Computer vision, a subfield of AI, has various application scenarios that involve the analysis and understanding of images and videos. Some key application scenarios include:
Video action analysis: Identifying and analyzing human actions or movements in videos.
Image search: Using visual information to search for similar images in large databases.
Smart albums: Organizing and categorizing photos using AI-based image recognition algorithms to group them by themes, people, or events.
Voice navigation is a part of natural language processing and speech recognition, not computer vision.
[Reference: Huawei HCIA-AI Certification, AI Applications in Computer Vision., , , ]
Question 2
Which of the following is NOT a key feature that enables all-scenario deployment and collaboration for MindSpore?
Options:
A.
Data and computing graphs are transmitted to Ascend AI Processors.
B.
Federal meta-learning enables real-time, coordinated model updates between different devices, and across the device and cloud.
C.
Unified model IR delivers a consistent deployment experience.
D.
Graph optimization based on a software-hardware synergy shields the differences between scenarios.
Answer:
B
Explanation:
While MindSpore supports all-scenario deployment with features like data and computing graph transmission to Ascend AI processors, unified model IR for consistent deployment, and graph optimization based on software-hardware synergy, federal meta-learning is not explicitly a core feature of MindSpore's deployment strategy. Federal meta-learning refers to a distributed learning paradigm, but MindSpore focuses more on efficient computing and model optimization across different environments.
[Reference: Huawei HCIA-AI Certification, AI Development Framework – MindSpore Features., , ]
Question 3
Which of the following are feedforward neural networks?
Options:
A.
Fully-connected neural networks
B.
Recurrent neural networks
C.
Boltzmann machines
D.
Convolutional neural networks
Answer:
A, D
Explanation:
Feedforward neural networks (FNNs) are networks where information moves in only one direction—forward—from the input nodes through hidden layers to the output nodes. Both fully-connected neural networks (where each neuron in one layer connects to every neuron in the next) and convolutional neural networks (CNNs) (which have a specific architecture for image data) are examples of feedforward networks.
However, recurrent neural networks (RNNs) and Boltzmann machines are not feedforward networks. RNNs include loops where information can be fed back into previous layers, and Boltzmann machines involve undirected connections between units, making them a form of a stochastic network rather than a feedforward structure.