黑马大模型第三期
深入浅出大模型，从入门到精通
编辑点评
系统讲解大模型知识，涵盖Python基础及大模型前置知识，适合AI领域初学者。
⭐ 编辑推荐
本课程由黑马程序员团队打造，针对大模型领域进行深入讲解。
从Python基础到大模型前置知识，助你轻松入门AI大模型。
✨ 课程亮点
✦系统讲解大模型知识
✦涵盖Python基础及大模型前置知识
✦适合AI领域初学者
课程目录
📁 2411版AI大模型三期
📁 📁 01阶段：大模型入门
📁 📁 day05-大模型前置知识
1-5 大模型前置知识_3_ev.mp4 [93.2 MB]
1-5 大模型前置知识_6_ev.mp4 [78.7 MB]
1-5 大模型前置知识_4_ev.mp4 [98.6 MB]
1-5 大模型前置知识_8_ev.mp4 [125.4 MB]
1-5 大模型前置知识_10_ev.mp4 [110.2 MB]
1-5 大模型前置知识_2_ev.mp4 [103.5 MB]
1-5 大模型前置知识_9_ev.mp4 [106.9 MB]
1-5 大模型前置知识_1_ev.mp4 [50.6 MB]
1-5 大模型前置知识_7_ev.mp4 [133.5 MB]
1-5 大模型前置知识_5_ev.mp4 [141.8 MB]
📁 📁 day07-大模型前置知识
1-7 大模型前置知识_2_ev.mp4 [78.6 MB]
1-7 大模型前置知识_8_ev.mp4 [105.3 MB]
1-7 大模型前置知识_7_ev.mp4 [117.9 MB]
1-7 大模型前置知识_3_ev.mp4 [104.4 MB]
1-7 大模型前置知识_5_ev.mp4 [98.4 MB]
1-7 大模型前置知识_1_ev.mp4 [75.9 MB]
1-7 大模型前置知识_9_ev.mp4 [68.9 MB]
1-7 大模型前置知识_6_ev.mp4 [101.3 MB]
1-7 大模型前置知识_4_ev.mp4 [102.8 MB]
📁 📁 day06-大模型前置知识
1-6 大模型前置知识_3_ev.mp4 [65.5 MB]
1-6 大模型前置知识_4_ev.mp4 [96.0 MB]
1-6 大模型前置知识_2_ev.mp4 [78.7 MB]
1-6 大模型前置知识_7_ev.mp4 [94.0 MB]
1-6 大模型前置知识_5_ev.mp4 [67.7 MB]
1-6 大模型前置知识_9_ev.mp4 [83.2 MB]
1-6 大模型前置知识_6_ev.mp4 [60.2 MB]
1-6 大模型前置知识_1_ev.mp4 [55.6 MB]
1-6 大模型前置知识_11_ev.mp4 [28.6 MB]
1-6 大模型前置知识_10_ev.mp4 [64.9 MB]
1-6 大模型前置知识_8_ev.mp4 [70.8 MB]
📁 📁 day01-大模型必备Python语言
1-1 大模型必备Python语言_5_ev.mp4 [57.4 MB]
1-1 大模型必备Python语言_4_ev.mp4 [73.9 MB]
1-1 大模型必备Python语言_6_ev.mp4 [68.2 MB]
1-1 大模型必备Python语言_8_ev.mp4 [84.8 MB]
1-1 大模型必备Python语言_9_ev.mp4 [60.1 MB]
1-1 大模型必备Python语言_2_ev.mp4 [74.5 MB]
1-1 大模型必备Python语言_1_ev.mp4 [42.7 MB]
1-1 大模型必备Python语言_3_ev.mp4 [77.5 MB]
1-1 大模型必备Python语言_7_ev.mp4 [83.0 MB]
📁 📁 day02-大模型必备Python语言
1-2 大模型必备Python语言_3_ev.mp4 [73.5 MB]
1-2 大模型必备Python语言_5_ev.mp4 [84.2 MB]
1-2 大模型必备Python语言_6_ev.mp4 [80.9 MB]
1-2 大模型必备Python语言_4_ev.mp4 [84.9 MB]
1-2 大模型必备Python语言_9_ev.mp4 [81.2 MB]
1-2 大模型必备Python语言_7_ev.mp4 [96.5 MB]
1-2 大模型必备Python语言_8_ev.mp4 [96.6 MB]
1-2 大模型必备Python语言_1_ev.mp4 [36.8 MB]
1-2 大模型必备Python语言_2_ev.mp4 [82.3 MB]
📁 📁 day08-大模型前置知识
1-8 大模型前置知识_7_ev.mp4 [99.1 MB]
1-8 大模型前置知识_9_ev.mp4 [68.1 MB]
1-8 大模型前置知识_5_ev.mp4 [86.8 MB]
