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Neural Networks with R

Giuseppe Ciaburro Balaji Venkateswaran 著

更新時(shí)間：2021-08-20 10:25:39

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ThisbookisintendedforanyonewhohasastatisticalbackgroundwithknowledgeinRandwantstoworkwithneuralnetworkstogetbetterresultsfromcomplexdata.Ifyouareinterestedinartificialintelligenceanddeeplearningandyouwanttolevelup,thenthisbookiswhatyouneed！

目錄(133章)

倒序

coverpage
Title Page
Copyright
Neural Networks with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
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
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
Competitive learning
Kohonen SOM
Summary
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
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
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
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
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 更新時(shí)間：2021-08-20 10:25:39

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Neural Networks with R