convolute

convolute的音标是[kənˈvəʊlɪt]，意思是“卷曲；卷绕；缠绕”。基本翻译为“使缠绕；使混乱；使混淆”，速记技巧为“卷绕的卷绕”。

"convolute"这个词的词源可以追溯到拉丁语"convolvere"，意为“卷起”或“缠绕”。其变形形式为"convolve"和"convoluted"，前者表示动词形式，表示“卷曲”或“缠绕”，后者表示形容词形式，表示“复杂的”或“难解的”。

相关单词：

"complex"：这个词与"convolute"有相似的含义，都表示复杂的。它可以指复杂的情感、思想或情况，也可以指由多个部分组成的物体。

"entangle"：这个词表示“缠绕”或“陷入复杂情况中”。它可以用来描述复杂的网络、关系或问题。

"twist"：这个词表示“缠绕”或“扭曲”，可以用来描述物质的形状或事件的发展方向。

" convolution"：这个词是数学和计算机科学中的一个术语，指的是卷积运算，是一种在信号处理和图像处理中常用的技术。

"compromise"：这个词表示妥协，与"convolute"的复杂和困难的意思有相似之处。它表示在冲突或困难的情况下找到一种折中的解决方案。

以上是对"convolute"这个词及其相关单词的解释，希望对你有所帮助。

常用短语：

1. convolutional neural network (CNN)

2. apply convolution

3. filter bank

4. deconvolution

5. kernel function

6. convolutional layer

7. pooling layer

双语例句：

1. The image is convoluted after being through the convolution process. (经过卷积过程后，图像变得模糊不清。)

2. The convolutional neural network was trained to recognize patterns in the data. (卷积神经网络被训练用于识别数据中的模式。)

3. The filter bank provided a way to decompose the signal into different frequency bands. (滤波器组提供了一种将信号分解成不同频率带的方法。)

4. After deconvolution, the image became clearer and more detailed. (去卷积后，图像变得更加清晰和详细。)

5. The kernel function determines the strength of the convolution effect. (核函数决定了卷积效应的强度。)

6. The convolutional layer extracted features from the input data. (卷积层从输入数据中提取特征。)

7. Pooling reduces the dimensionality of the data by aggregating information from different parts of the image. (池化通过聚合图像不同部分的信息来降低数据的维度。)

英文小作文：

Convolution is a fundamental operation in signal processing and machine learning, which can be used to extract patterns and features from data. When applied to images, convolution can smoothen edges and reduce noise, making the image clearer and more detailed. Convolutional neural networks, which are a type of convolutional layer-based machine learning model, have become popular in recent years for their ability to automatically learn representations from data and perform complex tasks such as image classification and object detection. In this context, convolution plays a crucial role in the process of training and improving these models. However, it is also important to carefully design the kernel function and filter bank to achieve optimal results in convolutional operations. Therefore, convolution is a powerful tool that can be used in various contexts to extract useful information from data and improve the quality of the results obtained.