"AI application fields include only computer vision and speech processing." Which of the following is true about this statement?
AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.
When feature engineering is complete, which of the following is not a step in the decision tree building process?
When building a decision tree, the steps generally involve:
Decision tree generation: This is the process where the model iteratively splits the data based on feature values to form branches.
Pruning: This step occurs post-generation, where unnecessary branches are removed to reduce overfitting and enhance generalization.
Feature selection: This is part of decision tree construction, where relevant features are selected at each node to determine how the tree branches.
Data cleansing, on the other hand, is a preprocessing step carried out before any model training begins. It involves handling missing or erroneous data to improve the quality of the dataset but is not part of the decision tree building process itself.
HCIA AI
Machine Learning Overview: Includes a discussion on decision tree algorithms and the process of building decision trees.
AI Development Framework: Highlights the steps for building machine learning models, separating data preprocessing (e.g., data cleansing) from model building steps.
In MindSpore, the basic unit of the neural network is nn.Cell.
In MindSpore, nn.Cell is the basic unit of a neural network. It represents layers, models, and other neural network components, encapsulating the forward logic of the network. It allows users to define, organize, and manage neural network layers in MindSpore, making it a core building block in neural network construction.
Which of the following are common gradient descent methods?
The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:
Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.
Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent.
Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.
Multi-dimensional gradient descent is not a recognized method in AI or machine learning.
Which of the following are use cases of generative adversarial networks?
Generative Adversarial Networks (GANs) are widely used in several creative and image generation tasks, including:
A . Photo repair: GANs can be used to restore missing or damaged parts of images.
B . Generating face images: GANs are known for their ability to generate realistic face images.
C . Generating a 3D model from a 2D image: GANs can be used in applications where 2D images are converted into 3D models.
D . Generating images from text: GANs can also generate images based on text descriptions, as seen in tasks like text-to-image synthesis.
All of the provided options are valid use cases of GANs.
HCIA AI
Deep Learning Overview: Discusses the architecture and use cases of GANs, including applications in image generation and creative content.
AI Development Framework: Covers the role of GANs in various generative tasks across industries.
