AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond, Zacharias Voulgaris, Yunus Bulut
Автор: Hurwitz, Kaufman Marcia, Bowles Adrian Название: Cognitive Computing: Implementing Big Data Machine Learning Solutions ISBN: 1118896629 ISBN-13(EAN): 9781118896624 Издательство: Wiley Рейтинг: Цена: 40120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.
Автор: Krohn Jon, Grant Beyleveld, Aglae Bassens Название: Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence ISBN: 0135116694 ISBN-13(EAN): 9780135116692 Издательство: Pearson Education Рейтинг: Цена: 58070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience.
Part I's high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible and is illuminated with hands-on Python code. Theory is supported with practical "run-throughs" available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming. To help readers accomplish more in less time, the authors feature several of today's most widely used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch; and the recently released, high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms.
Автор: Giuseppe Iurato Название: Computational Psychoanalysis and Formal Bi-Logic Frameworks ISBN: 1522541284 ISBN-13(EAN): 9781522541288 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 208830.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Computational psychoanalysis is a new field stemming from Freudian psychoanalysis. The new area aims to understand the primary formal structures and running mechanisms of the unconscious while implementing them into computer sciences. Computational Psychoanalysis and Formal Bi-Logic Frameworks provides emerging information on this new field which uses psychoanalysis and the unconscious mind to make advancements in computational research. While highlighting the challenges of applying analytical logic trends to primary formal structures, readers will learn the valuable outputs to society when these trends are successfully implemented. This book is an important resource for computer scientists, researchers, academics, and other professionals seeking current research on applying psychoanalysis and Freudian concepts to computational structures.
Автор: Li, Fanzhang / Zhang, Li / Zhang, Zhao Название: Dynamic Fuzzy Machine Learning ISBN: 3110518708 ISBN-13(EAN): 9783110518702 Издательство: Walter de Gruyter Рейтинг: Цена: 149590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.
Автор: Kristin J. Dana Название: Computational Texture and Patterns: From Textons to Deep Learning ISBN: 1681730111 ISBN-13(EAN): 9781681730110 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 51750.00 T Наличие на складе: Невозможна поставка. Описание: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.
Автор: Michael Stanley, Jongmin Lee Название: Sensor Analysis for the Internet of Things ISBN: 1681732890 ISBN-13(EAN): 9781681732893 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 82230.00 T Наличие на складе: Невозможна поставка. Описание: While it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals.Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types.We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.
Автор: Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks Название: Deep Learning for Computer Architects ISBN: 168173219X ISBN-13(EAN): 9781681732190 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 76690.00 T Наличие на складе: Невозможна поставка. Описание: A primer for computer architects in a new and rapidly evolving field. The authors review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that have emerged in the last decade.
Автор: Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis Название: Data Exploration Using Example-Based Methods ISBN: 1681734559 ISBN-13(EAN): 9781681734552 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66530.00 T Наличие на складе: Невозможна поставка. Описание: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Автор: Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis Название: Data Exploration Using Example-Based Methods ISBN: 1681734575 ISBN-13(EAN): 9781681734576 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 87780.00 T Наличие на складе: Невозможна поставка. Описание: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Автор: Moises Rivas-Lopez, Oleg Sergiyenko, Wendy Flores-Fuentes, Julio Cesar Rodriguez-Quinonez Название: Optoelectronics in Machine Vision-Based Theories and Applications ISBN: 1522557512 ISBN-13(EAN): 9781522557517 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 188100.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Sensor technologies play a large part in modern life, as they are present in things like security systems, digital cameras, smartphones, and motion sensors. While these devices are always evolving, research is being done to further develop this technology to help detect and analyze threats, perform in-depth inspections, and perform tracking services.Optoelectronics in Machine Vision-Based Theories and Applications provides innovative insights on theories and applications of optoelectronics in machine vision-based systems. It also covers topics such as applications of unmanned aerial vehicle, autonomous and mobile robots, medical scanning, industrial applications, agriculture, and structural health monitoring. This publication is a vital reference source for engineers, technology developers, academicians, researchers, and advanced-level students seeking emerging research on sensor technologies and machine vision.
Автор: Riguzzi Fabrizio Название: Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning ISBN: 8770220182 ISBN-13(EAN): 9788770220187 Издательство: Taylor&Francis Рейтинг: Цена: 93910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The integration of logic and probability combines the capability of the first to represent complex relations among entities with the capability of the latter to model uncertainty over attributes and relations. Logic programming provides a Turing complete language based on logic and thus represent an excellent candidate for the integration.Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. One of most successful approaches to Probabilistic Logic Programming is the Distribution Semantics, where a probabilistic logic program defines a probability distribution over normal logic programs and the probability of a ground query is then obtained from the joint distribution of the query and the programs. Foundations of Probabilistic Logic Programming aims at providing an overview of the field of Probabilistic Logic Programming, with a special emphasis on languages under the Distribution Semantics. The book presents the main ideas for semantics, inference and learning and highlights connections between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Автор: Michael Felsberg Название: Probabilistic and Biologically Inspired Feature Representations ISBN: 1681730235 ISBN-13(EAN): 9781681730233 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 46200.00 T Наличие на складе: Невозможна поставка. Описание: Under the title Probabilistic and Biologically Inspired Feature Representations, this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.
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