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.