1-8 大模型前置知识_8_ev.mp4 [72.2 MB]
1-8 大模型前置知识_11_ev.mp4 [66.6 MB]
1-8 大模型前置知识_3_ev.mp4 [86.2 MB]
1-8 大模型前置知识_1_ev.mp4 [50.4 MB]
1-8 大模型前置知识_10_ev.mp4 [94.7 MB]
1-8 大模型前置知识_4_ev.mp4 [91.3 MB]
1-8 大模型前置知识_6_ev.mp4 [88.3 MB]
1-8 大模型前置知识_2_ev.mp4 [72.7 MB]
📁 📁 day03-大模型必备Python语言
1-3 大模型必备Python语言_4_ev.mp4 [108.1 MB]
1-3 大模型必备Python语言_2_ev.mp4 [93.4 MB]
1-3 大模型必备Python语言_5_ev.mp4 [115.0 MB]
1-3 大模型必备Python语言_7_ev.mp4 [126.6 MB]
1-3 大模型必备Python语言_11_ev.mp4 [69.9 MB]
1-3 大模型必备Python语言_1_ev.mp4 [80.6 MB]
1-3 大模型必备Python语言_6_ev.mp4 [110.8 MB]
1-3 大模型必备Python语言_8_ev.mp4 [106.6 MB]
1-3 大模型必备Python语言_3_ev.mp4 [97.4 MB]
1-3 大模型必备Python语言_9_ev.mp4 [91.5 MB]
1-3 大模型必备Python语言_10_ev.mp4 [119.3 MB]
📁 📁 day04-大模型必备Python语言
1-4 大模型必备Python语言_1_ev.mp4 [113.9 MB]
1-4 大模型必备Python语言_6_ev.mp4 [149.4 MB]
1-4 大模型必备Python语言_5_ev.mp4 [107.9 MB]
1-4 大模型必备Python语言_2_ev.mp4 [126.6 MB]
1-4 大模型必备Python语言_4_ev.mp4 [115.4 MB]
1-4 大模型必备Python语言_7_ev.mp4 [68.0 MB]
1-4 大模型必备Python语言_3_ev.mp4 [103.5 MB]
📁 📁 05阶段：多模态大模型应用实战
📁 📁 day01 【项目】Stable Diffusion多模态大模型应用实
01-图像生成_ev.mp4 [96.0 MB]
03-dalle_ev.mp4 [46.4 MB]
02-clip模型_ev.mp4 [57.6 MB]
📁 📁 day02 【项目】Stable Diffusion多模态大模型应用实战2
05-hai平台使用_ev.sz [44.4 MB]
04-处理流程_ev.sz [67.4 MB]
02-stablediffusion的基本概念_ev.sz [40.3 MB]
03-模型结构_ev.sz [16.8 MB]
01-imagen_ev.sz [24.7 MB]
📁 📁 02阶段⼤模型应⽤初体验
📁 📁 day05 大模型Prompt-Tuning方法进阶
01-GPT原理_ev.sz [32.6 MB]
05-chatGPT_ev.mp4 [110.1 MB]
02-GPT1_ev.sz [65.6 MB]
04-GPT3_ev.mp4 [115.9 MB]
06-主流的开源大模型_ev.mp4 [80.9 MB]
03-GPT2_ev.sz [87.5 MB]
📁 📁 day02 大模型应用工具实战2
13-（重点）Kimi大模型工具_ev.mp4 [29.1 MB]
02-（重点）VSCode集成IFlyCode实现前端页面编写_ev.mp4 [82.7 MB]
14-（重点）智谱清言_ev.mp4 [58.9 MB]
12-（重点）AI运营极虎漫剪_ev.mp4 [189.7 MB]
11-（重点）腾讯智影_ev.mp4 [54.8 MB]
06-（重点）哩布哩布AIGC生图工具使用_ev.mp4 [156.4 MB]
10-（重点）元分身数字人_ev.mp4 [194.7 MB]
04-（重点）通义灵码的使用_ev.mp4 [84.7 MB]
07-（重点）Pika文生视频及图生视频效果_ev.mp4 [48.6 MB]
05-（重点）AIGC堆友实现文生图以及图生图_ev.mp4 [101.4 MB]
08-（重点）Luma文生视频以及图生视频_ev.mp4 [89.2 MB]
09-（重点）可灵AI工具使用说明_ev.mp4 [221.8 MB]
03-（重点）基于IFlyCode编写后端代码_ev.mp4 [57.9 MB]
01-（重点）讯飞智文_ev.mp4 [106.9 MB]
