convolvuli

convolvuli的音标为[kən"vɒlvjʊɪ]，基本翻译为“旋花科植物；旋花属植物”。

速记技巧：可以将单词拆分记忆，如“con-volvuli”，其中“con”可以联想到“一起”的意思，“volvuli”可以谐音联想到“乌龟累”，这样可以将单词记忆得更加深刻。

Convolvuli这个词来源于拉丁语，意为“卷曲的”，这个词来源于动词convolvere，意为“卷起，缠绕”。

变化形式：在词尾加-vuli，构成了该词的名词形式。

相关单词：

1. convolution - 卷曲，卷绕；卷层；复层结构。这个词可以指代数学中的卷积，也可以用来描述物质的卷曲或缠绕状态。

2. involution - 退化，渐变；自返曲线。这个词指的是一种数学上的概念，表示一种逐渐变化或退化的过程。

3. volute - 螺旋形；涡卷形。这个词来源于希腊语，用来描述螺旋或涡卷的形状，也可以用来描述艺术作品或建筑物的形状。

4. coils - 卷曲；盘绕；螺旋形物。这个词用来描述物体被卷曲或盘绕的状态，也可以用来描述像螺旋形的物体或形状。

5. entwine - 缠绕；交织。这个词用来描述两个或多个物体之间的缠绕或交织状态，也可以用来描述情感或思想之间的相互交织。

6. entwined - 缠绕的；交织的。这个词用来描述两个或多个物体之间被缠绕或交织的状态，也可以用来描述情感或思想之间的相互纠缠。

7. coiling - 盘绕；螺旋形运动。这个词用来描述物体被盘绕或螺旋形运动的状态，也可以用来描述生物体的生长方式。

8. convolutional - 卷曲的；卷绕的；适用于卷积的。这个词用来描述一种适用于卷积的算法或结构，也可以用来描述物质的卷曲或缠绕状态。

9. convolutional layer - 卷积层；卷积网络层。这个词用来描述一种在深度学习中常用的网络层，它通过卷积操作来提取图像或其他二维数据的特征。

10. convolutional neural network - 卷积神经网络。这个词用来描述一种使用卷积层的神经网络，它广泛应用于图像识别、语音识别等任务中。

常用短语：

1. convolutional neural networks (CNN)

2. feature extraction

3. spatial convolution

4. pooling layer

5. training dataset

6. validation dataset

7. test dataset

双语例句：

1. The convolutional neural network achieved excellent results on the classification task.

2. The feature extraction process extracted important information from the image data.

3. The spatial convolution operation resulted in a more accurate representation of the input data.

4. The pooling layer helped to reduce the dimensionality of the data while maintaining its essential features.

5. The training dataset was carefully selected to maximize the network"s performance.

6. The validation dataset helped to identify overfitting and adjust the network"s parameters accordingly.

7. The test dataset provided a final assessment of the network"s accuracy and generalizability.

英文小作文：

Convolutional Neural Networks in Deep Learning

Convolutional neural networks (CNNs) have revolutionized the field of machine learning, particularly in the field of image recognition and classification tasks. By using convolutional layers to extract spatial features from the input data, CNNs are able to learn complex patterns from visual data, resulting in excellent performance on a wide range of tasks. Feature extraction, spatial convolution, pooling layers, and training datasets are all essential components of a successful CNN model, and it is essential to carefully select and prepare these datasets to maximize the network"s performance. However, overfitting can occur if too much data is used, so it is necessary to use validation datasets to identify and adjust the network"s parameters accordingly. Ultimately, the accuracy and generalizability of a CNN model can be assessed using test datasets, which provide a final evaluation of its performance in unseen data. In conclusion, convolutional neural networks are powerful tools for machine learning, and they have opened up new possibilities for solving complex classification tasks using visual data.