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Learning and Categorization in Modular Neural Networks

This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions;

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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation

applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Deep Learning from Scratch Building with Python from First Principles

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;

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Neural Networks with R

intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn * Set up R packages for neural;

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Advanced Deep Learning with Python

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural;

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Engineering Applications of Neural Networks

machine, machine learning applications, predictive models, fuzzy and recommender systems, recurrent neural networks, spiking neural networks.;

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Deep Neural Networks and Applications

Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;

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New Developments In Artificial Neural Networks Research

neural network based visual servo system; modelling of computer-assisted learning using artificial neural networks; prediction of hole quality;

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Handbook on Neural Information Processing

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical;

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Neural Networks for Complete Beginners

, and you will explore how to implement one programmatically.The following topics are discussed in this book:-What is a Neural Network?-Learning;

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Learning in Energy-Efficient Neuromorphic Computing - Algorithm and Architecture Co-Design

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning;

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Computational Mechanics with Neural Networks

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other;

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Computational Mechanics with Neural Networks

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Supervised Learning with Complex-valued Neural Networks

complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Introduction to Deep Learning and Neural Networks with PythonT

Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to;

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Introduction to Neural and Cognitive Modeling

findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence;

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Introduction to Neural and Cognitive Modeling

findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Hands-On Neural Networks

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key;

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Einde inhoud

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