IBM AI Enterprise Workflow V1 Data Science Specialist - C1000-059 FREE EXAM DUMPS QUESTIONS & ANSWERS

A. to scale the gradient descent step in proportion to the gradient magnitude B. to smooth the gradient of the loss function in order to avoid getting trapped in small local minimas C. to compute the gradient of the loss function with respect to the neural network parameters D. to randomize the trajectory of the neural network parameters during training

A. by increasing the depth of the neural network B. by assigning random labels C. by using a customized pre-trained model D. by creating random data

A. A * B B. A @ B C. A × B D. np.dot(A,B) E. np.matprod(A,B)

A. Machine learning models take less time to train. B. Machine learning does not require explicit coding of decision logic. C. Machine learning is optimized to run on parallel computing and cloud computing. D. Machine learning takes full advantage of SDKs and APIs.

A. Training and testing data for the model contain outliers. B. Data for the machine learning model is available only as static CSV files. C. The neural network for the model requires significantly more computing power than a logistic regression model. D. The use case requires a 100% likelihood of making a correct/true prediction.

A. The data sparsity becomes more severe as the number of features is increased. B. The number of available algorithms for a given task is high. C. The number of available data sources for a given task is high. D. The data sparsity becomes more severe as the number of samples is increased.

A. square B. subtract median C. division by random numbers D. logarithm