H13-311_V3.5 Exam Dumps - HCIA-AI V3.5 Exam

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.

AI chips, also known as AI accelerators, are specialized hardware designed to enhance the performance of AI workloads, particularly for tasks like matrix multiplication, which is heavily used in machine learning and deep learning algorithms. These chips optimize operations like matrix multiplications because they are computationally intensive and central to neural network computations (e.g., in forward and backward passes).

HCIA AI References:

Cutting-edge AI Applications: Discussion of AI chips and accelerators, with a focus on their role in improving computation efficiency.

Deep Learning Overview: Explains how neural network operations like matrix multiplication are optimized in AI hardware.

Atlas is a key part of Huawei's full-stack, all-scenario AI solution. It provides AI hardware resources in the form of modules, boards, edge stations, and servers powered by Huawei's Ascend AI processors. The Atlas series is designed to meet customer demands for AI computing power in a variety of deployment scenarios, including cloud, edge, and device environments.

Huawei’s full-stack AI solution aims to deliver comprehensive AI capabilities across different levels. The Atlas series supports a wide range of industries by offering scalable AI computing resources, which are critical for industries dealing with large volumes of data and needing high-performance computing.

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.

In standard Recurrent Neural Networks (RNNs), the Tanh activation function is commonly used in the hidden layers. The Tanh function squashes input values to a range between -1 and 1, allowing the network to learn complex patterns over time by transforming the input data into non-linear patterns.

While other activation functions like Sigmoid can be used, Tanh is preferred in many RNNs for its wider range. ReLU is generally used in feed-forward networks, and Softmax is often applied in the output layer for classification problems.

HCIA AI References:

Deep Learning Overview: Describes the architecture of RNNs, highlighting the use of Tanh as the standard activation function.

AI Development Framework: Discusses the various activation functions used across different neural network architectures.

The pad_mode parameter in mindspore.nn.Conv2d() can take values such as:

same: Ensures the output has the same spatial dimensions as the input.

valid: Performs convolution without padding, resulting in an output smaller than the input.

Other values like "pad" and "nopadding" are not valid options for the pad_mode parameter.

In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.

However, the same kernel’s weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.

Thus, the statement that all kernels share weights is false.

HCIA AI References:

Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.

While MindSpore supports all-scenario deployment with features like data and computing graph transmission to Ascend AI processors, unified model IR for consistent deployment, and graph optimization based on software-hardware synergy, federal meta-learning is not explicitly a core feature of MindSpore's deployment strategy. Federal meta-learning refers to a distributed learning paradigm, but MindSpore focuses more on efficient computing and model optimization across different environments.

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 References:

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.

In MindSpore, using ops.Ones() with a specified shape and dtype=mindspore.float32 will create a tensor of ones with floating-point values. The output will be a 2x2 matrix filled with 1.0 values. The floating-point format (with a decimal point) ensures that the output is in the form of [[1. 1.], [1. 1.]].

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.

In MindSpore, the neural network structure is defined by inheriting the Cell class, which represents a computational node or a layer in the network. Users can customize the network by overriding the __init__ method (for initializing layers) and the construct method (for defining the forward pass of the network). This modular design allows for easy and flexible neural network construction.

Thus, the statement is true because MindSpore's framework allows developers to build neural networks by extending the Cell class and defining custom behavior through the __init__ and construct methods.

HCIA AI References:

AI Development Framework: Detailed coverage of building neural networks in MindSpore, including how to inherit from the Cell class and rewrite key methods for custom network architecture.

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 References:

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.

While speech recognition technology has improved significantly, its accuracy can still be affected by external factors such as noise, background sound, accents, and speech clarity. Although systems can achieve over 90% accuracy under controlled conditions, the accuracy drops in noisy or complex real-world environments. Therefore, the statement that today's speech processing technology can always achieve high recognition accuracy is incorrect.

Speech recognition systems are sophisticated but still face challenges in environments with heavy noise, where the technology has difficulty interpreting speech accurately.

The statement that mini-batch gradient descent (MBGD) takes less time than stochastic gradient descent (SGD) to complete an epoch when GPUs are used for parallel computing is incorrect. Here’s why:

Stochastic Gradient Descent (SGD) updates the weights after each training sample, which can lead to faster updates but more noise in the gradient steps. It completes an epoch after processing all samples one by one.

Mini-batch Gradient Descent (MBGD) processes small batches of data at a time, updating the weights after each batch. While MBGD leverages the computational power of GPUs effectively for parallelization, the comparison made in this question is not about overall computation speed, but about completing an epoch.

MBGD does not necessarily complete an epoch faster than SGD, as MBGD processes multiple samples in each batch, meaning fewer updates per epoch compared to SGD, where weights are updated after every individual sample.

Therefore, the correct answer is B. FALSE, as MBGD does not always take less time than SGD for completing an epoch, even when GPUs are used for parallelization.

HCIA AI References:

AI Development Framework: Discussion of gradient descent algorithms and their efficiency on different hardware architectures like GPUs.