📁 📁 day07 【项目】金融行业动态风向评估
04-迭代优化_ev.mp4 [103.3 MB]
01-提示词工程_ev.sz [98.5 MB]
02-清晰的描述_ev.sz [33.5 MB]
03-充足的思考_ev.mp4 [51.8 MB]
📁 📁 day04 主流大模型介绍及大模型Prompt-Tuning方法入
04-主流的模型架构_ev.mp4 [18.4 MB]
01-语言模型的评估指标_ev.mp4 [116.7 MB]
02-大语言模型的主要类别_ev.mp4 [48.0 MB]
03-AR和Seq2Seq模型_ev.mp4 [65.4 MB]
📁 📁 day06 大模型提示词工程应用
02-硬模版微调_ev.mp4 [33.4 MB]
01-微调方法_ev.mp4 [85.7 MB]
03-软模版_ev.mp4 [170.9 MB]
📁 📁 day08 企业大模型定制平台1
01-项目说明_ev.mp4 [49.3 MB]
03-文本分类_ev.mp4 [66.4 MB]
02-few-shot说明_ev.mp4 [64.1 MB]
📁 📁 day09 企业大模型定制平台2
01-信息抽取_ev.sz [62.1 MB]
02-信息抽取2_ev.sz [21.2 MB]
03-文本匹配_ev.mp4 [35.4 MB]
📁 📁 day01 大模型应用工具实战1
04-（重点）通义万象_ev.mp4 [150.0 MB]
08-（重点）讯飞星火_ev.mp4 [111.4 MB]
01-（了解）AI工具学习目标_ev.mp4 [7.2 MB]
07-（重点）通义法睿_ev.mp4 [59.3 MB]
05-（重点）通义智文_ev.mp4 [70.7 MB]
02-（重点）传智星云网_ev.mp4 [78.8 MB]
06-（重点）通义听悟_ev.mp4 [48.2 MB]
03-（重点）通义千问大模型使用_ev.mp4 [169.2 MB]
📁 📁 day03 大模型开发入门
04-语言模型的发展_ev.sz [153.3 MB]
01-课程内容说明_ev.sz [14.6 MB]
05-内容总结_ev.sz [5.2 MB]
02-大语言模型的背景_ev.sz [72.2 MB]
03-语言模型理解_ev.sz [18.8 MB]
📁 📁 day10 【项目】电商领域虚拟试衣系统
03-案例_ev.mp4 [39.9 MB]
01-saas平台_ev.mp4 [69.0 MB]
04-大模型定制平台_ev.mp4 [39.6 MB]
02-API调用_ev.mp4 [5.4 MB]
📁 📁 03阶段：⼤模型开发新增技术
📁 📁 day01 大模型开发工具Function Call的原理及实践
02-百度千帆大模型使用_ev.mp4 [126.7 MB]
01-百度千帆大模型介绍_ev.mp4 [91.6 MB]
📁 📁 day08 大模型开发工具Langchain详解3
02-向量数据库_ev.mp4 [33.9 MB]
03-检索_ev.mp4 [28.4 MB]
01-index_ev.mp4 [47.3 MB]
📁 📁 day04 基于阿里魔搭社区的Agent应用
01-AssistantAPI_ev.sz [88.3 MB]
02-agent_ev.sz [43.9 MB]
📁 📁 day06 大模型开发工具Langchain详解1
02-model组件_ev.mp4 [107.7 MB]
01-langchain介绍_ev.mp4 [43.6 MB]
📁 📁 day07 大模型开发工具Langchain详解2
01-model_ev.sz [25.7 MB]
05-memory_ev.sz [73.8 MB]
02-prompt_ev.sz [52.2 MB]
04-agent_ev.sz [72.1 MB]
03-chain_ev.sz [34.9 MB]
📁 📁 day03 大模型Agent的原理及实践
01-function_call多个函数_ev.mp4 [109.5 MB]
03-GPTs_ev.mp4 [62.6 MB]
02-function_call数据库查询_ev.mp4 [79.0 MB]
📁 📁 day05 大模型Agent应用
02-应用场景_ev.mp4 [101.9 MB]
01-agent_ev.mp4 [112.6 MB]
03-邮件案例_ev.mp4 [121.1 MB]
04-modelscope_ev.mp4 [10.0 MB]
📁 📁 day02 【项目】财务助手
03-天气获取_ev.sz [117.1 MB]
02-阿里百炼_ev.sz [122.6 MB]
