![codecamp](https://atts.w3cschool.cn/attachments/image/20230811/1691741920856601.png)
TensorFlow 机器学习相关英文术语
机器学习相关术语(按首字母排序)
缩写 | 英语 | 汉语 |
---|---|---|
A | ||
Activation Function | 激活函数 | |
Adversarial Networks | 对抗网络 | |
Affine Layer | 仿射层 | |
agent | 代理/智能体 | |
algorithm | 算法 | |
alpha-beta pruning | α-β剪枝 | |
anomaly detection | 异常检测 | |
approximation | 近似 | |
AGI | Artificial General Intelligence | 通用人工智能 |
AI | Artificial Intelligence | 人工智能 |
association analysis | 关联分析 | |
attention mechanism | 注意机制 | |
autoencoder | 自编码器 | |
ASR | automatic speech recognition | 自动语音识别 |
automatic summarization | 自动摘要 | |
average gradient | 平均梯度 | |
Average-Pooling | 平均池化 | |
B | ||
BP | backpropagation | 反向传播 |
BPTT | Backpropagation Through Time | 通过时间的反向传播 |
BN | Batch Normalization | 分批标准化 |
Bayesian network | 贝叶斯网络 | |
Bias-Variance Dilemma | 偏差/方差困境 | |
Bi-LSTM | Bi-directional Long-Short Term Memory | 双向长短期记忆 |
bias | 偏置/偏差 | |
big data | 大数据 | |
Boltzmann machine | 玻尔兹曼机 | |
C | ||
CPU | Central Processing Unit | 中央处理器 |
chunk | 词块 | |
clustering | 聚类 | |
cluster analysis | 聚类分析 | |
co-adapting | 共适应 | |
co-occurrence | 共现 | |
Computation Cost | 计算成本 | |
Computational Linguistics | 计算语言学 | |
computer vision | 计算机视觉 | |
concept drift | 概念漂移 | |
CRF | conditional random field | 条件随机域/场 |
convergence | 收敛 | |
CA | conversational agent | 会话代理 |
convexity | 凸性 | |
CNN | convolutional neural network | 卷积神经网络 |
Cost Function | 成本函数 | |
cross entropy | 交叉熵 | |
D | ||
Decision Boundary | 决策边界 | |
Decision Trees | 决策树 | |
DBN | Deep Belief Network | 深度信念网络 |
DCGAN | Deep Convolutional Generative Adversarial Network | 深度卷积生成对抗网络 |
DL | deep learning | 深度学习 |
DNN | deep neural network | 深度神经网络 |
Deep Q-Learning | 深度Q学习 | |
DQN | Deep Q-Network | 深度Q网络 |
DNC | differentiable neural computer | 可微分神经计算机 |
dimensionality reduction algorithm | 降维算法 | |
discriminative model | 判别模型 | |
discriminator | 判别器 | |
divergence | 散度 | |
domain adaption | 领域自适应 | |
Dropout | ||
Dynamic Fusion | 动态融合 | |
E | ||
Embedding | 嵌入 | |
emotional analysis | 情绪分析 | |
End-to-End | 端到端 | |
EM | Expectation-Maximization | 期望最大化 |
Exploding Gradient Problem | 梯度爆炸问题 | |
ELM | Extreme Learning Machine | 超限学习机 |
F | ||
FAIR | Facebook Artificial Intelligence Research | Facebook人工智能研究所 |
factorization | 因子分解 | |
feature engineering | 特征工程 | |
Featured Learning | 特征学习 | |
Feedforward Neural Networks | 前馈神经网络 | |
G | ||
game theory | 博弈论 | |
GMM | Gaussian Mixture Model | 高斯混合模型 |
GA | Genetic Algorithm | 遗传算法 |
Generalization | 泛化 | |
GAN | Generative Adversarial Networks | 生成对抗网络 |
Generative Model | 生成模型 | |
Generator | 生成器 | |
Global Optimization | 全局优化 | |
GNMT | Google Neural Machine Translation | 谷歌神经机器翻译 |
Gradient Descent | 梯度下降 | |
graph theory | 图论 | |
GPU | graphics processing unit | 图形处理单元/图形处理器 |
H | ||
HDM | hidden dynamic model | 隐动态模型 |
hidden layer | 隐藏层 | |
HMM | Hidden Markov Model | 隐马尔可夫模型 |
hybrid computing | 混合计算 | |
hyperparameter | 超参数 | |
I | ||
ICA | Independent Component Analysis | 独立成分分析 |
input | 输入 | |
ICML | International Conference for Machine Learning | 国际机器学习大会 |
language phenomena | 语言现象 | |
latent dirichlet allocation | 隐含狄利克雷分布 | |
J | ||
JSD | Jensen-Shannon Divergence | JS距离 |
K | ||
K-Means Clustering | K-均值聚类 | |
K-NN | K-Nearest Neighbours Algorithm | K-最近邻算法 |
