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‎.vscode/.server-controller-port.log

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{
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"port": 9145,
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"time": 1741529993430,
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"version": "0.0.3"
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}

‎docs/.vuepress/config.js

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"01-扫码支付后都发生了啥？",
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"02-大厂的第三方支付业务架构设计",
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"wechat-pay-development-guide-avoid-pitfalls",
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"high-avail-payments",
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]
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}],
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"building-neural-networks-with-pytorch",
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"pytorch-cifar10-image-classifier-tutorial",
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]
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}, ],
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},
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{
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title: "NLP",
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collapsable: false,
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sidebarDepth: 0,
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children: [
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"basic-of-nlp",
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"text-preprocessing-overview",
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"text-vectorization-guide",
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"text-data-analysis-practical-guide",
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]
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},
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],
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"/md/AI/langchain4j/": [{
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title: "LangChain4j基础",
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"building-effective-agents",
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"ai-agent-is-coming",
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"software-development-in-AI2",
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"overcoming-fear-uncertainty-and-doubt-in-the-era-of-ai-transformation",
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]
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},
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‎docs/md/AI/ml/basic-of-nlp.md

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# NLP入门
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## 0 目标
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- 了解啥是NLP
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- 了解NLP的发展简史
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- 了解NLP的应用场景
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- 了解本教程中的NLP
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## 1 啥是NLP？
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计算机科学与语言学中关注于计算机与人类语言间转换的领域。
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## 2 发展简史
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![](https://my-img.javaedge.com.cn/javaedge-blog/2025/03/8b8f1018e60cc213528e58c83629a5d7.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2025/03/47ec99996359ae976bebece4ee28ffeb.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2025/03/97d97fbc38d6e002d3808b6b545b65a3.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2025/03/24ed2c62a015d244eb3b792e1fdf6a8e.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2025/03/5f816f6e0b649e7e023aae10c84d48c8.png)
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## 3 应用场景
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- 语音助手
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- 机器翻译
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- 搜索引擎
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- 智能问答
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- ...
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### 3.1 语音助手
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科大讯飞语音识别技术访谈：
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<video src="/Volumes/mobileData/data/%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%96%99/01-%E9%98%B6%E6%AE%B51-3%EF%BC%88python%E5%9F%BA%E7%A1%80%20%E3%80%81python%E9%AB%98%E7%BA%A7%E3%80%81%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%EF%BC%89/03-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%8ENLP/01-%E8%AE%B2%E4%B9%89/HTML/mkdocs_NLP/img/xunfei.mp4"></video>
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### 3.2 机器翻译
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CCTV上的机器翻译系统, 让世界聊得来！
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<video src="/Volumes/mobileData/data/%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%96%99/01-%E9%98%B6%E6%AE%B51-3%EF%BC%88python%E5%9F%BA%E7%A1%80%20%E3%80%81python%E9%AB%98%E7%BA%A7%E3%80%81%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%EF%BC%89/03-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%8ENLP/01-%E8%AE%B2%E4%B9%89/HTML/mkdocs_NLP/img/fanyi.mp4"></video>
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## 4 本专栏的NLP
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### 4.1 课程理念与宗旨
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本系列课程将开启你的NLP之旅, 全面从企业实战角度出发, 课程设计内容对应企业开发标准流程和企业发展路径, 助力你成为一名真正的AI-NLP工程师。
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### 4.2 内容先进性说明
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本课程内容结合当下时代背景, 更多关注NLP在深度学习领域的进展, 这也将是未来几年甚至几十年NLP的重要发展方向, 简化传统NLP的内容, 如语言规则, 传统模型, 特征工程等, 带来效果更好, 应用更广的Transfomer, 迁移学习等先进内容。
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### 4.3 内容大纲概要
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| 模块名称 | 主要内容 | 案例 |
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| ------------ | ------------------------------------------------------------ | -------------------------------- |
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| 文本预处理 | 文本处理基本方法，文本张量表示、文本数据分析、文本增强方法等 | 路透社新闻类型分类任务 |
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| 经典序列模型 | HMM与CRF模型的作用, 使用过程, 差异比较以及发展现状等 | |
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| RNN及其变体 | RNN, LSTM, GRU模型的作用, 构建, 优劣势比较等 | 全球人名分类任务, 英译法翻译任务 |
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| Transformer | Transformer模型的作用, 细节原理解析, 模型构建过程等 | 构建基于Transformer的语言模型 |
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| 迁移学习 | fasttext工具的作用, 迁移学习理论, NLP标准数据集和预训练模型的使用等 | 全国酒店评论情感分析任务 |
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## 5 云服务器使用入门
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### 5.1 基本操作
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```shell
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# 查看cpu逻辑核
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lscpu
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```
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```text
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Architecture: x86_64
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CPU op-mode(s): 32-bit, 64-bit
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Byte Order: Little Endian
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CPU(s): 4
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On-line CPU(s) list: 0-3
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Thread(s) per core: 2
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Core(s) per socket: 2
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座： 1
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NUMA 节点： 1
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厂商 ID： GenuineIntel
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CPU 系列： 6
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型号： 85
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型号名称： Intel(R) Xeon(R) Platinum 8269CY CPU @ 2.50GHz
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步进： 7
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CPU MHz： 2500.000
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BogoMIPS： 5000.00
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超管理器厂商： KVM
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虚拟化类型： 完全
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L1d 缓存： 32K
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L1i 缓存： 32K
