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Backpropagation

Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section;

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Backpropagation

Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section;

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The Roots Of Backpropagation

Now, for the first time, publication of the landmark work in backpropagation! Scientists, engineers, statisticians, operations researchers;

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Introduction to Neural Networks for C#, 2nd Edition

backpropagation, genetic algorithms, and simulated annealing are also introduced.;

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Neural Network Programming with Python

backpropagation algorithm. This is the algorithm behind the functionality of neural networks, and it involves a forward and backward pass. Numby is a;

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Bonitatsprufung von Firmenkunden mit Hilfe kunstlicher neuronaler Netze

4.2.2Das Multi-Layer-Perceptron 4.2.2.1Grundlagen 4.2.2.2Beispiel 2: Das XOR-Problem mit dem MLP 4.2.2.3Der Backpropagation-Algorithmus;

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Multilayer Perceptrons

neural network with one hidden layer and trained through the efficient resilient backpropagation algorithm is used for modeling quasi-fractal;

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Math For Deep Learning

as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also use Python to work through the;

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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.The;

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Environmental Remote Sensing in Flooding Areas

outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new;

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Environmental Remote Sensing in Flooding Areas

outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new;

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Die Eignung Neuronaler Netze fur die Mining-Funktionen Clustern und Vorhersage

die Mining-Funktionen Clustern und Vorhersage zu untersuchen. Dabei begrenzt sich die Sichtweise auf Backpropagation- und Kohonen-Netze, da diese;

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Artificial Adaptive Systems Using Auto Contractive Maps

backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book's;

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Clustering and Neural Network Approaches for General NN-Simulator

different example in real analysis.The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the;

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Intrusion Detection with Artificial Neural Networks

' behavior and finds the anomalies by matching with this normal behavior. A special type of neural network called backpropagation neural network;

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Simulating Neural Networks with Mathematica

Mathematica Training by Error Minimization Backpropagation and Its Variants Probability and Neural Networks Optimization and Constraint Satisfaction;

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Recurrent Neural Networks

) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled;

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Untersuchung, Implementierung und Einsatz des Levenberg-Marquardt-Algorithmus zur Analyse von Chip-Fertigungsdaten

wie Backpropagation oder Counterpropagation, in diesen Simulator implementiert und auf seine Potentiale bei der Analyse von Chip-Fertigungsdaten evaluiert;

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Introduction to Modeling Cognitive Processes

covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical;

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Analyse und Prognose des Modellzyklusses in der Automobilbranche mit kunstlichen neuronalen Netzen

konfigurierte Backpropagation-Netze auf ihre Zweckmassigkeit hin gepruft. Die Ergebnisse werden abschliessend zusammengefasst und Ansatze zu;

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Deep Learning

comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the;

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Principles of Data Mining

known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation;

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Introduction to Machine Learning with Applications in Information Security

Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP;

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Odyssey of Light in Nonlinear Optical Fibers

-Kaup-Newell-Segur (AKNS) hierarchies for nonautonomous solitons, state-of-the-art Brillouin scattering applications, backpropagation, and the;

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Complex-Valued Neural Networks with Multi-Valued Neurons

Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error;

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Deep Learning

training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets;

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Elements of Artificial Neural Networks

separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after;

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