Description

The app is a complete free handbook of Neural network, fuzzysystems which cover important topics, notes, materials, news &blogs on the course. Download the App as a reference material &digital book for Brain and Cognitive Sciences, AI, computerscience, machine learning, knowledge engineering programs &degree courses.  This useful App lists 149 topics withdetailed notes, diagrams, equations, formulas & coursematerial, the topics are listed in 10 chapters. The app is musthave for all the engineering science students &professionals.  The app provides quick revision and referenceto the important topics like a detailed flash card notes, it makesit easy & useful for the student or a professional to cover thecourse syllabus quickly before an exams or interview forjobs.  Track your learning, set reminders, edit the studymaterial, add favorite topics, share the topics on socialmedia.  You can also blog about engineering technology,innovation, engineering startups,  college research work,institute updates, Informative links on course materials &education programs from your smartphone or tablet or athttp://www.engineeringapps.net/.  Use this useful engineeringapp as your tutorial, digital book, a reference guide for syllabus,course material, project work, sharing your views on theblog.  Some of the topics Covered in the app are: 1) RegisterAllocation and Assignment 2) The Lazy-Code-Motion Algorithm 3)Matrix Multiply: An In-Depth Example 4) Rsa topic 1 5) Introductionto Neural Networks 6) History of neural networks 7) Networkarchitectures 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 inneural networks 14) Components of neural Networks 15) NetworkTopologies 16) The bias neuron 17) Representing neurons 18) Orderof activation 19) Introduction to learning process 20) Paradigms oflearning 21) Training patterns and Teaching input 22) Usingtraining samples 23) Learning curve and error measurement 24)Gradient optimization procedures 25) Exemplary problems allow fortesting self-coded learning strategies 26) Hebbian learning rule27) Genetic Algorithms 28) Expert systems 29) Fuzzy Systems forKnowledge Engineering 30) Neural Networks for Knowledge Engineering31) Feed-forward Networks 32) The perceptron, backpropagation andits variants 33) A single layer perceptron 34) Linear Separability35) A multilayer perceptron 36) Resilient Backpropagation 37)Initial configuration of a multilayer perceptron 38) The 8-3-8encoding problem 39) Back propagation of error 40) Components andstructure of an RBF network 41) Information processing of an RBFnetwork 42) Combinations of equation system and gradient strategies43) Centers and widths of RBF neurons 44) Growing RBF networksautomatically adjust the neuron density 45) Comparing RBF networksand multilayer perceptrons 46) Recurrent perceptron-like networks47) Elman networks 48) Training recurrent networks 49) Hopfieldnetworks 50) Weight matrix 51) Auto association and traditionalapplication 52) Heteroassociation and analogies to neural datastorage 53) Continuous Hopfield networks 54) Quantization 55)Codebook vectors 56) Adaptive Resonance Theory 57) KohonenSelf-Organizing Topological Maps 58) Unsupervised Self-OrganizingFeature Maps 59) Learning Vector Quantization Algorithms forSupervised Learning 60) Pattern Associations 61) The HopfieldNetwork 62) Limitations to using the Hopfield network Each topic iscomplete with diagrams, equations and other forms of graphicalrepresentations for better learning and quick understanding. Neural network, fuzzy systems is part of Brain and CognitiveSciences, AI, computer science, machine learning, electrical,electronics, knowledge engineering education courses and technologydegree programs at various universities.