Deep Learning Vocabulary - English-Chinese Dictionary
深度学习词汇 - 中英对照词典
Created: 2025-12-08 22:18:14
Core Concepts / 核心概念
1. normalization [ˌnɔːrməlaɪˈzeɪʃən]
中文: 归一化，标准化
Definition: The process of scaling data to a standard range or distribution
例句 / Example:
- Feature normalization helps stabilize training. （特征归一化有助于稳定训练）
- We apply z-score normalization to the input data. （我们对输入数据应用 z-score 标准化）
2. normalized [ˈnɔːrməlaɪzd]
中文: 归一化的，标准化的
Definition: Data that has been scaled to a standard range
例句 / Example:
- The normalized features have zero mean and unit variance. （归一化后的特征具有零均值和单位方差）
- Normalized data converges faster in gradient descent. （归一化数据在梯度下降中收敛更快）
3. deep learning [diːp ˈlɜːrnɪŋ]
中文: 深度学习
Definition: A subset of machine learning using neural networks with multiple layers
例句 / Example:
- Deep learning models require large amounts of data. （深度学习模型需要大量数据）
- Normalization is crucial in deep learning contexts. （在深度学习场景中，归一化至关重要）
4. feature [ˈfiːtʃər]
中文: 特征
Definition: An individual measurable property or characteristic of a phenomenon
例句 / Example:
- Each feature should be normalized independently. （每个特征应独立归一化）
- The model uses two features with very different scales. （模型使用两个量级差异很大的特征）
5. scaling [ˈskeɪlɪŋ]
中文: 缩放
Definition: The process of adjusting the range of values
例句 / Example:
- Feature scaling prevents one feature from dominating. （特征缩放防止某个特征主导）
- Min-max scaling maps values to [0, 1] range. （最小最大缩放将值映射到 [0, 1] 范围）
Statistical Terms / 统计术语
6. mean [miːn]
中文: 均值，平均值
Definition: The average value of a set of numbers
例句 / Example:
- Zero mean normalization subtracts the mean from each value. （零均值归一化从每个值中减去均值）
- The mean of normalized features should be approximately zero. （归一化特征的均值应接近零）
7. variance [ˈveriəns]
中文: 方差
Definition: A measure of how spread out values are from the mean
例句 / Example:
- Unit variance means the standard deviation equals 1. （单位方差意味着标准差等于 1）
- Normalization adjusts both mean and variance. （归一化同时调整均值和方差）
8. standard deviation (std) [ˈstændərd ˌdiːviˈeɪʃən]
中文: 标准差
Definition: The square root of variance, measuring data dispersion
例句 / Example:
- We divide by standard deviation in z-score normalization. （在 z-score 归一化中，我们除以标准差）
- The std of normalized data should be close to 1. （归一化数据的标准差应接近 1）
9. zero mean [ˈzɪroʊ miːn]
中文: 零均值
Definition: Having an average value of zero
例句 / Example:
- Z-score normalization produces zero mean features. （Z-score 归一化产生零均值特征）
- Zero mean helps center the data distribution. （零均值有助于将数据分布居中）
10. unit variance [ˈjuːnɪt ˈveriəns]
中文: 单位方差
Definition: Variance equal to 1 (standard deviation = 1)
例句 / Example:
- Normalized features have unit variance. （归一化特征具有单位方差）
- Unit variance ensures consistent feature scales. （单位方差确保特征尺度一致）
Optimization Terms / 优化术语
11. gradient [ˈɡreɪdiənt]
中文: 梯度
Definition: The vector of partial derivatives indicating the direction of steepest ascent
例句 / Example:
- Normalization stabilizes gradients during training. （归一化在训练过程中稳定梯度）
- Large gradients can cause training instability. （大梯度可能导致训练不稳定）
12. gradient descent [ˈɡreɪdiənt dɪˈsent]
中文: 梯度下降
Definition: An optimization algorithm that minimizes loss by following gradients
例句 / Example:
- Gradient descent converges faster with normalized features. （使用归一化特征时，梯度下降收敛更快）
- We use gradient descent to train the regression model. （我们使用梯度下降训练回归模型）
13. convergence [kənˈvɜːrdʒəns]
中文: 收敛
Definition: The process of approaching a stable solution
例句 / Example:
- Normalization speeds up convergence. （归一化加速收敛）
- The model shows faster convergence with normalized inputs. （使用归一化输入时，模型收敛更快）
14. learning rate [ˈlɜːrnɪŋ reɪt]
中文: 学习率
Definition: A hyperparameter controlling the step size in optimization
例句 / Example:
- Normalization allows larger learning rates. （归一化允许使用更大的学习率）
- Adjust the learning rate to see stability differences. （调整学习率以观察稳定性差异）
15. stabilize [ˈsteɪbəlaɪz]
中文: 稳定
Definition: To make something steady or consistent
例句 / Example:
- Normalization helps stabilize gradients. （归一化有助于稳定梯度）
- Feature scaling stabilizes the training process. （特征缩放稳定训练过程）
Loss & Evaluation / 损失与评估
16. loss [lɔːs]
中文: 损失
Definition: A measure of how far predictions are from actual values
例句 / Example:
- The final loss without normalization was 13822. （未归一化的最终损失为 13822）
- Normalization significantly reduces the loss. （归一化显著降低损失）
17. loss landscape [lɔːs ˈlændskeɪp]
中文: 损失景观
Definition: The shape of the loss function in parameter space
例句 / Example:
- Normalization creates a smoother loss landscape. （归一化创建更平滑的损失景观）