01-function_call_ev.sz [77.2 MB]
📁 📁 04阶段：⼤模型⾼级项目开发
📁 📁 day09 【项目】新媒体行业评论智能分类与信息抽取系统
03-dataloader_ev.mp4 [86.3 MB]
02-getmax_len_ev.mp4 [36.8 MB]
01-数据处理_ev.mp4 [122.2 MB]
04-模型训练_ev.mp4 [21.4 MB]
05-模型预测_ev.mp4 [7.1 MB]
06-aigc介绍_ev.mp4 [70.4 MB]
07-图像生成算法_ev.mp4 [22.2 MB]
📁 📁 day07 【项目】新媒体行业评论智能分类与信息抽取系统
02-项目介绍_ev.mp4 [36.0 MB]
01-模型推理_ev.mp4 [30.0 MB]
03-数据处理_ev.mp4 [24.6 MB]
04-数据处理实现_ev.mp4 [80.3 MB]
📁 📁 day03 【项目】大健康行业智能问诊系统2
01-项目介绍-1730813282_ev.sz [42.6 MB]
04-dataset_ev.mp4 [53.5 MB]
03-preprcoess_ev.sz [157.6 MB]
05-dataloader_ev.mp4 [28.4 MB]
02-数据集介绍_ev.sz [23.9 MB]
📁 📁 day01 项目 基于知识库RAG的物流行业信息问答系统
03模型构建_ev.sz [71.0 MB]
05-检索_ev.sz [68.7 MB]
07-PET微调_ev.sz [93.5 MB]
06-微调方法_ev.sz [101.6 MB]
01-项目介绍_ev.sz [32.3 MB]
02-环境配置_ev.sz [12.9 MB]
04-构建向量库_ev.sz [86.7 MB]
📁 📁 day04【项目】新零售行业评价决策系统
02-模型搭建_ev.mp4 [24.5 MB]
03-模型训练过程_ev.mp4 [135.5 MB]
01-模型结构_ev.mp4 [56.1 MB]
📁 📁 day08 【项目】新媒体行业评论智能分类与信息抽取系统
01-模型训练与推理_ev.sz [89.4 MB]
04-数据集介绍_ev.sz [82.6 MB]
02-lora微调项目介绍_ev.sz [32.3 MB]
03-技术选型_ev.sz [19.8 MB]
📁 📁 day05【项目】新零售行业评价决策系统
10-配置信息_ev.mp4 [17.0 MB]
03-预测实现_ev.mp4 [12.0 MB]
07-PET回顾_ev.mp4 [17.5 MB]
08-项目架构_ev.mp4 [19.1 MB]
02-预测流程_ev.mp4 [17.4 MB]
06-电商评论_ev.mp4 [29.5 MB]
01-function_tool_ev.mp4 [76.2 MB]
12-template_ev.mp4 [70.6 MB]
04-预测实现2_ev.mp4 [104.2 MB]
11-数据获取_ev.mp4 [46.2 MB]
13-datapreprocess_ev.mp4 [51.0 MB]
05-模型上线_ev.mp4 [21.5 MB]
09-数据集介绍_ev.mp4 [28.3 MB]
📁 📁 day02【项目】大健康行业智能问诊系统
04-lora微调思想（重点）_ev.mp4 [46.4 MB]
01-上下文学习_ev.mp4 [41.3 MB]
05-lora伪代码_ev.mp4 [10.9 MB]
03-adapter_ev.mp4 [18.1 MB]
02-prefix微调_ev.mp4 [36.2 MB]
📁 📁 day06 【项目】新零售行业评价决策系统
06-评价指标_ev.mp4 [24.9 MB]
05-logits转id_ev.mp4 [12.1 MB]
01-dataloader_ev.sz [47.1 MB]
02-主标签找子标签_ev.sz [74.6 MB]
07-训练过程_ev.mp4 [37.7 MB]
04-损失函数_ev.sz [47.9 MB]
03-子标签找主标签_ev.sz [29.3 MB]
📁 📁 06阶段：技术面试分享（赠送）
📁 📁 day02-大模型面试指导
1-42 大模型加餐课（面试指导）_ev.mp4 [663.4 MB]
1-42 大模型加餐课（面试指导）_1_ev.mp4 [167.0 MB]
1-42 大模型加餐课（面试指导）_2_ev.mp4 [132.8 MB]
1-42 大模型加餐课（面试指导）_3_ev.mp4 [179.4 MB]
1-42 大模型加餐课（面试指导）_4_ev.mp4 [181.8 MB]
📁 📁 day01-综合项目与项目路演
