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Geochemical Mechanics and Deep Neural Network Modeling

testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the;

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Neural Network Modeling

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network;

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Flight Mechanics Modeling and Analysis

Covers flight mechanics, flight simulation, flight testing, flight control, and aeroservoelasticity. Features artificial neural network and;

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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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Rock Indentation

indentation. Includes artificial neural network concepts used in field of rock mechanics.;

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PyTorch Recipes

code. You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as;

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Deep Learning Essentials

the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural;

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Neural Network Perspectives on Cognition and Adaptive Robotics

Featuring an international team of authors, Neural Network Perspectives on Cognition and Adaptive Robotics presents several approaches to;

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Recurrent Neural Networks with Python Quick Start Guide

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network;

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Neural Network Perspectives on Cognition and Adaptive Robotics

Featuring an international team of authors, Neural Network Perspectives on Cognition and Adaptive Robotics presents several approaches to;

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Artificial Neural Networks

-layered learning network based on simple math rules. The structure of an artificial neural network consists of an input layer, an output layer and;

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Automated Deep Learning Using Neural Network Intelligence

Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI;

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Deep Neural Networks in a Mathematical Framework

to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive;

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Deep Learning for Hydrometeorology and Environmental Science

on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to;

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Deep Learning for Hydrometeorology and Environmental Science

on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to;

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Semi-empirical Neural Network Modeling and Digital Twins Development

solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single;

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Geochemical Modeling of Groundwater, Vadose and Geothermal Systems

Geochemical modeling is an important tool in environmental studies, and in the areas of subsurface and surface hydrology, pedology, water;

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Geochemical Modeling of Groundwater, Vadose and Geothermal Systems

Geochemical modeling is an important tool in environmental studies, and in the areas of subsurface and surface hydrology, pedology, water;

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Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network;

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Deep Learning on Edge Computing Devices

learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and;

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Mastering PyTorch

PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and;

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Advances in Computational Intelligence

systems; random-weights neural networks; pattern recognition; deep learning and natural language processing; software testing and intelligent;

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Advances in Computational Intelligence

systems; random-weights neural networks; pattern recognition; deep learning and natural language processing; software testing and intelligent;

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Gas Turbines Modeling, Simulation, and Control

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the;

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Gas Turbines Modeling, Simulation, and Control

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the;

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Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools

The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria;

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