Pass the Huawei HCIA-AI H13-311_V3.5 Questions and answers with CertsForce

Huawei Cloud EIHealth is a comprehensive platform that offers AI-powered solutions across various healthcare-related fields such as:

Drug R&D: Accelerates drug discovery and development using AI.

Clinical research: Enhances research efficiency through AI data analysis.

Diagnosis and treatment: Provides AI-based diagnostic support and treatment recommendations.

Genome analysis: Uses AI to analyze genetic data for medical research and personalized medicine.

AI chips, also known as AI accelerators, are specialized hardware designed to enhance the performance of AI workloads, particularly for tasks like matrix multiplication, which is heavily used in machine learning and deep learning algorithms. These chips optimize operations like matrix multiplications because they are computationally intensive and central to neural network computations (e.g., in forward and backward passes).

HCIA AI References:

Cutting-edge AI Applications: Discussion of AI chips and accelerators, with a focus on their role in improving computation efficiency.

Deep Learning Overview: Explains how neural network operations like matrix multiplication are optimized in AI hardware.

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 References:

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.

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.

The mean squared error (MSE) loss function is primarily used for regression problems, where the goal is to minimize the difference between the predicted and actual continuous values. For classification problems, where the target output is categorical (e.g., binary or multi-class labels), loss functions like cross-entropy are more suitable, as they are designed to handle the probabilistic interpretation of outputs in classification tasks.

Using MSE for classification could lead to inefficient training because it doesn't capture the probabilistic relationships required for classification tasks.

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.

The Rectified Linear Unit (ReLU) activation function is defined as f(x)=max⁡(0,x)f(x) = \max(0, x)f(x)=max(0,x). In the positive interval, where x>0x > 0x>0, the derivative of ReLU is always 1. This makes ReLU popular for deep learning networks because it helps avoid the vanishing gradient problem during backpropagation, ensuring efficient gradient flow.