Neural network and Deep learning libraries for R

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package	description
nnet	Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models
neuralnet	Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005).
NeuralNetTools	Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights
RSNNS	The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the RSNNS low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R
FCNN4R	Provides an interface to kernel routines from the FCNN C++ library. FCNN is based on a completely new Artificial Neural Network representation that offers unmatched efficiency, modularity, and extensibility. FCNN4R provides standard teaching (backpropagation, Rprop, simulated annealing, stochastic gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon), but it is first and foremost an efficient computational engine. Users can easily implement their algorithms by taking advantage of fast gradient computing routines, as well as network reconstruction functionality (removing weights and redundant neurons, reordering inputs, merging networks). Networks can be exported to C functions in order to integrate them into virtually any software solution.
softmaxreg	Implementation of 'softmax' regression and classification models with multiple layer neural network. It can be used for many tasks like word embedding based document classification, 'MNIST' dataset handwritten digit recognition and so on. Multiple optimization algorithm including 'SGD', 'Adagrad', 'RMSprop', 'Moment', 'NAG', etc are also provided.
DARCH	The darch package is built on the basis of the code from G. E. Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief nets). This package is for generating neural networks with many layers (deep architectures) and train them with the method introduced by the publications "A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero, Y. W. Teh 2006) and "Reducing the dimensionality of data with neural networks" (G. E. Hinton, R. R. Salakhutdinov (2006). This method includes a pre training with the contrastive divergence method published by G.E Hinton (2002) and a fine tuning with common known training algorithms like backpropagation or conjugate gradients. Additionally, supervised fine-tuning can be enhanced with maxout and dropout, two recently developed techniques to improve fine-tuning for deep learning.
deeplearning	An implementation of deep neural network with rectifier linear units trained with stochastic gradient descent method and batch normalization. A combination of these methods have achieved state-of-the-art performance in ImageNet classification by overcoming the gradient saturation problem experienced by many deep architecture neural network models in the past. In addition, batch normalization and dropout are implemented as a means of regularization. The deeplearning package is inspired by the darch package and uses its class DArch.
deepnet	Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on
rnn	Implementation of a Recurrent Neural Network in R including GRU, LSTM
rbm	Restricted Boltzmann Machines in R(also see Landgraf's implementaion)
RcppDL	This package is based on the C++ code from Yusuke Sugomori, which implements basic machine learning methods with many layers (deep learning), including dA (Denoising Autoencoder), SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and DBN (Deep Belief Nets).
h20 DeepLearning	Deep Learning: Model high-level abstractions in data by using non-linear transformations in a layer-by-layer method. Deep learning is an example of unsupervised learning and can make use of unlabeled data that other algorithms cannot. Also see, deepwater
mxnet	Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler
TensorFlow	TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
autoencoder	Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng. The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks
SAENET	An implementation of a stacked sparse autoencoder for dimension reduction of features and pre-training of feed-forward neural networks with the 'neuralnet' package is contained within this package. The package also includes a predict function for the stacked autoencoder object to generate the compressed representation of new data if required. For the purposes of this package, 'stacked' is defined in line with http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders . The underlying sparse autoencoder is defined in the documentation of 'autoencoder'.
nnetpredint	Computing prediction intervals of neural network models (e.g.backpropagation) at certain confidence level. It can take the output from models trained by other packages like 'nnet', 'neuralnet', 'RSNNS', etc
deepr	An R package to streamline the training, fine-tuning and predicting processes for deep learning based on darch and deepnet
pnn	Probabilistic neural networks:
qrnn	Quantile Regression Neural Network
validann	Validation Tools for Artificial Neural Networks.
TeachNet	Fits neural networks to learn about back propagation.
neural	RBF and MLP neural networks with graphical user interface
monmlp	Monotone Multi-Layer Perceptron Neural Network.
learNN	Examples of Neural Networks..
grnn	The program GRNN implements the algorithm proposed by Specht (1991)
GMDH	Short Term Forecasting via GMDH-Type Neural Network Algorithms.
elmNN	Training and predict functions for SLFN
brnn	Bayesian Regularization for Feed-Forward Neural Networks
AMORE	This package was born to release the TAO robust neural network algorithm to the R users. It has
simpleNeural	An Easy to Use Multilayer Perceptron. Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification

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关键词：libraries Learning earning network Neural learning network

1. # create data
   
2. X = matrix(c(0,0,1,
   
3. 0,1,1,
   
4. 1,0,1,
   
5. 1,1,1), nrow=4, byrow=TRUE)
   
6. y = matrix(c(0,
   
7. 1,
   
8. 1,
   
9. 0),
   
10. nrow=4)
    
11. # run full function
    
12. learn_bp(X, y)

复制代码

https://cran.r-project.org/web/packages/learNN/learNN.pdf

1. Examples
   
2. # construct new data
   
3. X = matrix(c(0,0,1,0,1,1,1,0,1,1,1,1), nrow=4, byrow=TRUE)
   
4. y = matrix(c(0,1,1,0),nrow=4)
   
5. # run 11 lines function
   
6. learn_bp11(X, y)
   
7. # view output
   
8. syn0
   
9. syn1

复制代码

https://cran.r-project.org/web/packages/learNN/learNN.pdf

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