The nn module in MindSpore provides essential tools for building neural networks, including:

C. Optimizers: such as Momentum and Adam, which are used to adjust the weights of the model during training.

D. Loss functions: such as MSELoss (Mean Squared Error Loss) and SoftmaxCrossEntropyWithLogits, which are used to compute the difference between predicted and actual values.

The other options are incorrect because:

A. Hyperparameter search modes (like GridSearch and RandomSearch) are typically found in model training and tuning modules, but not in the nn module.

B. Model evaluation indicators like F1 Score and AUC are also handled by specific evaluation functions or libraries outside the nn module.

HCIA AI References:

AI Development Framework: Detailed coverage of MindSpore’s nn module, its optimizers, and loss functions.

Introduction to Huawei AI Platforms: Explains various MindSpore features, including network construction and training.

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.

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 References:

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.