Knowledge Representation | 知识表征 | |
KB | knowledge base | 知识库 |
L | ||
Latent Dirichlet Allocation | 隐狄利克雷分布 | |
LSA | latent semantic analysis | 潜在语义分析 |
learner | 学习器 | |
Linear Regression | 线性回归 | |
log likelihood | 对数似然 | |
Logistic Regression | Logistic回归 | |
LSTM | Long-Short Term Memory | 长短期记忆 |
loss | 损失 | |
M | ||
MT | machine translation | 机器翻译 |
Max-Pooling | 最大池化 | |
Maximum Likelihood | 最大似然 | |
minimax game | 最小最大博弈 | |
Momentum | 动量 | |
MLP | Multilayer Perceptron | 多层感知器 |
multi-document summarization | 多文档摘要 | |
MLP | multi layered perceptron | 多层感知器 |
multimodal learning | 多模态学习 | |
multiple linear regression | 多元线性回归 | |
N | ||
Naive Bayes Classifier | 朴素贝叶斯分类器 | |
named entity recognition | 命名实体识别 | |
Nash equilibrium | 纳什均衡 | |
NLG | natural language generation | 自然语言生成 |
NLP | natural language processing | 自然语言处理 |
NLL | Negative Log Likelihood | 负对数似然 |
NMT | Neural Machine Translation | 神经机器翻译 |
NTM | Neural Turing Machine | 神经图灵机 |
NCE | noise-contrastive estimation | 噪音对比估计 |
non-convex optimization | 非凸优化 | |
non-negative matrix factorization | 非负矩阵分解 | |
Non-Saturating Game | 非饱和博弈 | |
O | ||
objective function | 目标函数 | |
Off-Policy | 离策略 | |
On-Policy | 在策略 | |
one shot learning | 一次性学习 | |
output | 输出 | |
P | ||
Parameter | 参数 | |
parse tree | 解析树 | |
part-of-speech tagging | 词性标注 | |
PSO | Particle Swarm Optimization | 粒子群优化算法 |
perceptron | 感知器 | |
polarity detection | 极性检测 | |
pooling | 池化 | |
PPGN | Plug and Play Generative Network | 即插即用生成网络 |
PCA | principal component analysis | 主成分分析 |
Probability Graphical Model | 概率图模型 | |
Q | ||
QNN | Quantized Neural Network | 量子化神经网络 |
quantum computer | 量子计算机 | |
Quantum Computing | 量子计算 | |
R | ||
RBF | Radial Basis Function | 径向基函数 |
Random Forest Algorithm | 随机森林算法 | |
ReLU | Rectified Linear Unit | 线性修正单元/线性修正函数 |
RNN | Recurrent Neural Network | 循环神经网络 |
recursive neural network | 递归神经网络 | |
RL | reinforcement learning | 强化学习 |
representation | 表征 | |
representation learning | 表征学习 | |
Residual Mapping | 残差映射 | |
Residual Network | 残差网络 | |
RBM | Restricted Boltzmann Machine | 受限玻尔兹曼机 |
Robot | 机器人 | |
Robustness | 稳健性 | |
RE | Rule Engine | 规则引擎 |
S | ||
saddle point | 鞍点 | |
Self-Driving | 自动驾驶 | |
SOM | self organised map | 自组织映射 |
Semi-Supervised Learning | 半监督学习 | |
sentiment analysis | 情感分析 | |
SLAM | simultaneous localization and mapping | 同步定位与地图构建 |
SVD | Singular Value Decomposition | 奇异值分解 |
Spectral Clustering | 谱聚类 | |
Speech Recognition | 语音识别 | |
SGD | stochastic gradient descent | 随机梯度下降 |
supervised learning | 监督学习 | |
SVM | Support Vector Machine | 支持向量机 |
synset | 同义词集 | |
T | ||
t-SNE | T-Distribution Stochastic Neighbour Embedding | T-分布随机近邻嵌入 |
tensor | 张量 | |
TPU | Tensor Processing Units | 张量处理单元 |
the least square method | 最小二乘法 | |
Threshold | 阙值 | |
Time Step | 时间步骤 | |
tokenization | 标记化 | |
treebank | 树库 | |
transfer learning | 迁移学习 | |
Turing Machine | 图灵机 | |
U | ||
unsupervised learning | 无监督学习 | |
V | ||
Vanishing Gradient Problem | 梯度消失问题 | |
VC Theory | Vapnik–Chervonenkis theory | 万普尼克-泽范兰杰斯理论 |
von Neumann architecture | 冯·诺伊曼架构/结构 | |
W | ||
WGAN | Wasserstein GAN | |
W | weight | 权重 |
word embedding | 词嵌入 | |
WSD | word sense disambiguation | 词义消歧 |
X | ||
Y | ||
Z | ||
ZSL | zero-shot learning | 零次学习 |
zero-data learning | 零数据学习 |
参考文献
[1]机器学习.Tom M.Mitchell