H13-311_V3.5 Questions and Answers

Feedforward neural networks (FNNs) are networks where information moves in only one direction—forward—from the input nodes through hidden layers to the output nodes. Both fully-connected neural networks (where each neuron in one layer connects to every neuron in the next) and convolutional neural networks (CNNs) (which have a specific architecture for image data) are examples of feedforward networks.

However, recurrent neural networks (RNNs) and Boltzmann machines are not feedforward networks. RNNs include loops where information can be fed back into previous layers, and Boltzmann machines involve undirected connections between units, making them a form of a stochastic network rather than a feedforward structure.

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.

A. TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.B. FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy.C. TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.D. TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.

HCIA AI References:

Machine Learning Overview: Covers decision tree algorithms and their use cases.

Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.

Logistic regression is not a solution for underfitting in regression models, as it is used primarily for classification problems rather than regression tasks. If underfitting occurs, it means that the model is too simple to capture the underlying patterns in the data. Solutions include using a more complex regression model like polynomial regression or increasing the number of features in the dataset.

Other options like adding a regularization term for overfitting (Lasso or Ridge) and using data cleansing and feature engineering are correct methods for improving model performance.

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.

OpenCV is a library used primarily for computer vision tasks like image and video processing. It is not considered an AI computing framework in the same way as PyTorch, MindSpore, or TensorFlow, which are commonly used frameworks for developing AI and machine learning models. AI frameworks like PyTorch, TensorFlow, and Huawei’s MindSpore are designed to facilitate the development and deployment of deep learning models.

To correctly connect the layers in the architecture of an Ascend AI Processor, here’s a typical hierarchy and explanation for each layer:

Computing resources: This is generally considered as the lowest level in an AI processor architecture, directly related to the physical hardware. It should be matched with L0, which represents the hardware layer where the basic computational resources are managed.

Chip enablement: This typically involves firmware or low-level software that directly interacts with the chip hardware to enable its functionalities. This layer facilitates the operation of the hardware and is vital for the execution of any higher-level processes. It should be linked with L1.

Execution framework: This is the middle layer where higher-level frameworks that facilitate the development and execution of AI models operate. Frameworks like TensorFlow, PyTorch, or Huawei’s MindSpore would operate at this level. Connect this to L2.

Application enablement: This is the topmost layer and involves the use of AI capabilities within application-specific contexts, often utilizing the frameworks and computing resources provided by the lower layers. It should be matched with L3, indicating the highest level of abstraction in the architecture focused on applications.

This hierarchy reflects how each layer builds upon the lower ones to enable more complex functionalities and user interactions with AI technologies in processors like Huawei's Ascend.

Both Sigmoid and Tanh activation functions can cause the vanishing gradient problem. This issue occurs because these functions squash their inputs into a very small range, leading to very small gradients during backpropagation, which slows down learning. In deep neural networks, this can prevent the weights from updating effectively, causing the training process to stall.

Sigmoid: Outputs values between 0 and 1. For large positive or negative inputs, the gradient becomes very small.

Tanh: Outputs values between -1 and 1. While it has a broader range than Sigmoid, it still suffers from vanishing gradients for larger input values.

ReLU, on the other hand, does not suffer from the vanishing gradient problem since it outputs the input directly if positive, allowing gradients to pass through. However, Softplus is also less prone to this problem compared to Sigmoid and Tanh.

HCIA AI References:

Deep Learning Overview: Explains the vanishing gradient problem in deep networks, especially when using Sigmoid and Tanh activation functions.

AI Development Framework: Covers the use of ReLU to address the vanishing gradient issue and its prevalence in modern neural networks.

In MindSpore, when you define a tensor using Tensor(np.array([[1, 2], [3, 4]]), dtype.int32), the dtype attribute returns the data type of the tensor, which in this case is mindspore.int32. This specifies the type of elements in the tensor.

Based on the image you've described, here's how the inputs and outputs of a Generative Adversarial Network (GAN) should be matched:

Gaussian white noise vector: This is typically used as an Input to the generator in GANs. The generator uses this random noise vector to produce synthetic data.

Sample data vector: This is an Input to the discriminator. The discriminator in a GAN receives either real data from the training set or fake data generated by the generator to determine its authenticity.

Real sample data or generated sample data: This is the Output from the generator. The generator creates synthetic data that mimics the real training data, which is then fed into the discriminator.

True or false: This is the Output from the discriminator. The discriminator outputs a judgment about whether the input data it received (either from real datasets or generated by the generator) is real (true) or fake (false).

These matches align with how GANs are designed to operate, with the generator creating data and the discriminator evaluating it.

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.

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.

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.