How deeply does Deep Learning with R, Third Edition cover SimpleRNN layer?

Defines input and output shapes, variable-length handling, stacking, and long-term-dependency limitations without deriving recurrent internals.

SimpleRNN layer

As explained in Deep Learning with R, Third Edition

A SimpleRNN layer is a Keras recurrent layer that computes sequence representations using basic recurrent state updates.

A SimpleRNN layer processes batches of sequences by updating recurrent state across timesteps. It accepts inputs shaped as (batch_size, timesteps, input_features). section 13.3.2

A SimpleRNN with full sequence output

num_features <- 14 steps <- 120 inputs <- keras_input(shape = c(steps, num_features)) outputs <- inputs |> layer_simple_rnn(16, return_sequences = TRUE)

SimpleRNN is a basic recurrent baseline, but it rarely handles long-term dependencies effectively because of the vanishing-gradient problem. LSTM and GRU layers provide alternatives. section 13.3.2

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Deep Learning with Python, Third EditionA Keras recurrent layer that processes sequences by iterating a simple recurrent cell over timesteps.

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Related concepts

recurrent neural networkkind of · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
Kerasuses · Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.
return_sequencesuses · return_sequences is a recurrent-layer setting that determines whether outputs are returned for every timestep or only the final timestep.
arrayuses · An array is an indexed, ordered collection of elements arranged in one or more dimensions, with elements accessed by position.
tensoruses · A tensor is a multidimensional, array-like structure of values indexed along zero or more dimensions and used for numerical computation.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.