Neural network fuzzy systems

The app is a complete free handbook of Neural network, fuzzy systems 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, computer science, machine learning, knowledge engineering programs & degree courses. 
This useful App lists 149 topics with detailed notes, diagrams, equations, formulas & course material, the topics are listed in 10 chapters. The app is must have for all the engineering science students & professionals. 

The app provides quick revision and reference to the important topics like a detailed flash card notes, it makes it easy & useful for the student or a professional to cover the course syllabus quickly before an exams or interview for jobs. 

Track your learning, set reminders, edit the study material, add favorite topics, share the topics on social media. 

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 at http://www.engineeringapps.net/. 

Use this useful engineering app as your tutorial, digital book, a reference guide for syllabus, course material, project work, sharing your views on the blog. 

Some of the topics Covered in the app are:

1) Register Allocation and Assignment2) The Lazy-Code-Motion Algorithm3) Matrix Multiply: An In-Depth Example4) Rsa topic 15) Introduction to Neural Networks6) History of neural networks7) Network architectures8) Artificial Intelligence of neural network9) Knowledge Representation10) Human Brain11) Model of a neuron12) Neural Network as a Directed Graph13) The concept of time in neural networks14) Components of neural Networks15) Network Topologies16) The bias neuron17) Representing neurons18) Order of activation19) Introduction to learning process20) Paradigms of learning21) Training patterns and Teaching input22) Using training samples23) Learning curve and error measurement24) Gradient optimization procedures25) Exemplary problems allow for testing self-coded learning strategies26) Hebbian learning rule27) Genetic Algorithms28) Expert systems29) Fuzzy Systems for Knowledge Engineering30) Neural Networks for Knowledge Engineering31) Feed-forward Networks32) The perceptron, backpropagation and its variants33) A single layer perceptron34) Linear Separability35) A multilayer perceptron36) Resilient Backpropagation37) Initial configuration of a multilayer perceptron38) The 8-3-8 encoding problem39) Back propagation of error40) Components and structure of an RBF network41) Information processing of an RBF network42) Combinations of equation system and gradient strategies43) Centers and widths of RBF neurons44) Growing RBF networks automatically adjust the neuron density45) Comparing RBF networks and multilayer perceptrons46) Recurrent perceptron-like networks47) Elman networks48) Training recurrent networks49) Hopfield networks50) Weight matrix51) Auto association and traditional application52) Heteroassociation and analogies to neural data storage53) Continuous Hopfield networks54) Quantization55) Codebook vectors56) Adaptive Resonance Theory57) Kohonen Self-Organizing Topological Maps58) Unsupervised Self-Organizing Feature Maps59) Learning Vector Quantization Algorithms for Supervised Learning60) Pattern Associations61) The Hopfield Network62) Limitations to using the Hopfield network

Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding. 

Neural network, fuzzy systems is part of Brain and Cognitive Sciences, AI, computer science, machine learning, electrical, electronics, knowledge engineering education courses and technology degree programs at various universities.