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In a hyperparameter-based search, the hyperparameters of a model are searched based on the data on and the model's performance metrics.
In machine learning, hyperparameters are the parameters that govern the learning process and are not learned from the data. Hyperparameter optimization or hyperparameter tuning is a critical part of improving a model's performance. The goal of a hyperparameter-based search is to find the set of hyperparameters that maximizes the model's performance on a given dataset.
There are different techniques for hyperparameter tuning, such as grid search, random search, and more advanced methods like Bayesian optimization. The performance of the model is assessed based on evaluation metrics (like accuracy, precision, recall, etc.), and the hyperparameters are adjusted accordingly to achieve the best performance.
In Huawei's HCIA AI curriculum, hyperparameter optimization is discussed in relation to both traditional machine learning models and deep learning frameworks. The course emphasizes the importance of selecting appropriate hyperparameters and demonstrates how frameworks such as TensorFlow and Huawei's ModelArts platform can facilitate hyperparameter searches to optimize models efficiently.
HCIA AI
AI Overview and Machine Learning Overview: Emphasize the importance of hyperparameters in model training.
Deep Learning Overview: Highlights the role of hyperparameter tuning in neural network architectures, including tuning learning rates, batch sizes, and other key parameters.
AI Development Frameworks: Discusses the use of hyperparameter search tools in platforms like TensorFlow and Huawei ModelArts.
What are the application scenarios of computer vision?
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.
Which of the following algorithms presents the most chaotic landscape on the loss surface?
Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.
In machine learning, which of the following inputs is required for model training and prediction?
In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables. While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.
Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.
When learning the MindSpore framework, John learns how to use callbacks and wants to use it for AI model training. For which of the following scenarios can John use the callback?
In MindSpore, callbacks can be used in various scenarios such as:
Early stopping: To stop training when the performance plateaus or certain criteria are met.
Saving model parameters: To save checkpoints during or after training using the ModelCheckpoint callback.
Monitoring loss values: To keep track of loss values during training using LossMonitor, allowing interventions if necessary.
Adjusting the activation function is not a typical use case for callbacks, as activation functions are usually set during model definition.
