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: 42230.00 T 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: MIT Press Рейтинг: Цена: 90290.00 T 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.
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.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Автор: Misra, Siddharth Название: Machine Learning for Subsurface Characterization ISBN: 0128177365 ISBN-13(EAN): 9780128177365 Издательство: Elsevier Science Рейтинг: Цена: 123520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
To continue to meet demand while keeping costs down, petroleum and reservoir engineers know it is critical to utilize their asset's data through more complex modeling methods, and machine learning and data analytics is the known alternative approach to accurately represent the complexity of fluid-filled rocks. With a lack of training resources available, Machine Learning for Subsurface Characterization focuses on the development and application of neural networks, deep learning, unsupervised learning, reinforcement learning, and clustering methods for subsurface characterization under constraints. Such constraints are encountered during subsurface engineering operations due to financial, operational, regulatory, risk, technological, and environmental challenges.
This reference teaches how to do more with less. Used to develop tools and techniques of data-driven predictive modelling and machine learning for subsurface engineering and science, engineers will be introduced to methods of generating subsurface signals and analyzing the complex relationships within various subsurface signals using machine learning. Algorithmic procedures in MATLAB, R, PYTHON, and TENSORFLOW are displayed in text and through online instructional video to assist training and learning. Field cases are also presented to understand real-world applications, with a particular focus on examples involving shale reservoirs.
Explaining the concept of machine learning, advantages to the industry, and applications applied to complex subsurface rocks, Machine Learning for Subsurface Characterization delivers a missing piece to the reservoir engineer's toolbox needed to support today's complex operations.
Focus on applying predictive modelling and machine learning from real case studies and Q&A sessions at the end of each chapter
Learn how to develop codes such as MATLAB, PYTHON, R, and TENSORFLOW with step-by-step guides included
Visually learn code development with video demonstrations included
Автор: Singh, Pardeep Название: Fundamentals and methods of machine and deep learning ISBN: 1119821258 ISBN-13(EAN): 9781119821250 Издательство: Wiley Рейтинг: Цена: 197420.00 T 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: Trappenberg Thomas Название: Fundamentals of Machine Learning ISBN: 0198828047 ISBN-13(EAN): 9780198828044 Издательство: Oxford Academ Рейтинг: Цена: 43810.00 T 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: Buduma Nithin, Buduma Nikhil Название: Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, 2 ed. ISBN: 149208218X ISBN-13(EAN): 9781492082187 Издательство: Wiley Рейтинг: Цена: 67570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Jiang Hui Название: Machine Learning Fundamentals: A Concise Introduction ISBN: 1108837042 ISBN-13(EAN): 9781108837040 Издательство: Cambridge University Press Рейтинг: Цена: 144610.00 T 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: 111790.00 T 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: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Intermediate user level
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