最新的NVIDIA Generative AI Multimodal - NCA-GENM免費考試真題

A. Ignoring certain modalities and only using one modality for analysis and prediction.

B. Integrating different modalities at the beginning of the model pipeline.

C. Implementing the model in the early stages of development of the ML solution.

D. Training separate models for each modality and then combining their predictions.

A. NVIDIA Metropolis

B. NVIDIA Clara

C. NVIDIA DeepStream

D. NVIDIA NeMo

A. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.

B. Manually labeling each image in the dataset for precise classification.

C. Designing an algorithm to replace the need for textual descriptions in the classification process.

D. Focusing on enhancing the resolution and quality of images before classification.

A. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.

B. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.

C. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.

D. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.

E. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.

A. K-means clustering

B. Generative adversarial network (GAN)

C. Support vector machine (SVM)

D. Decision tree

A. To optimize the training process.

B. To generate new data based on the training set.

C. To calculate the loss function and update the generator.

D. To distinguish between real and generated data.