عنوان

Exploring neural networks with C♯

(Number (ISBN

978-1-4822-3340-7 (eBook - PDF)

Number

3776

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Exploring neural networks with C♯

General Material Designation

[electronic resources]

First Statement of Responsibility

/ Ryszard Tadeusiewicz, Rituparna Chaki, Nabendu Chaki; programs by Tomasz Gąciarz, Barbara Borowik & Bartosz Leper.

Place of Publication, Distribution, etc.

Boca Raton

Name of Publisher, Distributor, etc.

: CRC Press/Taylor & Francis Group

Date of Publication, Distribution, etc.

, 2015.

Specific Material Designation and Extent of Item

xvi, 275 pages.

Other Physical Details

: illustrations (color), tables, charts.

Text of Note

Includes bibliographical references.

Text of Note

Introduction to Natural and Artificial Neural NetworksWhy Learn about Neural Networks? From Brain Research to Artificial Neural NetworksConstruction of First Neural NetworksLayered Construction of Neural NetworkFrom Biological Brain to First Artificial Neural NetworkCurrent Brain Research MethodsUsing Neural Networks to Study the Human MindSimplification of Neural Networks: Comparison with Biological NetworksMain Advantages of Neural NetworksNeural Networks as Replacements for Traditional ComputersWorking with Neural NetworksReferencesNeural Net StructureBuilding Neural NetsConstructing Artificial NeuronsAttempts to Model Biological NeuronsHow Artificial Neural Networks WorkImpact of Neural Network Structure on CapabilitiesChoosing Neural Network Structures Wisely"Feeding" Neural Networks: Input LayersNature of Data: The Home of the CowInterpreting Answers Generated by Networks: Output LayersPreferred Result: Number or Decision?Network Choices: One Network with Multiple Outputs versus Multiple Networks with Single OutputsHidden LayersDetermining Numbers of NeuronsReferencesQuestions and Self-Study TasksTeaching NetworksNetwork TutoringSelf-LearningMethods of Gathering InformationOrganizing Network LearningLearning FailuresUse of MomentumDuration of Learning ProcessTeaching Hidden LayersLearning without TeachersCautions Surrounding Self-LearningQuestions and Self-Study TasksFunctioning of Simplest NetworksFrom Theory to Practice: Using Neural NetworksCapacity of Single NeuronExperimental ObservationsManaging More InputsNetwork FunctioningConstruction of Simple Linear Neural NetworkUse of NetworkRivalry in Neural NetworksAdditional ApplicationsQuestions and Self-Study TasksTeaching Simple Linear One-Layer Neural NetworksBuilding Teaching FileTeaching One Neuron"Inborn" Abilities of NeuronsCautionsTeaching Simple NetworksPotential Uses for Simple Neural NetworksTeaching Networks to Filter SignalsQuestions and Self-Study TasksNonlinear NetworksAdvantages of NonlinearityFunctioning of Nonlinear NeuronsTeaching Nonlinear NetworksDemonstrating Actions of Nonlinear NeuronsCapabilities of Multilayer Networks of Nonlinear NeuronsNonlinear Neuron Learning SequenceExperimentation during Learning PhaseQuestions and Self-Study TasksBackpropagationDefinitionChanging Thresholds of Nonlinear CharacteristicsShapes of Nonlinear CharacteristicsFunctioning of Multilayer Network Constructed of Nonlinear ElementsTeaching Multilayer NetworksObservations during TeachingReviewing Teaching ResultsQuestions and Self-Study TasksForms of Neural Network LearningUsing Multilayer Neural Networks for RecognitionImplementing a Simple Neural Network for RecognitionSelecting Network Structure for ExperimentsPreparing Recognition TasksObservation of LearningAdditional ObservationsQuestions and Self-Study TasksSelf-Learning Neural NetworksBasic ConceptsObservation of Learning ProcessesEvaluating Progress of Self-TeachingNeuron Responses to Self-TeachingImagination and ImprovisationRemembering and ForgettingSelf-Learning TriggersBenefits from CompetitionResults of Self-Learning with CompetitionQuestions and Self-Study TasksSelf-Organizing Neural NetworksStructure of Neural Network to Create Mappings Resulting from Self-OrganizingUses of Self-OrganizationImplementing Neighborhood in NetworksNeighbor NeuronsUses of Kohonen NetworksKohonen Network Handling of Difficult DataNetworks with Excessively Wide Ranges of Initial WeightsChanging Self-Organization via Self-LearningPractical Uses of Kohonen NetworksTool for Transformation of Input Space DimensionsQuestions and Self-Study TasksRecurrent NetworksDescription of Recurrent Neural NetworkFeatures of Networks with FeedbackBenefits of Associative MemoryConstruction of Hopfield NetworkFunctioning of Neural Network as Associative MemoryProgram for Examining Hopfield Network OperationsInteresting ExamplesAutomatic Pattern Generation for Hopfield NetworkStudies of Associative MemoryOther Observations of Associative MemoryQuestions and Self-Study TasksIndex

0

Entry Element

شبکه‌های عصبی (کامپیوتر)

Entry Element

Neural networks (Computer Science)

Entry Element

Neural networks (Computer science) -- Problems, exercises, etc.

Entry Element

سی‌ شارپ (زبان برنامه‌نویسی کامپیوتر)

Entry Element

C # (Computer program language)

a03

a03

a06

a06

Subject Term

Computer science.

Subject Term

Neural networks.

Subject Term

C#.

Subject Term

C# Computer program language.

Entry Element

Tadeusiewicz, Ryszard,

Relator Code

Author

Entry Element

Chaki, Rituparna,

Entry Element

Chaki, Nabendu,

Relator Code

Cllaborator

Relator Code

Cllaborator

Entry Element

Gąciarz, Tomasz

Borowik, Barbara

Leper, Bartosz

Country

ایران

Agency

Central Library of the Kurdistan University

e

BL

280328

a

Y