day05-综合项目与项目路演2_ev.mp4 [39.1 MB]
day05-综合项目与项目路演3_ev.mp4 [57.2 MB]
day05-综合项目与项目路演0_ev.mp4 [44.8 MB]
day05-综合项目与项目路演1_ev.mp4 [71.9 MB]
📁 📁 day03-大模型加餐课
大模型加餐课（模型部署）_06_ev.mp4 [81.1 MB]
大模型加餐课（模型部署）_04_ev.mp4 [91.8 MB]
大模型加餐课（模型部署）_01_ev.mp4 [41.7 MB]
大模型加餐课（模型部署）_09_ev.mp4 [79.0 MB]
大模型加餐课（模型部署）_05_ev.mp4 [88.5 MB]
大模型加餐课（模型部署）_07_ev.mp4 [91.2 MB]
大模型加餐课（模型部署）_03_ev.mp4 [124.6 MB]
大模型加餐课（模型部署）_08_ev.mp4 [93.4 MB]
大模型加餐课（模型部署）_02_ev.mp4 [103.1 MB]
大模型加餐课（模型部署）_10_ev.mp4 [48.3 MB]
📁 3期AI大模型配套资料
📁 📁 06阶段：配套资料
大模型训练营-大模型时代.pdf [2.9 MB]
简历优化及面试注意事项.txt [740.0 B]
人工智能-求职自我介绍以及项目描述参考模板.docx [20.8 KB]
论文导读.zip [54.9 MB]
大模型训练营—简历优化.pdf [680.0 KB]
📁 📁 AI大模型 赠送资料
简历模板.zip [2.0 MB]
11本AI大模型相关电子书.zip [309.5 MB]
📁 📁 02阶段：配套资料
📁 📁 9月14号
📁 📁 03-weights
📁 📁 chatglm2-6b-int4
tokenizer.model [994.5 KB]
configuration_chatglm.py [2.2 KB]
modeling_chatglm.py [53.6 KB]
tokenizer_config.json [243.0 B]
config.json [1.1 KB]
MODEL_LICENSE [2.3 KB]
tokenization_chatglm.py [9.8 KB]
quantization.py [2.5 MB]
README.md [7.5 KB]
📁 📁 02-代码
finance_classify.py [4.4 KB]
finance_ie.py [4.9 KB]
finance_text_matching.py [3.1 KB]
📁 📁 01-讲义_0915135902
02-金融行业动态方向评估项目.pdf [583.3 KB]
1.环境要求.pdf [110.8 KB]
03-LLM实现金融文本分类.pdf [360.8 KB]
趋动云使用《补充》.pdf [3.1 MB]
📁 📁 01-讲义
03-LLM实现金融文本分类.pdf [360.8 KB]
02-金融行业动态方向评估项目.pdf [583.3 KB]
📁 📁 9月12号
📁 📁 01-讲义
01-大模型提示工程指南.pdf [1.3 MB]
📁 📁 8月31日
📁 📁 1.讲义
大模型应用工具实战02.pptx [19.9 MB]
📁 📁 9月4号
📁 📁 01-讲义
02-LLM主要架构介绍.pdf [7.7 MB]
01-LLM基础知识.pdf [11.4 MB]
📁 📁 02-代码
02-rouge.py [178.0 B]
01-bleu.py [545.0 B]
03-PPL.py [365.0 B]
大模型.xmind [214.3 KB]
大语言模型的背景.xmind [159.6 KB]
📁 📁 9月18号
📁 📁 02-代码
📁 📁 Dataset-of-financial-news-classification
Fiance_train_data.csv [912.4 KB]
Fiance_test_data.csv [159.9 KB]
translate_in_many_style.zip [79.3 MB]
📁 📁 01-讲义
星火大模型（博学谷）.pdf [12.1 MB]
📁 📁 9月10号
📁 📁 01-讲义
01-大模型prompt-Tuning方法入门.pdf [8.2 MB]
02-大模型prompt-Tuning方法进阶.pdf [9.2 MB]
📁 📁 9月7号
📁 📁 01-讲义
01-LLM主流开源大模型介绍.pdf [11.4 MB]
📁 📁 8月30日
📁 📁 1.讲义
大模型应用工具实战01.pptx [36.3 MB]
📁 📁 9月5号
📁 📁 01-讲义
01-LLM主要架构介绍.pdf [7.7 MB]
