Most Recent Huawei H13-311_V3.5 Exam Dumps

This statement is false because not all feedforward neural networks follow this architecture. While fully-connected layers do have this type of connectivity (where each neuron is connected to all neurons in the previous layer), feedforward networks can include layers like convolutional layers, where not every neuron is connected to all previous neurons. Convolutional layers, common in convolutional neural networks (CNNs), only connect to a local region of the input, preserving spatial information.

HarmonyOS provides AI capabilities not only through HMS Core (Huawei Mobile Services Core), but also through other system-level integrations and AI frameworks. While HMS Core is one way to offer AI functionalities, HarmonyOS also has native support for AI processing that can be accessed by external systems or applications beyond HMS Core.

Thus, the statement is false as AI capabilities are not limited solely to HMS Core in HarmonyOS.

HCIA AI

Introduction to Huawei AI Platforms: Covers HarmonyOS and the various ways it integrates AI capabilities into external systems.

Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:

Expert systems, which are computer programs that mimic the decision-making abilities of a human expert by reasoning through bodies of knowledge. These systems are used in various domains such as healthcare, engineering, and finance.

Computer vision, which enables machines to interpret and understand visual data from the world. It includes tasks such as object detection, image recognition, and video analysis.

While options like backpropagation and smart finance are related to AI, they represent specific algorithms or application areas rather than broad subfields.

Both single-layer perceptrons and logistic regression are linear classifiers, meaning they are capable of separating data that is linearly separable. However, they cannot effectively model non-linear relationships in the data. For more complex, non-linearly separable data, multi-layer neural networks or other non-linear classifiers are required.

In standard Recurrent Neural Networks (RNNs), the Tanh activation function is commonly used in the hidden layers. The Tanh function squashes input values to a range between -1 and 1, allowing the network to learn complex patterns over time by transforming the input data into non-linear patterns.

While other activation functions like Sigmoid can be used, Tanh is preferred in many RNNs for its wider range. ReLU is generally used in feed-forward networks, and Softmax is often applied in the output layer for classification problems.

HCIA AI

Deep Learning Overview: Describes the architecture of RNNs, highlighting the use of Tanh as the standard activation function.

AI Development Framework: Discusses the various activation functions used across different neural network architectures.