Concept

Random seed

Related lectures (10)

Explores smart weight initialization in neural networks, emphasizing the importance of proper data normalization and random weight initialization.

Covers the concept of tokens, how they work, and the implementation of two-factor authentication.

Explores random number generators on computers, focusing on the Modulo generator and the criteria for pseudo random numbers.

Explores Schrage's algorithm for generating random numbers and shift-register generators in computers.

Explores portable and non-portable random number generation algorithms and the shift-register generator concept.

Explores random number generators, from true random numbers to pseudo random algorithms, including the modulo generator and advanced techniques.

Covers Monte Carlo methods for simulating single particle diffusion in cementitious materials, including random functions and number generators.

Covers uniform and pseudo-random number generators, properties, examples, and quality assessment through empirical tests.

Explores random number generators, including Pseudo-RNG algorithms, properties, assessment methods, and independence testing.

Introduces the k-means algorithm for clustering data points based on centroids and iterative updates.