02-ChatGPT模型原理介绍.pdf [14.3 MB]
📁 📁 9月15号
📁 📁 02-代码在9月14号
📁 📁 03-视频
02-信息抽取2.mp4 [34.7 MB]
03-文本匹配.mp4 [58.9 MB]
01-信息抽取.mp4 [104.4 MB]
📁 📁 01-讲义
04-LLM实现金融信息抽取.pdf [330.9 KB]
05-LLM实现金融信息匹配.pdf [303.2 KB]
📁 📁 03阶段：配套资料
📁 📁 9月21号
📁 📁 01-讲义
03-阿里云注册及开通PAI.pdf [2.0 MB]
PAI平台开通指南.pdf [3.8 MB]
06-资源清理.pdf [1.5 MB]
02-阿里PAI平台.pdf [2.8 MB]
01-虚拟试衣背景.pdf [1.8 MB]
04-PAI_DSW的环境搭建.pdf [2.0 MB]
01-Function Call的原理及简单应用.pdf [2.2 MB]
05-虚拟试衣实践.pdf [5.2 MB]
📁 📁 03-代码
📁 📁 ChatGLM3_FunctionCall
📁 📁 sql
📁 📁 __pycache__
sql_function_tools.cpython-310.pyc [3.1 KB]
sql_function_tools.cpython-312.pyc [3.9 KB]
sql_function_tools.py [3.9 KB]
sql_zhipu.py [2.1 KB]
📁 📁 .idea
📁 📁 dataSources
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [268.0 B]
workspace.xml [11.7 KB]
ChatGLM3_FunctionCall.iml [478.0 B]
dataSources.local.xml [486.0 B]
dataSources.xml [530.0 B]
.gitignore [184.0 B]
modules.xml [301.0 B]
📁 📁 __pycache__
📁 📁 weather
📁 📁 __pycache__
tools.cpython-310.pyc [1.6 KB]
tools.cpython-38.pyc [1.6 KB]
tools.cpython-312.pyc [2.5 KB]
weather_zhipu.py [2.2 KB]
tools.py [2.3 KB]
cityCode_use.json [117.0 B]
📁 📁 airplane
📁 📁 __pycache__
airplane_function_tools.cpython-312.pyc [885.0 B]
muti_utils.cpython-38.pyc [1.1 KB]
muti_utils.cpython-310.pyc [1.1 KB]
muti_utils.cpython-312.pyc [1.8 KB]
airplane_function_tools.cpython-310.pyc [732.0 B]
airplane_function_tools.cpython-38.pyc [742.0 B]
muti_utils.py [1.1 KB]
airplane_function_tools.py [1.4 KB]
muti_function_zhipu.py [3.0 KB]
📁 📁 9月19号
📁 📁 02-数据
sample-text-dialog-unsort-jsonl.zip [154.3 KB]
清洗emoji数据的demo数据集.zip [219.7 KB]
📁 📁 01-讲义
01-阿里百炼平台.pdf [3.7 MB]
图表分析数据.md [4.7 KB]
01-千帆大模型.pdf [6.3 MB]
📁 📁 10月13号
📁 📁 01-讲义
01-大模型prompt-Tuning方法入门.pdf [1.9 MB]
📁 📁 10月8号
📁 📁 01-讲义
02-基于LangChain+ChatGLM-6B实现物流行业信息咨询.pdf [554.5 KB]
📁 📁 01-code
📁 📁 RAG
📁 📁 __pycache__
model.cpython-311.pyc [2.8 KB]
model.cpython-38.pyc [1.8 KB]
model.cpython-312.pyc [2.6 KB]
get_vector.cpython-312.pyc [1.3 KB]
get_vector.cpython-311.pyc [1.4 KB]
get_vector.cpython-310.pyc [977.0 B]
