How deeply does Deep Learning with R, Third Edition cover global state?

Defines hidden random state, demonstrates seed control, and contrasts it with JAX keys without detailing all purity mechanics.

Global state

As explained in Deep Learning with R, Third Edition

Global state is mutable information maintained outside a function and implicitly shared across calls.

Global state is mutable information maintained outside a function and implicitly shared across calls. In R, random-number functions consult hidden global state, including .Random.seed, so repeated calls can produce different values. section 3.5.3

Randomness through hidden state

Calling runif(3) twice produces different results because the first call changes the random-number state used by the second. set.seed() can set that state, while keras3::set_random_seed() coordinates seeds across R, NumPy, Python, TensorFlow, and Torch. section 3.5.3

JAX avoids hidden global state. Its random operations receive explicit PRNG key objects, making identical calls with identical keys reproducible; jax$random$split() creates a new key for new values. section 3.5.3

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Related concepts

statekind of · State is information or a configuration describing the condition of a system, entity, or computation at a particular time and potentially influencing subsequent behavior or decisions.
resource contentionproduces · Resource contention is competition among concurrent operations, processes, or workloads for access to a limited shared resource.
shared state objectpart of · A shared state object is a structured data object that holds current or common information for access or updating during collaborative execution.
windowproduced by · The browser window object is the global object for a browsing context, exposing its document, JavaScript environment, and associated browser APIs.
pure functioncontrasts with · A pure function is a function whose output is determined solely by its explicit inputs and whose evaluation has no externally observable side effects.

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.