Neural Networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles

Neural Networks with R smart models using CNN, RNN, deep learning, and artificial intelligence principles

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Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of n...

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Detalles Bibliográficos
Otros Autores: Ciaburro, Giuseppe, author (author), Venkateswaran, Balaji, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai, [India] : Packt 2017.
Edición:1st edition
Materias:
Neural networks (Computer science)
R (Computer program language).
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630732406719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Credits
  • About the Authors
  • About the Reviewer
  • www.PacktPub.com
  • Customer Feedback
  • Table of Contents
  • Preface
  • Chapter 1: Neural Network and Artificial Intelligence Concepts
  • Introduction
  • Inspiration for neural networks
  • How do neural networks work?
  • Layered approach
  • Weights and biases
  • Training neural networks
  • Supervised learning
  • Unsupervised learning
  • Epoch
  • Activation functions
  • Different activation functions
  • Linear function
  • Unit step activation function
  • Sigmoid
  • Hyperbolic tangent
  • Rectified Linear Unit
  • Which activation functions to use?
  • Perceptron and multilayer architectures
  • Forward and backpropagation
  • Step-by-step illustration of a neuralnet and an activation function
  • Feed-forward and feedback networks
  • Gradient descent
  • Taxonomy of neural networks
  • Simple example using R neural net library - neuralnet()
  • Let us go through the code line-by-line
  • Implementation using nnet() library
  • Let us go through the code line-by-line
  • Deep learning
  • Pros and cons of neural networks
  • Pros
  • Cons
  • Best practices in neural network implementations
  • Quick note on GPU processing
  • Summary
  • Chapter 2: Learning Process in Neural Networks
  • What is machine learning?
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Training and testing the model
  • The data cycle
  • Evaluation metrics
  • Confusion matrix
  • True Positive Rate
  • True Negative Rate
  • Accuracy
  • Precision and recall
  • F-score
  • Receiver Operating Characteristic curve
  • Learning in neural networks
  • Back to backpropagation
  • Neural network learning algorithm optimization
  • Supervised learning in neural networks
  • Boston dataset
  • Neural network regression with the Boston dataset
  • Unsupervised learning in neural networks&amp.
  • #160
  • Competitive learning
  • Kohonen SOM
  • Summary
  • Chapter 3: Deep Learning Using Multilayer Neural Networks
  • Introduction of DNNs
  • R for DNNs
  • Multilayer neural networks with neuralnet
  • Training and modeling a DNN using H2O
  • Deep autoencoders using H2O
  • Summary
  • Chapter 4: Perceptron Neural Network Modeling - Basic Models
  • Perceptrons and their applications
  • Simple perceptron - a linear separable classifier
  • Linear separation
  • The perceptron function in R
  • Multi-Layer Perceptron
  • MLP R implementation using RSNNS
  • Summary
  • Chapter 5: Training and Visualizing a Neural Network in R
  • Data fitting with neural network
  • Exploratory analysis
  • Neural network model
  • Classifing breast cancer with a neural network
  • Exploratory analysis
  • Neural network model
  • The network training phase
  • Testing the network
  • Early stopping in neural network training
  • Avoiding overfitting in the model
  • Generalization of neural networks
  • Scaling of data in neural network models
  • Ensemble predictions using neural networks
  • Summary
  • Chapter 6: Recurrent and Convolutional Neural Networks
  • Recurrent Neural Network
  • The rnn package in R
  • LSTM model
  • Convolutional Neural Networks
  • Step #1 - filtering
  • Step #2 - pooling
  • Step #3 - ReLU for normalization
  • Step #4 - voting and classification in the fully connected layer
  • Common CNN architecture - LeNet
  • Humidity forecast using RNN
  • Summary
  • Chapter 7: Use Cases of Neural Networks - Advanced Topics
  • TensorFlow integration with R
  • Keras integration with R
  • MNIST HWR using R
  • LSTM using the iris dataset
  • Working with autoencoders
  • PCA using H2O
  • Autoencoders using H2O
  • Breast cancer detection using darch
  • Summary
  • Index.

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