning methods.
Автор: Trappenberg Thomas Название: Fundamentals of Machine Learning ISBN: 0198828047 ISBN-13(EAN): 9780198828044 Издательство: Oxford Academ Рейтинг: Цена: 7781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Interest in machine learning is exploding across the world, both in research and for industrial applications. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to both students and researchers.
Автор: Jin Название: Federated Learning ISBN: 9811970823 ISBN-13(EAN): 9789811970825 Издательство: Springer Рейтинг: Цена: 19514.00 р. Наличие на складе: Нет в наличии.
Описание: This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.
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