Practice Free H13-311_V3.5 Exam Online Questions
In a neural network based on connectionism, each node can express a specific meaning.
- A . TRUE
- B . FALSE
Deep learning algorithms can be divided into supervised learning and unsupervised learning.
- A . True
- B . False
Which of the following is not the difference between Python 2 and Python 3?
- A . print
- B . Unicode
- C . import
- D . xrange
Which of the following options belong to Tensorflow concept? (Multiple Choice)
- A . Tensor
- B . Variables
- C . Placeholder
- D . Operation
- E . Conversation
Which of the following is not an artificial intelligence school?
- A . Symbolism
- B . StatJst1caltsm
- C . Behaviorism
- D . Connectionism
About Bayesian formula- P(WlX)=P(XlW)*P(W)/P(X) What is the correct description?
- A . P(WIX) is a prior probability
- B . P(XIW) 1s a conditional probability
- C . P(W) is the posterior probability
- D . P(X) is the posterior probability
In the face search service, if we want to delete a certain face set, we can use this code: firs_cliet.get_v2().get_face_set_service().delete_face_set("**"), among them "**" is to fill in the actual face set name.
- A . TRUE
- B . FALSE
In the process of training the neural network, we use the gradient descent method to continuously update which value, which makes the loss Function minimization?
- A . Number of samples
- B . Eigenvalues
- C . Hyperparameter
- D . parameter
In the deep learning network, the backpropagation algorithm is used to find the optimal parameters.
What rules are used in the backpropagation algorithm to obtain the derivation layer by layer?
- A . Chain rule
- B . Cumulative rule
- C . Law of reciprocity
- D . Rule of Normalization
An algorithm of unsupervised learning classifies samples in a dataset into several categories.
Samples belonging to the same category have high similarity.
- A . TRUE
- B . FALSE
A
Explanation:
In unsupervised learning, the goal is to find hidden patterns or intrinsic structures in input data without labeled outcomes. One common unsupervised learning task is clustering, where an algorithm groups the dataset into several categories or clusters. Samples within the same cluster have high similarity based on certain features, while samples in different clusters have low similarity. Examples of clustering algorithms include k-means and hierarchical clustering.
Reference: Huawei HCIA-AI Certification, Machine Learning Overview.