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Advanced Models of Neural Networks

introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and;

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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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Neural Networks with R

advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a;

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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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Advanced Deep Learning with R

advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks;

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Animal Learning and Cognition

In this advanced text the author, starting with the simple assumption that psychological associations are represented by the strength;

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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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Statistical Mechanics of Neural Networks

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks;

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Complex-Valued Neural Networks

This book covers the hot topics and applications surrounding complex-valued neural networks. It demonstrates advanced theories with a wide;

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Hands-On Deep Learning with Go

that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted;

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Network Models for Control and Processing

related fields. These cover: control processes, modelling features and operations of biological neural networks and neurons, simulation;

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The Essence of Neural Networks

The Essence of Neural Networks 1e editie is een boek van Robert Callan uitgegeven bij Pearson Education (Us). ISBN 9780139087325

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Neural Information Processing

Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019.For ICONIP 2019 a total of 345 papers was carefully;

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Introduction to Graph Neural Networks

contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural;

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Introduction to Graph Neural Networks

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with;

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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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Mastering Predictive Analytics with scikit-learn and TensorFlow

Learn advanced techniques to improve the performance and quality of your predictive models Key Features Use ensemble methods to improve the;

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PyTorch Recipes

evaluating and fine-tuning models using PyTorch Use advanced torch features in training deep neural networks Explore various neural network models;

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Pattern Recognition with Neural Networks in C++

book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate;

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Pattern Recognition with Neural Networks in C++

book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate;

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Sensory Neural Networks

Sensory information is detected and transformed by sensory neural networks before reaching higher levels of processing. These networks need;

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Revival: Sensory Neural Networks (1991)

Sensory information is detected and transformed by sensory neural networks before reaching higher levels of processing. These networks need;

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Deep Neuro-Fuzzy Systems with Python

Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current;

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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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Neural Networks with Discontinuous/Impact Activations

This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities;

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