Huawei H13-311_V3.5 Exam Questions

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.

Huawei Cloud ModelArts provides ModelBox, a tool for device-edge-cloud joint development, enabling efficient deployment across multiple environments. Some of its key optimization policies include:

Hardware affinity: Ensures that the models are optimized to run efficiently on the target hardware.

Operator optimization: Improves the performance of AI operators for better model execution.

Automatic segmentation of operators: Automatically segments operators for optimized distribution across devices, edges, and clouds.

Model replication is not an optimization policy offered by ModelBox.

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.

This statement is false because not all feedforward neural networks follow this architecture. While fully-connected layers do have this type of connectivity (where each neuron is connected to all neurons in the previous layer), feedforward networks can include layers like convolutional layers, where not every neuron is connected to all previous neurons. Convolutional layers, common in convolutional neural networks (CNNs), only connect to a local region of the input, preserving spatial information.