Keras is a popular deep learning library that provides a user-friendly API for building and training deep neural networks. It is written in Python and supports various backends such as TensorFlow, Theano, and CNTK.
In R, the Keras library provides an interface to the Keras API, allowing users to build and train deep learning models using R syntax. Keras in R supports many of the same features as the Python version, including the ability to build various types of neural networks, apply different types of activation functions, use various types of optimizers, and perform data preprocessing.
The Keras package in R provides various functions to build and train deep learning models, including keras_model_sequential() to create a sequential model, layer_dense() to add a dense layer to the model, layer_conv_2d() to add a convolutional layer, and many more. Additionally, Keras in R provides functions to compile the model with the chosen optimizer and loss function, and functions to train and evaluate the model.
Keras in R also supports other advanced features such as early stopping, data augmentation, and transfer learning. Overall, Keras in R is a powerful tool for building and training deep learning models using R syntax.