How deeply does Deep Learning with R, Third Edition cover data availability?

Defines the constraint and illustrates it with sentiment data, stopping at problem framing rather than detailing dataset collection or annotation procedures.

Data availability

As explained in Deep Learning with R, Third Edition

Data availability is the extent to which suitable examples and target annotations exist for developing a predictive model.

Data availability is the extent to which suitable examples and target annotations exist for developing a predictive model. It often limits whether a machine-learning problem is feasible: a system can learn sentiment classification only when both movie reviews and sentiment annotations are available. section 6.1.1

Movie-review sentiment classification

Before choosing a model, check whether the project has review texts paired with sentiment labels. If those annotations do not exist, collecting and annotating a dataset becomes necessary. section 6.1.1

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Machine Learning System DesignThe condition in which required data can be accessed reliably during model training and system runtime.

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Related concepts

migrationaddressed by · A migration is a process of transferring or transforming data, software, or system state from a source form or environment to a target form or environment.
replicationaddressed by · Replication is the creation or maintenance of multiple copies of data, state, or functionality across separate locations or components.
prototypingmeasured by · Prototyping is the practice of creating and evaluating a preliminary or partial implementation to explore or validate an idea, design, or approach.
pipelineconfigures · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
language modelused by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

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