This unique free application is for all students of Neural Network & Fuzzy Systems across the world. It covers 149 topics of Neural Network & Fuzzy Systems in detail. These 149 topics are divided in 10 units. Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail. The USP of this application is 'ultra-portability'. Students can access the content on-the-go from any where they like. Basically, each topic is like a detailed flash card and will make the lives of students simpler and easier. Some of topics Covered in this application are: 1) Register Allocation and Assignment 2) The Lazy-Code-Motion Algorithm 3) Matrix Multiply: An In-Depth Example 4) Rsa topic 1 5) Introduction to Neural Networks 6) History of neural networks 7) Network architectures 8) Artificial Intelligence of neural network 9) Knowledge Representation 10) Human Brain 11) Model of a neuron 12) Neural Network as a Directed Graph 13) The concept of time in neural networks 14) Components of neural Networks 15) Network Topologies 16) The bias neuron 17) Representing neurons 18) Order of activation 19) Introduction to learning process 20) Paradigms of learning 21) Training patterns and Teaching input 22) Using training samples 23) Learning curve and error measurement 24) Gradient optimization procedures 25) Exemplary problems allow for testing self-coded learning strategies 26) Hebbian learning rule 27) Genetic Algorithms 28) Expert systems 29) Fuzzy Systems for Knowledge Engineering 30) Neural Networks for Knowledge Engineering 31) Feed-forward Networks 32) The perceptron, backpropagation and its variants 33) A single layer perceptron 34) Linear Separability 35) A multilayer perceptron 36) Resilient Backpropagation 37) Initial configuration of a multilayer perceptron 38) The 8-3-8 encoding problem 39) Back propagation of error 40) Components and structure of an RBF network 41) Information processing of an RBF network 42) Combinations of equation system and gradient strategies 43) Centers and widths of RBF neurons 44) Growing RBF networks automatically adjust the neuron density 45) Comparing RBF networks and multilayer perceptrons 46) Recurrent perceptron-like networks 47) Elman networks 48) Training recurrent networks 49) Hopfield networks 50) Weight matrix 51) Auto association and traditional application 52) Heteroassociation and analogies to neural data storage 53) Continuous Hopfield networks 54) Quantization 55) Codebook vectors 56) Adaptive Resonance Theory 57) Kohonen Self-Organizing Topological Maps 58) Unsupervised Self-Organizing Feature Maps 59) Learning Vector Quantization Algorithms for Supervised Learning 60) Pattern Associations 61) The Hopfield Network 62) Limitations to using the Hopfield network 63) Boltzmann Machines 64) Neural Network Models 65) Hamming Networks 66) Counterpropagation Networks 67) RAM-Based Neurons and Networks 68) Fuzzy Neurons 69) Fuzzy Neural Networks 70) Hierarchical and Modular Connectionist Systems 71) Neural Networks as a Problem-Solving Paradigm 72) Problem Identification and Choosing the Neural Network Model 73) Encoding the Information 74) The Best Neural Network Model 75) Architectures and Approaches to Building Connectionist Expert Systems 76) Connectionist Knowledge Bases from Past Data 77) Neural Networks Can Memorize and Approximate Fuzzy Rules 78) Acquisition of Knowledge 79) Destructive Learning 80) Competitive Learning Neural Networks for Rules Extraction 81) The REFuNN algorithm 82) Representing Spatial and Temporal Patterns in Neural Networks 83) Pattern Recognition and Classification 84) Image Processing 85) Speech processing 86) MLP for Speech Recognition 87) Using SOM for Phoneme Recognition 88) Time-Delay Neural Networks for Speech Recognition 89) Monitoring 90) Connectionist Systems for Diagnosis
Manufacturer: Ashish Kumar
Brand: Ashish Kumar
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