Perceptron as a basic model of artificial neural network, its unique significance is()
A. Groundbreaking work of artificial neural network for solving linear classification problems
B. It is precisely because of the defect that cannot solve the nonlinear classification problem that inspired various artificial neural network models later
C. Because it is simple and easy to understand, as an entry model for artificial neural network learning is appropriate
D. The perceptron aims to find separate hyperplanes that divide the instances in the input space into two categories.
查看答案
The three core parts of the BP neural network learning algorithm are()
A. weight adjustment
B. output layer connection right adjustment
C. hidden layer connection right adjustment
D. Input layer connection right adjustment
For convolutional networks, the following statement is correct ()
A. The role of the fully connected layer is to classify. Each node of the fully connected layer is connected to all the nodes of the previous layer, and the features of the previous layer are further combined.
B. The layers in front of the convolutional neural network map the low-dimensional features of the image to the abstract feature space, and the fully connected layer maps the abstract features to the sample label space.
C. The function of the loss function is a function used to estimate the gap between the predicted value and the actual value of the model
D. The pooling layer in the convolutional neural network is also called the down sampling layer. The purpose of this layer is to reduce the dimension of the feature map, thereby reducing the number of parameters of the convolutional neural network and reducing overfitting.
Deep Convolutional Neural Networks have been widely used in ()
A. target image acquisition
B. target extraction
C. target recognition
D. target classification
The reason why deep learning uses a neural network such as a hierarchical network structure is ()
A. Visual hierarchy, in which the learning of attributes is a combination mapping that makes categories into attributes.
B. Structural (or semantic) high-level features are more meaningful for classification.
C. High-level features or images are often composed of some basic structures (shallow features).
D. Adopt a hierarchical network structure to achieve structural feature representation, and different features are represented by different structures.
微信支付 
支付宝支付