convolves

convolves 的音标是[kən"vɑːlvz]，基本翻译是“卷曲；缠绕；交织”。

速记技巧是：可以将单词拆分成con（共同）+volv（环绕）+e，其中con表示“共同”，volv表示“环绕”的意思，e是单词的结尾，这样就可以快速记住单词的意思和拼写。

“convolves”这个词源可以追溯到拉丁语“convolvere”，意为“卷起”或“缠绕”。它的变化形式包括动词的过去式“convolvolve”和过去分词“convolvolve”以及形容词“convolvular”。

相关单词：

1. convolution - 卷曲，卷绕；卷积（数学术语）

2. convolutional - 卷曲的，卷绕的；卷积的，卷积层的

3. volute - 涡状物；螺旋形

4. involute - 螺旋形的；渐伸线

5. coils - 卷曲；盘绕

6. coiled - 卷曲的；盘绕的

7. spiral - 螺旋形的；盘旋上升的

8. whorl - 旋涡；旋环

9. coiling - 盘绕；螺旋上升

10. convoluted - 复杂的，难解的；卷曲的，缠绕的

“convolves”这个词在英语中常用于描述物体的卷曲或缠绕状态，以及数学中的卷积运算。这个词的含义和用法随着时间的推移而演变，反映了英语语言的发展和变化。

常用短语：

1. convolve with 与...有关联

2. convolutional layer 卷积层

3. convolutional neural network 卷积神经网络

4. convolve a function with a kernel 将函数与核函数相卷积

5. convolutional operation 卷积运算

6. convolutional filter 卷积滤波器

7. convolutional pooling 卷积池化

双语例句：

1. The image is convolved with a Gaussian kernel to enhance the edges.

该图像与高斯核进行卷积以增强边缘。

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 convolutional filter helps to extract features from the image that are relevant for the task at hand.

卷积滤波器有助于从图像中提取与当前任务相关的特征。

4. Convolutional pooling helps to reduce the dimensionality of the data while preserving important information.

卷积池化有助于降低数据的维度，同时保留重要的信息。

5. The convolutional operation is a key component of image processing and machine learning.

卷积运算是在图像处理和机器学习中一个关键的组成部分。

6. The convolutional layer is essential for building effective image classification models.

卷积层对于构建有效的图像分类模型至关重要。

7. Convolutional neural networks have revolutionized the field of machine learning, making it possible to achieve state-of-the-art results in a wide range of tasks.

卷积神经网络已经彻底改变了机器学习领域，使得在各种任务中取得最先进的结果成为可能。

英文小作文：

Convolutional Neural Networks in Image Processing

Convolutional neural networks (CNNs) have revolutionized the field of image processing, making it possible to achieve state-of-the-art results in a wide range of tasks, such as object recognition, scene understanding, and image generation. By using convolutional layers and pooling operations, CNNs are able to extract features from images that are relevant for the task at hand, while also being able to handle complex spatial relationships between different parts of the image.

In this way, CNNs have become one of the most powerful tools in machine learning for dealing with image data. They are used in various applications, such as computer vision, medical imaging, and self-driving cars, where they are able to perform tasks that were previously impossible or very difficult using traditional machine learning methods. However, despite their widespread use, there is still much research to be done on CNNs, as they still have limitations and can be prone to overfitting when used with large amounts of data. Therefore, it is essential to continue exploring new techniques and methods to improve the performance and applicability of CNNs in various domains.