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L2 缓存： 1024K
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L3 缓存： 36608K
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NUMA 节点0 CPU： 0-3
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Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl eagerfpu pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 arat avx512_vnni
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```
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查看计算环境：
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```shell
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cd /home/ec2-user/
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vim README
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```
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你将看到所有的虚拟环境
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```text
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Please use one of the following commands to start the required environment with the framework of your choice:
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for MXNet(+Keras2) with Python3 (CUDA 10.1 and Intel MKL-DNN) ____________________________________ source activate mxnet_p36
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for MXNet(+Keras2) with Python2 (CUDA 10.1 and Intel MKL-DNN) ____________________________________ source activate mxnet_p27
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for MXNet(+AWS Neuron) with Python3 ___________________________________________________ source activate aws_neuron_mxnet_p36
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for TensorFlow(+Keras2) with Python3 (CUDA 10.0 and Intel MKL-DNN) __________________________ source activate tensorflow_p36
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for TensorFlow(+Keras2) with Python2 (CUDA 10.0 and Intel MKL-DNN) __________________________ source activate tensorflow_p27
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for TensorFlow(+AWS Neuron) with Python3 _________________________________________ source activate aws_neuron_tensorflow_p36
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for TensorFlow 2(+Keras2) with Python3 (CUDA 10.1 and Intel MKL-DNN) _______________________ source activate tensorflow2_p36
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for TensorFlow 2(+Keras2) with Python2 (CUDA 10.1 and Intel MKL-DNN) _______________________ source activate tensorflow2_p27
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for TensorFlow 2.3 with Python3.7 (CUDA 10.2 and Intel MKL-DNN) _____________________ source activate tensorflow2_latest_p37
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for PyTorch 1.4 with Python3 (CUDA 10.1 and Intel MKL) _________________________________________ source activate pytorch_p36
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for PyTorch 1.4 with Python2 (CUDA 10.1 and Intel MKL) _________________________________________ source activate pytorch_p27
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for PyTorch 1.6 with Python3 (CUDA 10.1 and Intel MKL) ________________________________ source activate pytorch_latest_p36
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for PyTorch (+AWS Neuron) with Python3 ______________________________________________ source activate aws_neuron_pytorch_p36
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for Chainer with Python2 (CUDA 10.0 and Intel iDeep) ___________________________________________ source activate chainer_p27
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for Chainer with Python3 (CUDA 10.0 and Intel iDeep) ___________________________________________ source activate chainer_p36
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for base Python2 (CUDA 10.0) _______________________________________________________________________ source activate python2
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for base Python3 (CUDA 10.0) _______________________________________________________________________ source activate python3
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```
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如需用python3 + pytorch新版:
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```shell
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source activate pytorch_latest_p36
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```
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查看具体的python和pip版本：
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```shell
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python3 -V
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# 查看pip版本
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pip -V
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# 查看重点的科学计算包，tensorflow，pytorch等
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pip list
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```
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> - 输出效果:
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```text
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Python 3.6.10 :: Anaconda, Inc.
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pip 20.0.2 from /home/ec2-user/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/pip (python 3.6)
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```
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------
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- 查看图数据情况：
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```shell
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# 开启图数据库，这里后期我们将重点学习的数据库
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neo4j start
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# 关闭数据库
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neo4j stop
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```
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------
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> - 输出效果:
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```text
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Active database: graph.db
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Directories in use:
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home: /var/lib/neo4j
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config: /etc/neo4j
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logs: /var/log/neo4j
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plugins: /var/lib/neo4j/plugins
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import: /var/lib/neo4j/import
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data: /var/lib/neo4j/data
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certificates: /var/lib/neo4j/certificates
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run: /var/run/neo4j
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Starting Neo4j.
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Started neo4j (pid 17565). It is available at http://0.0.0.0:7474/
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There may be a short delay until the server is ready.
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See /var/log/neo4j/neo4j.log for current status.
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Stopping Neo4j.. stopped
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```
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------
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- 运行一个使用Pytorch的程序:
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```shell
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cd /data
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python3 pytorch_demo.py
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```
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输出效:
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```text
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Net(
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(conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1))
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(conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1))
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(fc1): Linear(in_features=576, out_features=120, bias=True)
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(fc2): Linear(in_features=120, out_features=84, bias=True)
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(fc3): Linear(in_features=84, out_features=10, bias=True)
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)
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```

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