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

Defines key creation, explicit passing, deterministic reuse, and splitting, but does not descend into key internals.

Key

As explained in Deep Learning with R, Third Edition

A PRNG key is an explicit data object that identifies the deterministic state used by a pseudo-random number generation operation.

A PRNG key is an explicit data object identifying the deterministic state used by a pseudo-random operation. JAX passes it explicitly instead of relying on global seed state. section 3.5.3

Create and reuse a key

Create a key with jax$random$key(123L), then pass it as the first argument to jax$random$normal(). Reusing the same key produces the same value each time. section 3.5.3

Split a key for new randomness

Use jax$random$split(seed_key, num = 1L)@py[0] to derive a new key. The split is deterministic, so the same sequence of splits produces the same resulting key. section 3.5.3

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

liveBook manning.com

Read Deep Learning with R, Third Edition on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Deep Learning with JAXA JAX pseudorandom-number-generator key passed explicitly to random functions for functional and reproducible randomness.
liveBook manning.com

Deep Learning with Python, Third EditionA JAX random-state value passed explicitly to PRNG operations to generate deterministic random numbers without global state.

Compare how 3 books explain key →

Related concepts

JAXused by · JAX is a Python library for high-performance array-based numerical computing with composable transformations including automatic differentiation, compilation, vectorization, and parallelization.
init()used by · The Linen method that initializes a Module's variables, including parameters, from dummy inputs and a PRNG key.
random seeduses · A random seed is an initial value or state that initializes or controls a pseudorandom process and determines its subsequent outputs.
jax.random.normal()used by · jax.random.normal() is a JAX function that generates normally distributed pseudorandom values using an explicit PRNG key and output shape.
jax.random.splitproduced by · jax.random.split() is a JAX function that deterministically derives one or more new pseudorandom-number generator keys from an existing key.
data augmentationused by · Data augmentation is the process of adding or deriving examples, variables, or associated information from existing data to increase its variety or support subsequent analysis or 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.