Exploring neural networks with C#. (2014)

• Record Type: Book
• Further Information: Note: Ryszard Tadeusiewicz, Rituparna Chaki, Nabendu Chaki.
• Authors: Tadeusiewicz, Ryszard; Chaki, Rituparna; Chaki, Nabendu
• Contents: Introduction to Natural and Artificial Neural Networks; Why Learn about Neural Networks?; From Brain Research to Artificial Neural Networks; Construction of First Neural Networks; Layered Construction of Neural Network; From Biological Brain to First Artificial Neural Network; Current Brain Research Methods; Using Neural Networks to Study the Human Mind; Simplification of Neural Networks: Comparison with Biological Networks; Main Advantages of Neural Networks; Neural Networks as Replacements for Traditional Computers; Working with Neural Networks; References; ; Neural Net Structure; Building Neural Nets; Constructing Artificial Neurons; Attempts to Model Biological Neurons; How Artificial Neural Networks Work; Impact of Neural Network Structure on Capabilities; Choosing Neural Network Structures Wisely; "Feeding" Neural Networks: Input Layers; Nature of Data: The Home of the Cow; Interpreting Answers Generated by Networks: Output Layers; Preferred Result: Number or Decision?; Network Choices: One Network with Multiple Outputs versus Multiple Networks with Single Outputs; Hidden Layers; Determining Numbers of Neurons; References; Questions and Self-Study Tasks; ; Teaching Networks; Network Tutoring; Self-Learning; Methods of Gathering Information; Organizing Network Learning; Learning Failures; Use of Momentum; Duration of Learning Process; Teaching Hidden Layers; Learning without Teachers; Cautions Surrounding Self-Learning; Questions and Self-Study Tasks; ; Functioning of Simplest Networks; From Theory to Practice: Using Neural Networks; Capacity of Single Neuron; Experimental Observations; Managing More Inputs; Network Functioning; Construction of Simple Linear Neural Network; Use of Network; Rivalry in Neural Networks; Additional Applications; Questions and Self-Study Tasks; ; Teaching Simple Linear One-Layer Neural Networks; Building Teaching File; Teaching One Neuron; "Inborn" Abilities of Neurons; Cautions; Teaching Simple Networks; Potential Uses for Simple Neural Networks; Teaching Networks to Filter Signals; Questions and Self-Study Tasks; ; Nonlinear Networks; Advantages of Nonlinearity; Functioning of Nonlinear Neurons; Teaching Nonlinear Networks; Demonstrating Actions of Nonlinear Neurons; Capabilities of Multilayer Networks of Nonlinear Neurons; Nonlinear Neuron Learning Sequence; Experimentation during Learning Phase; Questions and Self-Study Tasks; ; Backpropagation; Definition; Changing Thresholds of Nonlinear Characteristics; Shapes of Nonlinear Characteristics; Functioning of Multilayer Network Constructed of Nonlinear Elements; Teaching Multilayer Networks; Observations during Teaching; Reviewing Teaching Results; Questions and Self-Study Tasks; ; Forms of Neural Network Learning; Using Multilayer Neural Networks for Recognition; Implementing a Simple Neural Network for Recognition; Selecting Network Structure for Experiments; Preparing Recognition Tasks; Observation of Learning; Additional Observations; Questions and Self-Study Tasks; ; Self-Learning Neural Networks; Basic Concepts; Observation of Learning Processes; Evaluating Progress of Self-Teaching; Neuron Responses to Self-Teaching; Imagination and Improvisation; Remembering and Forgetting; Self-Learning Triggers; Benefits from Competition; Results of Self-Learning with Competition; Questions and Self-Study Tasks; ; Self-Organizing Neural Networks; Structure of Neural Network to Create Mappings Resulting from Self-Organizing; Uses of Self-Organization; Implementing Neighborhood in Networks; Neighbor Neurons; Uses of Kohonen Networks; Kohonen Network Handling of Difficult Data; Networks with Excessively Wide Ranges of Initial Weights; Changing Self-Organization via Self-Learning; Practical Uses of Kohonen Networks; Tool for Transformation of Input Space Dimensions; Questions and Self-Study Tasks; ; Recurrent Networks; Description of Recurrent Neural Network; Features of Networks with Feedback; Benefits of Associative Memory; Construction of Hopfield Network; Functioning of Neural Network as Associative Memory; Program for Examining Hopfield Network Operations; Interesting Examples; Automatic Pattern Generation for Hopfield Network; Studies of Associative Memory; Other Observations of Associative Memory; Questions and Self-Study Tasks; ; Index
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2014
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 006.32; Neural networks (Computer science) -- Congresses; C♯ (Computer program language)
• Languages: English
• ISBNs: 9781482233407
• Related ISBNs: 9781482233391
• Notes: Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.