convolutions

convolutions的音标为：[kənˈvəʊləʊʃnz]，翻译为“卷积；云雾缭绕；缠绕；重叠”。

速记技巧：可以将单词拆分记忆，注意卷字的主元音，从而与音标中的元音对应。同时， convolution中的每部分都可以联想到卷曲、缠绕等形象，有助于记忆单词的意思。

Convolution的英文词源可以追溯到拉丁语和法语，意为“卷曲”或“褶皱”。这个词在计算机科学和人工智能领域中通常指卷积神经网络（Convolutional Neural Networks），这是一种特殊的神经网络结构，特别适用于处理图像等具有卷积结构的数据。

变化形式：Convolution在英语中通常写作复数形式，即convolutions。

相关单词：

1. Convolve（动词）：意为“使卷曲；使交织”。例如，The two curves are convolve with each other，表示这两条曲线相互卷曲交织。

2. Convolutional（形容词）：意为“卷曲的；褶皱的；褶叠的”。例如，Convolutional neural networks are a type of neural network that is particularly suitable for processing images with convolutional structure。

3. Deconvolution（动词/名词）：意为“解卷；解褶；解卷积”。例如，Deconvolution of the blurring effect in images can be achieved through various techniques。

4. Multidimensional convolution（名词）：意为“多维卷积”。

5. Filter convolution（名词）：意为“滤波器卷积”。

6. Kernel convolution（名词）：意为“核卷积”。

7. Inner convolution（名词）：意为“内部卷积”。

8. Boundary convolution（名词）：意为“边界卷积”。

9. Convolution matrix（名词）：意为“卷积矩阵”。

以上这些单词都与convolution有着密切的联系，并且是在计算机科学和人工智能领域中常用的术语。

常用短语：

1. convolutional neural networks (CNN)

2. feature extraction

3. spatial filtering

4. pooling layer

5. batch normalization

6. dropout

7. training set

双语例句：

1. CNNs have been widely used in image recognition tasks. (CNN已被广泛应用于图像识别任务中。)

2. The convolution operation helps extract useful features from the input data. (卷积操作有助于从输入数据中提取有用的特征。)

3. Training a model with a large batch size can improve the accuracy and speed of the network. (使用较大的批量大小训练模型可以提高网络的准确性和速度。)

4. Dropout is a regularization technique that helps prevent overfitting. (Dropout是一种有助于防止过拟合的正则化技术。)

5. The convolutional layer helps to capture the spatial relationships between adjacent pixels. (卷积层有助于捕捉相邻像素之间的空间关系。)

6. Feature extraction is crucial for effective machine learning models. (特征提取对于有效的机器学习模型至关重要。)

7. Pooling layers are used to reduce the dimensionality of the data without losing too much information. (池化层用于降低数据的维度，而不会丢失太多信息。)

英文小作文：

Convolutional Neural Networks (CNNs) are a type of artificial intelligence model that has revolutionized the field of computer vision. They are particularly effective at recognizing patterns and extracting features from images, videos, and other types of data. CNNs are composed of convolutional layers, which use a type of spatial filtering to extract useful features from the input data, and pooling layers, which reduce the dimensionality of the data without losing too much information. These layers are trained using a variety of techniques, including batch normalization, dropout, and regularization methods such as weight decay and L1/L2 regularization.

One of the most exciting applications of CNNs is in deep learning image classification, where they have achieved state-of-the-art results in many challenging datasets. By training a CNN on a large dataset, one can obtain a powerful model that can accurately classify images into different categories, even in domains where traditional machine learning methods struggle. CNNs have also been used in other areas of computer vision, such as object detection and segmentation, where they have achieved impressive results in real-world applications.

In conclusion, CNNs are a powerful tool for machine learning and computer vision that has revolutionized the field. They are particularly effective at extracting features from images and other types of data, and have been used to achieve state-of-the-art results in many challenging applications.