C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist Questions and Answers

standard deviation = 0.5

expected value = 0

expected value = 0.5

expected value = 1

standard deviation = 1

grid search

population based training

random search

hyperband

Naive Bayes can be used for regression.

Let p(C1 | x) and p(C2 | x) be the conditional probabilities that x belongs to class C1 and C2 respectively, in a binary model, log p (C1 | x) – log p(C2 | x) > 0 results in predicting that x belongs to C2.

Naive Bayes is a conditional probability model.

Naive Bayes doesn't require any assumptions about the distribution of values associated with each class.

To go deep into the loss function landscape.

The higher the number of machine learning algorithms that can be applied, the deeper is the learning.

A kind of deeper understanding achieved by any approach taken.

It indicates the many layers contributing to a model of the data.

Machine learning models take less time to train.

Machine learning takes full advantage of SDKs and APIs.

Machine learning is optimized to run on parallel computing and cloud computing.

Machine learning does not require explicit coding of decision logic.

to randomize the trajectory of the neural network parameters during training

to smooth the gradient of the loss function in order to avoid getting trapped in small local minimas

to scale the gradient descent step in proportion to the gradient magnitude

to compute the gradient of the loss function with respect to the neural network parameters