298 lines
6.5 KiB
Plaintext
298 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8e7fb728",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"## 英汉术语对照\n",
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"\n",
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"鞍点，saddle point\n",
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"\n",
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"变换，transform\n",
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"\n",
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"编码器，encoder\n",
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"\n",
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"标签，label\n",
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"\n",
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"步幅，stride\n",
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"\n",
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"参数，parameter\n",
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"\n",
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"长短期记忆网络，long short-term memory (LSTM)\n",
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"\n",
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"超参数，hyperparameter\n",
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"\n",
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"层序softmax，hierarchical softmax\n",
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"\n",
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"查准率，precision\n",
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"\n",
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"成本，cost\n",
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"\n",
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"词表，vocabulary\n",
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"\n",
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"词嵌入，word embedding\n",
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"\n",
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"词向量，word vector\n",
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"\n",
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"词元，token\n",
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"\n",
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"词元分析器，tokenizer\n",
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"\n",
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"词元化，tokenize\n",
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"\n",
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"汇聚层，pooling layer\n",
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"\n",
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"稠密，dense\n",
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"\n",
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"大小，size\n",
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"\n",
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"导入，import\n",
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"\n",
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"轮，epoch\n",
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"\n",
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"暂退法，dropout\n",
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"\n",
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"动量法，momentum (method)\n",
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"\n",
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"独立同分布，independent and identically distributed (i.i.d.)\n",
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"\n",
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"端到端，end-to-end\n",
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"\n",
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"多层感知机，multilayer perceptron\n",
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"\n",
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"多头注意力，multi-head attention\n",
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"\n",
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"二元分类，binary classification\n",
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"\n",
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"二元，bigram\n",
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"\n",
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"子采样，subsample\n",
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"\n",
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"发散，diverge\n",
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"\n",
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"泛化，generalization\n",
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"\n",
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"泛化误差，generalization error\n",
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"\n",
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"方差，variance\n",
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"\n",
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"分类，classification\n",
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"\n",
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"分类器，classifier\n",
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"\n",
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"负采样，negative sampling\n",
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"\n",
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"感受野，receptive field\n",
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"\n",
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"格拉姆矩阵，Gram matrix\n",
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"\n",
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"共现，co-occurrence\n",
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"\n",
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"广播，broadcast\n",
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"\n",
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"规范化，normalization\n",
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"\n",
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"过拟合，overfitting\n",
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"\n",
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"核回归，kernel regression\n",
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"\n",
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"恒等映射，identity mapping\n",
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"\n",
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"假设，hypothesis\n",
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"\n",
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"基准，baseline\n",
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"\n",
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"激活函数，activation function\n",
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"\n",
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"解码器，decoder\n",
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"\n",
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"近似法，approximate method\n",
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"\n",
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"经验风险最小化，empirical risk minimization\n",
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"\n",
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"局部最小值，local minimum\n",
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"\n",
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"卷积核，convolutional kernel\n",
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"\n",
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"卷积神经网络，convolutional neural network\n",
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"\n",
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"决策边界，decision boundary\n",
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"\n",
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"均值，mean\n",
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"\n",
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"均方误差，mean squared error\n",
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"\n",
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"均匀采样，uniform sampling\n",
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"\n",
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"块，block\n",
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"\n",
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"困惑度，perplexity\n",
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"\n",
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"拉普拉斯平滑，Laplace smoothing\n",
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"\n",
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"连结，concatenate\n",
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"\n",
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"类，class\n",
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"\n",
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"交叉熵，cross-entropy\n",
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"\n",
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"连续词袋，continous bag-of-words (CBOW)\n",
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"\n",
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"零张量，zero tensor\n",
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"\n",
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"流水线，pipeline\n",
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"\n",
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"滤波器，filter\n",
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"\n",
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"门控循环单元，gated recurrent units (GRU)\n",
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"\n",
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"目标检测，object detection\n",
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"\n",
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"偏置，bias\n",
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"\n",
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"偏导数，partial derivative\n",
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"\n",
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"偏移量，offset\n",
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"\n",
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"批量，batch\n",
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"\n",
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"齐普夫定律，Zipf's law\n",
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"\n",
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"欠拟合，underfitting\n",
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"\n",
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"情感分析，sentiment analysis\n",
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"\n",
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"全连接层，fully-connected layer\n",
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"\n",
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"权重，weight\n",
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"\n",
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"三元，trigram\n",
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"\n",
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"上采样，upsample\n",
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"\n",
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"上下文变量，context variable\n",
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"\n",
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"上下文窗口，context window\n",
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"\n",
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"上下文词，context word\n",
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"\n",
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"上下文向量，context vector\n",
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"\n",
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"实例/示例，instance\n",
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"\n",
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"收敛，converge\n",
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"\n",
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"属性，property\n",
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"\n",
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"数值方法，numerical method\n",
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"\n",
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"数据集，dataset\n",
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"\n",
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"数据示例，data instance\n",
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"\n",
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"数据样例，data example\n",
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"\n",
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"顺序分区，sequential partitioning\n",
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"\n",
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"softmax回归，softmax regression\n",
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"\n",
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"随机采样，random sampling\n",
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"\n",
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"损失函数，loss function\n",
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"\n",
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"双向循环神经网络，bidirectional recurrent neural network\n",
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"\n",
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"特征，feature\n",
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"\n",
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"特征图，feature map\n",
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"\n",
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"特征值，eigenvalue\n",
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"\n",
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"梯度，gradient\n",
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"\n",
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"梯度裁剪，gradient clipping\n",
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"\n",
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"梯度消失，vanishing gradients\n",
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"\n",
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"填充，padding\n",
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"\n",
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"跳元模型，skip-gram model\n",
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"\n",
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"调参，tune hyperparameter\n",
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"\n",
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"停用词，stop words\n",
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"\n",
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"通道，channel\n",
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"\n",
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"凸优化，convex optimization\n",
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"\n",
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"图像，image\n",
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"\n",
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"未知词元，unknown token\n",
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"\n",
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"无偏估计，unbiased estimate\n",
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"\n",
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"误差，error\n",
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"\n",
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"小批量，minibatch\n",
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"\n",
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"小批量梯度，minibatch gradient\n",
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"\n",
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"线性模型，linear model\n",
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"\n",
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"线性回归，linear regression\n",
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"\n",
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"协同过滤，collaborative filtering\n",
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"\n",
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"学习率，learning rate\n",
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"\n",
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"训练误差，training error\n",
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"\n",
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"循环神经网络，recurrent neural network (RNN)\n",
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"\n",
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"样例，example\n",
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"\n",
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"一维梯度下降，gradient descent in one-dimensional space\n",
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"\n",
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"一元，unigram\n",
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"\n",
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"隐藏变量，hidden variable\n",
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"\n",
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"隐藏层，hidden layer\n",
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"\n",
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"优化器，optimizer\n",
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"\n",
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"语料库，corpus\n",
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"\n",
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"运算符，operator\n",
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"\n",
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"自注意力，self-attention\n",
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"\n",
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"真实值，ground truth\n",
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"\n",
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"指标，metric\n",
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"\n",
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"支持向量机，support vector machine\n",
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"\n",
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"注意力机制，attention mechanism\n",
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"\n",
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"注意力模型，attention model\n",
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"\n",
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"注意力提示，attention cue\n",
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"\n",
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"准确率/精度，accuracy\n"
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]
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}
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],
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"name": "python"
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