Course Intermediate  11361

Course Introduction："Self-study IT Network Linux Load Balancing Video Tutorial" mainly implements Linux load balancing by performing script operations on web, lvs and Linux under nagin.

Course Advanced  17663

Course Introduction："Shangxuetang MySQL Video Tutorial" introduces you to the process from installing to using the MySQL database, and introduces the specific operations of each link in detail.

Course Advanced  11380

Course Introduction："Brothers Band Front-end Example Display Video Tutorial" introduces examples of HTML5 and CSS3 technologies to everyone, so that everyone can become more proficient in using HTML5 and CSS3.

Course Introduction：Causal convolutional neural network is a special convolutional neural network designed for causality problems in time series data. Compared with conventional convolutional neural networks, causal convolutional neural networks have unique advantages in retaining the causal relationship of time series and are widely used in the prediction and analysis of time series data. The core idea of ​​causal convolutional neural network is to introduce causality in the convolution operation. Traditional convolutional neural networks can simultaneously perceive data before and after the current time point, but in time series prediction, this may lead to information leakage problems. Because the prediction results at the current time point will be affected by the data at future time points. The causal convolutional neural network solves this problem. It can only perceive the current time point and previous data, but cannot perceive future data.

2024-01-24 comment 0  900

Course Introduction：In neural networks, filters usually refer to convolution kernels in convolutional neural networks. The convolution kernel is a small matrix used to perform convolution operations on the input image to extract features in the image. The convolution operation can be regarded as a filtering operation. By performing a convolution operation on the input data, the spatial structure information in the data can be captured. This operation is widely used in the fields of image processing and computer vision, and can be used for tasks such as edge detection, feature extraction, and target recognition. By adjusting the size and weight of the convolution kernel, the characteristics of the filter can be changed to adapt to different feature extraction needs. In a convolutional neural network, each convolutional layer contains multiple filters, and each filter is responsible for extracting different features. These features can be used to identify objects, textures, edges, etc. in images

2024-01-23 comment 0  993

Course Introduction：Convolutional neural networks perform well in image denoising tasks. It utilizes the learned filters to filter the noise and thereby restore the original image. This article introduces in detail the image denoising method based on convolutional neural network. 1. Overview of Convolutional Neural Network Convolutional neural network is a deep learning algorithm that uses a combination of multiple convolutional layers, pooling layers and fully connected layers to learn and classify image features. In the convolutional layer, the local features of the image are extracted through convolution operations, thereby capturing the spatial correlation in the image. The pooling layer reduces the amount of calculation by reducing the feature dimension and retains the main features. The fully connected layer is responsible for mapping learned features and labels to implement image classification or other tasks. The design of this network structure makes convolutional neural networks useful in image processing and recognition.

2024-01-23 comment 0  1312

Course Introduction：Convolutional neural network (CNN) is a deep learning model widely used in computer vision tasks. Compared with fully connected neural networks, CNN has fewer parameters and more powerful feature extraction capabilities, and performs well in tasks such as image classification, target detection, and image segmentation. Below we will introduce how to build a basic CNN model. Convolutional Neural Network (CNN) is a deep learning model with multiple convolutional layers, pooling layers, activation functions and fully connected layers. The convolutional layer is the core component of CNN and is used to extract features of the input image. The pooling layer can reduce the size of the feature map and preserve the main features of the image. The activation function introduces nonlinear transformation and increases the model

2024-01-24 comment 0  521

Course Introduction：In a fully convolutional neural network (FCN), basically for each layer, there is a random weight initialization. And there are two points to note: Fully Convolutional Neural Networks (FCN) will not use 0 as weights during backpropagation. This is because when calculating the gradient dL/dX of the intermediate layer, if the weight is set to 0, the gradient will become 0, causing the network to fail to update. Therefore, FCN usually uses non-zero weights to ensure efficient calculation and update of gradients. In order to avoid using a single constant to initialize all weights of a fully convolutional neural network (FCN), we can use some more complex methods. A common approach is to use random initialization, which initializes the weights to random decimal values. In this way, each neuron will have a different

2024-01-23 comment 0  1082