先試後買

A. RAPIDS is a Python library that provides functions to accelerate the PCIe bus throughput via word- doubling.

B. RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU- GPU memory transfers.

C. RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.

A. NeMo focuses on the morphology of a language by studying its words, and how they are formed.

B. NeMo helps researchers to develop models that trade-off size with minimum loss impact.

C. NeMo facilitates the creation of models for speech recognition and natural language understanding.

D. NeMo helps researchers develop state-of-the-art models for computer vision based on convolutions.

A. Retrieval-augmented generation with external mathematical databases.

B. Chain-of-thought prompting with step-by-step reasoning examples.

C. Zero-shot prompting with a generic task description.

D. Few-shot prompting with randomly selected examples.

A. Zero-shot learning

B. Fine tuning a model

C. Few-shot learning

D. One-shot learning

A. Decision tree

B. K-means clustering

C. Generative adversarial network

D. Support vector machine

A. Git

B. Pandas

C. Falcon

D. Triton

A. To reduce the computational resources needed for training models.

B. To provide a consistent environment for model training and inference.

C. To directly increase the accuracy of machine learning models.

D. To automatically generate features for machine learning models.

A. JSON parser

B. Tableau dashboard

C. ActionScript

D. Regular expression filter

A. Diffusion models are unsupervised models that use clustering algorithms to group similar data points together.

B. Diffusion models are generative models that use a transformer architecture to learn the underlying probability distribution of the data.

C. Diffusion models are probabilistic generative models that progressively inject noise into data, then learn to reverse this process for sample generation.

D. Diffusion models are discriminative models that use gradient-based optimization algorithms to classify data points.

A. F1 Score

B. Perplexity

C. ROUGE score

D. BLEU score