Cargando…
Neural network programming with Java : unleash the power of neural networks by implementing professional Java code /
Create and unleash the power of neural networks by implementing professional Java codeAbout This Book Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition Explore the Java multi-platform feature to run your personal neural networks everywher...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2016.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Getting Started with Neural Networks; Discovering neural networks; Why artificial neural network?; How neural networks are arranged; The very basic element
- artificial neuron; Giving life to neurons
- activation function; The fundamental values
- weights; An important parameter
- bias; The parts forming the whole
- layers; Learning about neural network architectures; Monolayer networks; Multilayer networks; Feedforward networks; Feedback networks
- From ignorance to knowledge
- learning processLet the implementations begin! Neural networks in practice; Summary; How Neural Networks Learn; Learning ability in neural networks; How learning helps to solve problems; Learning paradigms; Supervised learning; Unsupervised learning; Systematic structuring
- learning algorithm; Two stages of learning
- training and testing; The details
- learning parameters; Error measurement and cost function; Examples of learning algorithms; Perceptron; Delta rule; Coding of the neural network learning; Learning parameter implementation; Learning procedure
- Class definitionsTwo practical examples; Perceptron (warning system); ADALINE (traffic forecast); Summary; Handling Perceptrons; Studying the perceptron neural network; Applications and limitations of perceptrons; Linear separation; Classical XOR case; Popular multilayer perceptrons (MLPs); MLP properties; MLP weights; Recurrent MLP; MLP structure in an OOP paradigm; Interesting MLP applications; Classification in MLPs; Regression in MLPs; Learning process in MLPs; Simple and very powerful learning algorithm
- Backpropagation; Elaborate and potent learning algorithm
- Levenberg-Marquardt
- Hands-on MLP implementation!Backpropagation in action; Exploring the code; Levenberg-Marquardt implementation; Practical application
- types of university enrolments; Summary; Self-Organizing Maps; Neural networks' unsupervised way of learning; Some unsupervised learning algorithms; Competitive learning or winner takes all; Kohonen self-organizing maps (SOMs); One-Dimensional SOM; Two-Dimensional SOM; Step-by-step of SOM learning; How to use SOMs; Coding of the Kohonen algorithm; Exploring the Kohonen class; Kohonen implementation (clustering animals); Summary; Forecasting Weather
- Neural networks for prediction problemsNo data, no neural net
- selecting data; Knowing the problem
- weather variables; Choosing input and output variables; Removing insignificant behaviors
- Data filtering; Adjusting values
- data preprocessing; Equalizing data
- normalization; Java implementation for weather prediction; Plotting charts; Handling data files; Building a neural network for weather prediction; Empirical design of neural networks; Choosing training and test datasets; Designing experiments; Results and simulations; Summary; Classifying Disease Diagnosis