convoluted

convoluted的音标为[kənˈvəʊlɪtɪd]，基本翻译为“复杂的；晦涩的；纠缠不清的”，速记技巧为：con（共同）+ volu（卷）+ t+ ed（过去分词后缀）→ 卷成一团→ 复杂的。

Convoluted这个词的词源可以追溯到拉丁语和古法语，具体来说，它是由“con”表示“一起”或“环绕”和“volut”或“volv”表示“旋转”或“卷曲”构成的。因此，这个词的含义就是“卷曲的”、“缠绕的”或“复杂的”。

变化形式：Convoluted作为形容词，可以有其名词形式Convolutions，意为“卷曲；缠绕；卷曲物”。

相关单词：

1. convolution - 卷曲；缠绕；复杂的事物

2. complex - 复杂的；复合的

3. involved - 复杂的；包含的

4. tangled - 纠结的；缠绕的

5. intertwined - 交织在一起的；缠绕的

6. knotted - 打结的；纠结的

7. serpentine - 蜿蜒的；盘旋的

8. labyrinth - 迷宫；复杂难解的事物

9. tortuous - 曲折的；复杂的

10. rambling - 冗长的；杂乱无章的

这些单词都与Convoluted这个词的含义相关，表示复杂、缠绕、难解的事物。这些单词在英语中广泛使用，描述各种复杂的情况和情境。

常用短语：

1. convolutional neural network (CNN)

2. complex problem

3. complicated situation

4. convolute oneself in

5. convolutional layer

6. complex convolution

7. complex convolutional algorithm

例句：

1. The problem is so convoluted that we need to break it down into smaller parts.

2. I"m trying to convolute myself in the complex algorithm, but it"s not easy.

3. The convolutional layer helps us to capture spatial relationships in the data.

4. The complicated situation requires a new strategy to address it.

5. We need to un-convolute the data before we can analyze it properly.

6. Convolutional neural networks have revolutionized the field of image recognition.

7. Can you explain the convolutional algorithm in simple terms?

英文小作文：

Convolutional Neural Networks in Deep Learning

Convolutional neural networks (CNNs) are a type of deep learning algorithm that has revolutionized the field of machine learning and computer vision. They work by applying a series of convolutional layers to data, with each layer analyzing a smaller and smaller section of the input data to extract features that are relevant to the task at hand. This process involves complex convolutional operations that require a deep understanding of the algorithm to effectively implement and train. However, once trained, CNNs can perform extremely well in a wide range of tasks, such as image classification, object detection, and segmentation. I believe that CNNs are one of the most powerful tools in the deep learning toolbox and will continue to play an important role in the field of machine learning and computer vision in the future.