Huawei H13-311_V3.5 Practice Test - Questions Answers, Page 4

In a fully-connected layer, the number of parameters is calculated by multiplying the number of input features by the number of neurons in the hidden layer. For an image of resolution 100100=10,000100 \times 100 = 10,000100100=10,000 pixels and a hidden layer of 1,000 neurons, the total number of parameters is 10,0001,000=1,000,00010,000 \times 1,000 = 1,000,00010,0001,000=1,000,000.

The global gradient descent algorithm evaluates the gradient over the entire dataset before each update, leading to accurate but slow convergence, especially for large datasets. In contrast, stochastic gradient descent updates the model parameters more frequently, which allows for faster convergence but with noisier updates. While batch gradient descent updates the parameters based on smaller batches of data, none of these algorithms can fully guarantee finding the global minimum in non-convex problems, where local minima may exist.

Activation functions like Sigmoid, tanh, and softsign suffer from the vanishing gradient problem when used in deep networks. This happens because, in these functions, gradients become very small as the input moves away from the origin (either positively or negatively). As a result, the weights of the earlier layers in the network receive very small updates, hindering the learning process in deep networks. This is one reason why activation functions like ReLU, which avoid this issue, are often preferred in deep learning.

Both single-layer perceptrons and logistic regression are linear classifiers, meaning they are capable of separating data that is linearly separable. However, they cannot effectively model non-linear relationships in the data. For more complex, non-linearly separable data, multi-layer neural networks or other non-linear classifiers are required.

Nesterov Accelerated Gradient (NAG) is indeed a variant of the momentum optimizer. In the traditional momentum method, the gradient is used to adjust the direction based on the current momentum. Nesterov, on the other hand, anticipates the change in the momentum by calculating the gradient at a slightly altered position. This small adjustment leads to better convergence and more efficient optimization, especially in non-convex problems.

Momentum methods and their variants like Nesterov are commonly discussed in the optimization strategies for neural networks, including frameworks such as TensorFlow, which is covered in Huawei's HCIA AI courses.

HCIA AI

Deep Learning Overview: Discussion of optimization algorithms, including gradient descent variants like Momentum and Nesterov.

AI Development Framework: Explains the use of Nesterov in deep learning frameworks such as TensorFlow and PyTorch.

Contrary to the statement, Convolutional Neural Networks (CNNs) can indeed be used to process text data. While CNNs are most famously used for image processing, they can also be adapted for natural language processing (NLP) tasks. In text data, CNNs can operate on word embeddings or character-level data to capture local patterns (e.g., sequences of words or characters). CNNs are used in applications such as text classification, sentiment analysis, and language modeling.

The key to CNN's application in text processing is that the convolutional layers can detect patterns in sequences, much like they detect spatial features in images. This versatility is covered in Huawei's HCIA AI platform when discussing CNN's applications beyond image data.

HCIA AI

Deep Learning Overview: Explores the usage of CNNs in different domains, including their application in NLP tasks.

Cutting-edge AI Applications: Discusses the use of CNNs in non-traditional tasks, including text and sequential data processing.

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

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.

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.