what are the packages required for a neural networks in R

Recipe Objective - what are the packages required for a neural networks in R.

Packages like "neuralnet" used for implementation of backpropogation in neural networks while implementing deep learning. "tensorflow" package provides an interface to Tensorflow which is an open source software developed by google for numerical computation and data flow diagrams mostly used implementing neural networks which requires mathematical computation. "keras" package provides API to keras library which supports both recurrent neural networks(RNN) and convolutional neural networks(CNN). It supports both CPU and GPU for faster computation.

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Explanation of Packages.

"neuralnet" package gives customized choice of selecting error and activation function for the neural network. It provides various function such as compute, confidence.interval, neuralnet, plot.nn, predict.nn and prediction. These functions plays an important role in creating, predicting and plotting a neural network in R.

"tensorflow" package is the R interface to popular Tensorflow library developed by Google Brain team for conducting research in deep learning and machine learning. It provides various functions such as tensorboard, tf_extract_ops, train, train_and_evaluate, view_savedmodel etc.

"keras" package is the R interface to popular Keras library developed for supporting high-level neural networks with focus on enabling experimentions at a fast rate. It provides pre-trained models, in-built datasets, callback function, visalization function etc.