AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond, Zacharias Voulgaris, Yunus Bulut
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: 31324.00 р. 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: 22439.00 р. 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.
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: 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: 28215.00 р. 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.
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.
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