convolving

convolving的音标是[kən"vəʊlvɪŋ]，基本翻译是“卷绕，缠绕”，速记技巧是：con（共同）+volv（卷）+ing→共同卷绕。

Convolving这个词的词源可以追溯到拉丁语convolveo，意为“卷曲，缠绕”。它的变化形式包括动词的过去式和过去分词形式convulvell(o)，形容词形式convulval等。

相关单词：卷积（Convolution），这个词在数学和信号处理领域中常用，表示卷绕或缠绕的过程。它的英文解释为“the act or process of convolve”。这个词可以引申出其他相关单词，如 convolutional（卷积的，卷绕的），convolutional layer（卷积层）等，用于描述神经网络中的一种特定结构。

在句子中，Convolving可以表示卷绕、叠加等含义，例如在图像处理中，卷积可以用于处理图像数据，实现图像特征的提取和识别。

总之，Convolving这个词在英语中的使用非常广泛，特别是在数学、信号处理、计算机科学等领域中。它的词源、变化形式和相关单词都反映了它在这些领域中的重要性和广泛应用。

常用短语：

1. convolve with 与...相关联

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. convolution 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 convolutional neural network was trained on a large dataset and achieved excellent results in image recognition tasks.

2. The kernel function was convolve with the input signal to produce a new signal that was more suitable for processing.

3. The convolutional layer effectively captured the spatial relationships between adjacent pixels in the image.

4. The convolution operation is a fundamental tool in signal processing and machine learning.

5. Convolutional filters are commonly used for feature extraction in computer vision tasks.

6. Convolutional pooling effectively reduces the dimensionality of the data while preserving important information.

7. The convolutional neural network has revolutionized the field of machine learning and is now widely used in various applications.

英文小作文：

Convolutional Neural Networks in Image Recognition

Convolutional neural networks (CNNs) have revolutionized the field of image recognition, enabling machines to perform tasks that were once considered impossible for computers. By using convolutional layers to capture the spatial relationships between adjacent pixels, CNNs are able to learn complex patterns from images and perform well in various tasks, such as object detection, classification, and segmentation.

In this era of big data and high-performance computing, CNNs have become a popular choice for image recognition tasks due to their superior performance and adaptability to various data sets. By using convolutional filters to extract features from the input images, CNNs are able to efficiently process large amounts of data and achieve accurate results with minimal supervision. Furthermore, convolutional pooling layers are commonly used to reduce the dimensionality of the data while preserving important information for downstream tasks.

In conclusion, convolutional neural networks have revolutionized the field of image recognition and are now widely used in various applications, from computer vision to medical imaging and self-driving cars. With advances in technology and increasing availability of data, CNNs are poised to continue making a significant impact on the field of machine learning and beyond.