H13-311_V3.5 Exam Dumps : HCIA-AI V3.5 Exam

In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.

However, the same kernel’s weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.

Thus, the statement that all kernels share weights is false.

HCIA AI References:

Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.

In MindSpore, when you define a tensor using Tensor(np.array([[1, 2], [3, 4]]), dtype.int32), the dtype attribute returns the data type of the tensor, which in this case is mindspore.int32. This specifies the type of elements in the tensor.

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.