- A smooth loss landscape enables better optimization. （平滑的损失景观有助于更好的优化）
Normalization Methods / 归一化方法
18. z-score [ziː skɔːr]
中文: Z 分数，标准分数
Definition: A normalization method: (x - mean) / std
例句 / Example:
- Z-score normalization subtracts mean and divides by std. （Z-score 归一化减去均值并除以标准差）
- We apply z-score normalization to the input features. （我们对输入特征应用 z-score 归一化）
19. min-max scaling [mɪn mæks ˈskeɪlɪŋ]
中文: 最小最大缩放
Definition: Scaling data to a fixed range, typically [0, 1]
例句 / Example:
- Min-max scaling maps values to [0, 1] range. （最小最大缩放将值映射到 [0, 1] 范围）
- Min-max scaling is an alternative to z-score normalization. （最小最大缩放是 z-score 归一化的替代方法）
20. per-channel [pər ˈtʃænəl]
中文: 按通道的
Definition: Applied separately to each channel (e.g., RGB channels)
例句 / Example:
- Per-channel image normalization processes each color channel separately. （按通道图像归一化分别处理每个颜色通道）
- We use per-channel normalization for image data. （我们对图像数据使用按通道归一化）
21. image normalization [ˈɪmɪdʒ ˌnɔːrməlaɪˈzeɪʃən]
中文: 图像归一化
Definition: Normalizing pixel values in images
例句 / Example:
- Image normalization is essential for CNN training. （图像归一化对 CNN 训练至关重要）
- Per-channel image normalization improves model performance. （按通道图像归一化提高模型性能）
Data & Model Terms / 数据与模型术语
22. regression [rɪˈɡreʃən]
中文: 回归
Definition: A supervised learning task predicting continuous values
例句 / Example:
- We generate synthetic two-feature regression data. （我们生成合成的双特征回归数据）
- Linear regression benefits from feature normalization. （线性回归受益于特征归一化）
23. synthetic [sɪnˈθetɪk]
中文: 合成的，人工的
Definition: Artificially created rather than naturally occurring
例句 / Example:
- We use synthetic data for demonstration purposes. （我们使用合成数据进行演示）
- Synthetic datasets help test normalization effects. （合成数据集有助于测试归一化效果）
24. magnitude [ˈmæɡnɪtuːd]
中文: 量级，大小
Definition: The size or scale of something
例句 / Example:
- Features should have comparable magnitude after normalization. （归一化后，特征应具有可比较的量级）
- Different feature magnitudes can cause training issues. （不同的特征量级可能导致训练问题）
25. feature dominance [ˈfiːtʃər ˈdɑːmɪnəns]
中文: 特征主导
Definition: When one feature overshadows others due to scale differences
例句 / Example:
- Normalization reduces feature dominance. （归一化减少特征主导）
- Feature dominance can bias the model's predictions. （特征主导可能使模型预测产生偏差）
Training Terms / 训练术语
26. trace [treɪs]
中文: 轨迹，追踪
Definition: A record of values over time (e.g., loss trace)
例句 / Example:
- We compare gradient descent loss traces with/without normalization. （我们比较归一化前后的梯度下降损失轨迹）
- The loss trace shows faster convergence with normalization. （损失轨迹显示归一化后收敛更快）
27. stability [stəˈbɪlɪti]
中文: 稳定性
Definition: The quality of being steady and consistent
例句 / Example:
- Normalization improves training stability. （归一化提高训练稳定性）
- Plotting losses helps visualize stability differences. （绘制损失有助于可视化稳定性差异）
28. step [step]
中文: 步骤，步数
Definition: A single iteration in the training process
例句 / Example:
- The first 5 steps show rapid loss reduction. （前 5 步显示损失快速下降）
- Each training step updates the model parameters. （每个训练步骤更新模型参数）
Additional Terms / 附加术语
29. input [ˈɪnpʊt]
中文: 输入
Definition: Data fed into a model
例句 / Example:
- Normalization scales inputs to comparable ranges. （归一化将输入缩放到可比较的范围）
- Raw inputs often need normalization before training. （原始输入在训练前通常需要归一化）
30. comparable [ˈkɑːmpərəbəl]
中文: 可比较的
Definition: Similar enough to be compared fairly
例句 / Example:
- Normalization makes features have comparable magnitude. （归一化使特征具有可比较的量级）
- Features with comparable scales train more efficiently. （具有可比较尺度的特征训练更高效）
Quick Reference / 快速参考
| English | 中文 | Phonetic | Key Usage |
|---|---|---|---|
| normalization | 归一化/标准化 | [ˌnɔːrməlaɪˈzeɪʃən] | Feature scaling technique |
| gradient | 梯度 | [ˈɡreɪdiənt] | Optimization direction |
| convergence | 收敛 | [kənˈvɜːrdʒəns] | Training progress |
| learning rate | 学习率 | [ˈlɜːrnɪŋ reɪt] | Step size parameter |
| loss | 损失 | [lɔːs] | Error measure |
| z-score | Z 分数 | [ziː skɔːr] | Normalization method |
| variance | 方差 | [ˈveriəns] | Data spread measure |
| feature | 特征 | [ˈfiːtʃər] | Input variable |
Study Tips / 学习建议
- Practice with examples: Use the example sentences to understand context. （通过例句理解语境）
- Focus on pronunciation: Pay attention to phonetic symbols for key terms. （关注重点词汇的音标）
- Connect concepts: Understand how normalization relates to gradients and convergence. （理解归一化与梯度、收敛的关系）
- Read code comments: Look at
normalize_demo.pyfor practical usage. （查看代码注释了解实际用法）
Last Updated: 2025-12-08 22:18:14