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Deep Learning and Linguistic Representation

achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the;

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Deep Learning and Linguistic Representation

achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the;

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Trends in Deep Learning Methodologies

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep;

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Graph Representation Learning

learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and;

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Deep Learning: Concepts and Architectures

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm;

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Deep Learning

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm;

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Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for;

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Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for;

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The Development of Deep Learning Technologies

methods in its ability to learn multiple levels of representation and abstraction by using several layers of nonlinear modules for feature;

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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem;

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Representation Learning

This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning;

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Representation Learning

This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning;

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Advanced Deep Learning with Keras

Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep reinforcement learning methods and;

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Deep Learning for Natural Language Processing

prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP;

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Deep Learning

, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models;

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Deep Learning for Biometrics

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation;

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Sentimental Analysis and Deep Learning

This book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021;

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Robust Representation for Data Analytics

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world;

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Frequency Effects In Language Learning And Processing

The volume contains a collection of studies on how the analysis of corpus and psycholinguistic data reveal how linguistic knowledge;

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Natural Language Processing

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops;

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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019;

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Chinese Learners and the Lexis Learning Rainbow

acquisition. This is the first model to combine both linguistic and non-linguistic strategies, motivation, and the stages of vocabulary learning into;

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Language, Biology and Cognition

neurolinguistics that natural language and linguistic cognition can be reconciled with neurobiology, the author argues that reducing representation to;

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Language, Biology and Cognition

neurolinguistics that natural language and linguistic cognition can be reconciled with neurobiology, the author argues that reducing representation to;

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Deep Learning Techniques for Biomedical and Health Informatics

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim;

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Deep Learning Techniques for Biomedical and Health Informatics

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim;

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An Introduction to Variational Autoencoders

wide array of applications from generative modeling, semi-supervised learning to representation learning.The authors expand earlier work and;

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