How deeply does Deep Learning with R, Third Edition cover sequence model?

Explains order handling, token preprocessing, padding, truncation, architectures, and position embeddings without deriving their mathematics.

Sequence model

As explained in Deep Learning with R, Third Edition

A sequence model is a model that processes an ordered sequence of tokens or other values while learning dependencies among their positions.

A sequence model processes tokens in order and learns positional dependencies instead of treating the input as an unordered set. Recurrent neural networks and Transformers are sequence models because they use word-order information, although they obtain it differently. section 14.3 section 14.5

IMDb sentiment classification

A practical workflow uses raw positive and negative movie reviews. Tokenization assigns integer IDs, while padding or truncation makes reviews rectangular for batch computation. Reviews can be limited to 600 words and a vocabulary of 30,000 tokens before being passed to a sequence model. section 14.3 section 14.5

Listing 14.18 Padding IMDb reviews to a fixed sequence length

max_length <- 600
max_tokens <- 30000
text_vectorization <- layer_text_vectorization(
  max_tokens = max_tokens,
  split = "whitespace",  #1
  output_mode = "int", #2
  output_sequence_length = max_length  #3
)
text_vectorization |> adapt(train_ds_no_labels)

sequence_train_ds <- train_ds |>
  dataset_map(\(x, y) tuple(text_vectorization(x), y),
              num_parallel_calls = 8)
sequence_val_ds <- val_ds |>
  dataset_map(\(x, y) tuple(text_vectorization(x), y),
              num_parallel_calls = 8)
sequence_test_ds <- test_ds |>
  dataset_map(\(x, y) tuple(text_vectorization(x), y),
              num_parallel_calls = 8)

Shows how Keras text vectorization converts reviews into fixed-length integer sequences, including vocabulary limits, padding, and dataset mapping. section 14.5

Sequence models can use several architectural strategies:

RNNs process tokens sequentially and use recurrent computation to remain order-aware. section 14.3 section 15.3.4
One-dimensional convolutional networks learn local patterns across the sequence. section 14.5
Transformers process relationships between sequence elements with attention, but require position embeddings because attention alone is order-agnostic. section 14.3 section 15.3.4
Compared with set models, sequence models generally require more data; pretraining on unlabeled text can reduce this data burden. section 14.6

How Deep Learning with R, Third Edition covers it

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Also explained in

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Deep Learning with Python, Third EditionA model designed to ingest an ordered token sequence and learn dependencies across positions in that sequence.

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Related concepts

recurrent neural networkincludes · 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.
n-gramincludes · An n-gram is a contiguous sequence of n consecutive items, commonly linguistic tokens or characters, drawn from a larger sequence.
bag-of-words modelcontrasts with · A bag-of-words model represents text using counts, presence indicators, or weights for its words or tokens while disregarding their order and syntactic relationships.
transformer architectureincludes · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
pretrainingaddressed by · Pretraining is the initial training of a model on broad data to learn general patterns or capabilities before adaptation to specific tasks or domains.

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.