【人工智能】英文学习材料01(每日一句)

🌻个人主页:相洋同学
🥇学习在于行动、总结和坚持,共勉!

目录

[1.Natural Language Processing,NLP(自然语言处理)](#1.Natural Language Processing,NLP(自然语言处理))

[2.Machine Learing,ML(机器学习)](#2.Machine Learing,ML(机器学习))

[3.Neural Networks(神经网络)](#3.Neural Networks(神经网络))

[4.Deep Learing(深度学习)](#4.Deep Learing(深度学习))

[5.Loss Function (损失函数)](#5.Loss Function (损失函数))

[6.Gradient Descent (梯度下降)](#6.Gradient Descent (梯度下降))

[7.Stochastic Gradient Descent, SGD (随机梯度下降)](#7.Stochastic Gradient Descent, SGD (随机梯度下降))

[8.Mini-batch Gradient Descent (小批量梯度下降)](#8.Mini-batch Gradient Descent (小批量梯度下降))

9.Backpropagation (反向传播)

10.Overfitting (过拟合)


1.Natural Language Processing,NLP(自然语言处理)

Natural Language Processing (NLP) is the field of artificial intelligence that enables computers to understand,interpret , and generate human language. It bridges the gap between human communication and computer understanding, making it possible for machines to perform tasks like translation, sentiment analysis , and topic classification.

  • interpret--解释、理解
  • bridges the gap -- 桥接差距
  • perform tasks -- 执行任务
  • sentiment analysis -- 情感分析
  • topic classification -- 主题分类

2.Machine Learing,ML(机器学习)

This is a subset of artificial intelligence that involvesalgorithms and statistical models that enable computers to performspecific tasks without using explicit instructions . Instead, they rely on patterns and inference derived from data. The goal of ML is to enable computers to learn from and make predictions or decisions based on data.

  • subet -- 子集
  • algorithms -- 算法
  • statistical models -- 统计模型
  • specific tasks -- 特定任务
  • explicit instructions -- 明确的指令
  • patterns -- 模式
  • inference -- 推理
  • derived from -- 源自

3.Neural Networks(神经网络)

Inspired by the human brain, neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize pattern s. They interpret sensory data through a kind of machine perception , labeling, or clustering of raw input . These networks can adapt to changing input, meaning they generate the best possible result without needing to redesign the output criteria.

  • Inspired by -- 受启发于
  • modeled loosely after -- 大致模仿,model有模仿的意思,loosely有偏差的
  • recognize patterns -- 识别模式
  • sensory data -- 感官数据
  • perception -- 感知、感觉
  • clustering -- 聚类
  • raw input -- 原始输入
  • adapt to -- 适应
  • changing -- chage的现在分词
  • redesign -- 重新设计
  • criteria -- 标准

4.Deep Learing(深度学习)

Deep Learning is a subset of machine learning in artificial intelligence that structures algorithms inlayers to create an "artificial neural network" that can learn and make intelligent decisions on its own. This technology powers advanced applications such as voice recognition and image analysis.

  • subset -- 子集
  • structures -- 组织
  • layers -- 层
  • powers advanced applications -- 驱动高级应用
  • voice recognition -- 语音识别
  • image analysis -- 图像分析

5.Loss Function (损失函数)

A Loss Function in machine learning measures the difference between the actual output and the predicted output of the model. It quantifies how well the prediction model performs by assigning a cost to prediction errors.

  • actual output -- 实际输出
  • predicted output -- 预测输出
  • quantifies -- 量化
  • assigning -- 分配

6.Gradient Descent (梯度下降)

Gradient Descent is anoptimization algorithm used tominimize some function by iteratively moving towards the minimum value of the function. It is commonly used in machine learning to find the best parameters for a model.

  • gradient -- 梯度
  • optimization algorithm -- 优化算法
  • minimize -- 最小化
  • iteratively -- 迭代地
  • minimum value -- 最小值
  • commonly -- 普遍地
  • parameters -- 参数

7.Stochastic Gradient Descent, SGD (随机梯度下降)

Stochastic Gradient Descent (SGD) is avariation of the gradient descent algorithm that updates the model's parameters using only a single sample or a small batch of samples, which makes the process faster and can help avoidlocal minima.

  • stochastic -- 随机的
  • variation -- 变体
  • batch -- 批量
  • local minima -- 局部最小值

8.Mini-batch Gradient Descent (小批量梯度下降)

Mini-batch Gradient Descent is a balance between the full batch gradient descent and stochastic gradient descent. It updates the model's parameters using a subset of the training data, rather than the full dataset or individual samples, optimizing computational efficiency.

  • full batch -- 全批量
  • subset -- 子集
  • training data -- 训练数据
  • computational efficiency -- 计算效率

9.Backpropagation (反向传播)

Backpropagation is a method used in artificial neural networks to calculate the gradient of the loss function with respect to each weight by thechain rule, effectively allowing for the optimization of weights to minimize loss.

  • calculate -- 计算
  • respect -- 关于
  • chain rule -- 链规则

10.Overfitting (过拟合)

Overfitting occurs when a machine learning model learns the detail and noise in the training data to the extent that it negatively impacts the model's performance on new data. This means the model is too complex, capturing noise as if it were a significant pattern , leading to poor generalization on unseen data.

  • occurs -- 出现
  • detail and noise -- 细节和噪声
  • to the extent that -- 到...的程度
  • negatively impacts -- 负面影响
  • performance -- 性能
  • capturing noise -- 捕捉噪声
  • significant pattern -- 重要模式
  • poor generalization -- 泛化能力差
  • unseen data -- 未见数据

以上

君子坐而论道,少年起而行之,共勉

相关推荐
说私域4 分钟前
社交新零售时代本地化微商的发展路径研究——基于开源AI智能名片链动2+1模式S2B2C商城小程序源的创新实践
人工智能·开源·零售
IT_陈寒9 分钟前
Python性能优化:5个被低估的魔法方法让你的代码提速50%
前端·人工智能·后端
Deng_Xian_Sheng36 分钟前
有哪些任务可以使用无监督的方式训练深度学习模型?
人工智能·深度学习·无监督
数据科学作家3 小时前
学数据分析必囤!数据分析必看!清华社9本书覆盖Stata/SPSS/Python全阶段学习路径
人工智能·python·机器学习·数据分析·统计·stata·spss
CV缝合救星5 小时前
【Arxiv 2025 预发行论文】重磅突破!STAR-DSSA 模块横空出世:显著性+拓扑双重加持,小目标、大场景统统拿下!
人工智能·深度学习·计算机视觉·目标跟踪·即插即用模块
TDengine (老段)7 小时前
从 ETL 到 Agentic AI:工业数据管理变革与 TDengine IDMP 的治理之道
数据库·数据仓库·人工智能·物联网·时序数据库·etl·tdengine
蓝桉8027 小时前
如何进行神经网络的模型训练(视频代码中的知识点记录)
人工智能·深度学习·神经网络
星期天要睡觉8 小时前
深度学习——数据增强(Data Augmentation)
人工智能·深度学习
笑脸惹桃花8 小时前
50系显卡训练深度学习YOLO等算法报错的解决方法
深度学习·算法·yolo·torch·cuda
南山二毛9 小时前
机器人控制器开发(导航算法——导航栈关联坐标系)
人工智能·架构·机器人