Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 76690.00 T Наличие на складе: Поставка под заказ. Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Автор: Mukesh Khare; S.M. Shiva Nagendra Название: Artificial Neural Networks in Vehicular Pollution Modelling ISBN: 3642072224 ISBN-13(EAN): 9783642072222 Издательство: Springer Рейтинг: Цена: 130590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a step-by-step procedure for formulation and development of Artificial Neural Networks based Vehicular pollution models. It takes into account meteorological and traffic aspects. The book will be useful for professionals and researchers working in problems associated with urban air pollution management and control
Автор: Zhang Tsinghua University Press Liyi Название: Blind Equalization in Neural Networks ISBN: 3110449625 ISBN-13(EAN): 9783110449624 Издательство: Walter de Gruyter Цена: 123910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists.
Want to predict what your customers want to buy without them having to tell you? Want to accurately forecast sales trends for your marketing team better than any employee could ever do? Then keep reading.
You've heard it before. The rise of artificial intelligence and how it will soon replace human beings and take away our jobs. What exactly is it capable of and how does this impact me? The real question you should be asking yourself is how can I use this to my advantage? How can I use machine learning to benefit my business and surpass my business goals? This book has the answer.
Designed for the tech novice, this book will break down the fundamentals of machine learning and what it truly means. You will learn to leverage neural networks, predictive modelling, and data mining algorithms, illustrated with real-world applications for finance, business and marketing.
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✅ Each of the buzzwords defined
✅ 20 real-world applications of machine learning.
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Автор: Graupe Название: Principles of artificial neural network ISBN: 9812706240 ISBN-13(EAN): 9789812706249 Издательство: World Scientific Publishing Рейтинг: Цена: 116160.00 T Наличие на складе: Невозможна поставка. Описание: Suitable as a self-study course for engineers and computer scientists in the industry, this book covers major neural network approaches and architectures with the theories. It presents detailed case studies for each of the approaches, accompanied with computer codes and the corresponding computed results.
Are you thinking that as much as we want to look for logical frameworks for intelligence, there is no certainty or scientific proof that intelligence is as structured as we believe it to be?
As in the evolutionary process, where chaos and order wisely coexist, I see a research gap related to our brain and mind, typically related to focusing on models based solely on order.
But if we are researching Artificial Intelligence, why are we so attached to the order and models that are supposed to be those of our brain?
Or, what binds us so much to what we see only, without opening spaces to what we don't see, if only to consider them small pieces of chaos?
In this openness and vision, when it comes to intelligence, I propose a new concept: that of unstructured intelligence, which I will try to explain in this book.
In this book, you will learn:
Automatic Learning
Machine Learning Paradigms
Inductive Learning
Induction Of Decision Trees
The relevance of attributes
Algorithms
Cluster
And Much more...
I think one of the main reasons for AI's long winter was that we went deep into it, creating architectures focused on existing paradigms, with little investment in new technologies and standards, such as machine learning itself.
But are we aren't repeating the same mistake in this new wave of AI?
If so, I consider the main mistake too much focus on artificial neural network architectures, as if this was the solution to solving complex learning problems in the human pattern or even the main door to generic artificial intelligence with semantic analysis capabilities.
And a possible solution to avoid the same history of past failure, perhaps, is to tackle high complexity real-world learning problems collectively and collaboratively, such as creating AI systems that can teach them to learn for themselves, like us humans.
So the architecture that seems to be the most logical for such problems is precisely the hybrid, where we have the most varied types of learning. In fact, before we are born, we are already learning in a hybrid way, with labeled and unlabeled data, by its very nature, and all its mechanisms of evolution.
You may think that you don't remember any important labeled data when you were a baby or child, but your mind and brain did a swell job to solve the puzzles that required some labeling to move on, as unsupervised learning systems follow.
So we can think of a similar machine architecture where the basis for all inferences is supervised learning, but capable of labeling any data that is not done by humans or other machines. And even criticize existing labels.
We are actually talking about machine learning - unsupervised - to generate labels for machine learning.
And creativity, in my view, is one of the essential links to evolve in understanding and formalizing new machine learning models.
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Автор: Quang Hung Do Название: Artificial Neural Network Applications in Business and Engineering ISBN: 1799832384 ISBN-13(EAN): 9781799832386 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 260570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method.
Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.
Автор: Quang Hung Do Название: Artificial Neural Network Applications in Business and Engineering ISBN: 1799832392 ISBN-13(EAN): 9781799832393 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 214370.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method.
Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.
Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book.
Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN)
Authored by top experts, global field pioneers and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making
Edited by high-level academics and researchers in intelligent systems and neural networks
Автор: Zhang Название: Toward Deep Neural Networks ISBN: 1138387037 ISBN-13(EAN): 9781138387034 Издательство: Taylor&Francis Рейтинг: Цена: 127600.00 T Наличие на складе: Нет в наличии. Описание: This book introduces deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors` 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet.
Автор: by Shashi Narayan, Claire Gardent Название: Deep Learning Approaches to Text Production ISBN: 1681737604 ISBN-13(EAN): 9781681737607 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 95170.00 T Наличие на складе: Поставка под заказ. Описание: Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
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