get_vector.cpython-38.pyc [939.0 B]
model.cpython-310.pyc [1.8 KB]
📁 📁 m3e-base
📁 📁 1_Pooling
config.json [190.0 B]
tokenizer_config.json [342.0 B]
gitattributes [1.5 KB]
vocab.txt [107.0 KB]
model.safetensors [390.1 MB]
README.md [26.0 KB]
config.json [932.0 B]
tokenizer.json [428.8 KB]
special_tokens_map.json [125.0 B]
sentence_bert_config.json [53.0 B]
pytorch_model.bin [390.2 MB]
modules.json [229.0 B]
📁 📁 faiss
📁 📁 camp
index.pkl [973.0 B]
index.faiss [6.0 KB]
📁 📁 logistics
index.faiss [9.0 KB]
index.pkl [1.1 KB]
📁 📁 chatglm2-6b-int4
pytorch_model.bin [3.7 GB]
MODEL_LICENSE [2.3 KB]
quantization.py [2.5 MB]
tokenizer_config.json [243.0 B]
tokenizer.model [994.5 KB]
config.json [1.1 KB]
tokenization_chatglm.py [9.8 KB]
README.md [7.5 KB]
modeling_chatglm.py [53.6 KB]
configuration_chatglm.py [2.2 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
RAG.iml [317.0 B]
workspace.xml [10.6 KB]
misc.xml [268.0 B]
.gitignore [184.0 B]
modules.xml [265.0 B]
物流信息.txt [550.0 B]
new_demo.py [3.1 KB]
get_vector.py [1.4 KB]
model.py [1.5 KB]
test.py [33.0 B]
main.py [1.5 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
modules.xml [273.0 B]
01-code.iml [317.0 B]
workspace.xml [15.5 KB]
misc.xml [268.0 B]
.gitignore [184.0 B]
📁 📁 10月10号
📁 📁 02-讲义
基于GPT2搭建医疗问诊机器人.pdf [612.9 KB]
📁 📁 01-code
📁 📁 Gpt2_Chatbot
📁 📁 templates
index.html [694.0 B]
index1.html [1.9 KB]
📁 📁 save_model
📁 📁 epoch97
pytorch_model.bin [378.5 MB]
config.json [838.0 B]
📁 📁 gpt2
generation_config.json [130.0 B]
vocab.json [1017.9 KB]
README.md [8.1 KB]
merges.txt [494.5 KB]
tokenizer.json [1.3 MB]
📁 📁 other_data
闲聊语料.pkl [68.8 MB]
闲聊语料.txt [65.0 MB]
📁 📁 save_model1
📁 📁 min_ppl_model_bj
generation_config.json [119.0 B]
config.json [977.0 B]
model.safetensors [366.5 MB]
📁 📁 vocab
vocab.txt [74.3 KB]
vocab2.txt [127.6 KB]
📁 📁 data_preprocess
dataset.py [2.1 KB]
preprocess.py [3.9 KB]
dataloader.py [4.4 KB]
__init__.py [70.0 B]
📁 📁 config
config.json [875.0 B]
📁 📁 data
medical_train.txt [9.5 MB]
medical_valid.txt [130.8 KB]
medical_valid.pkl [134.3 KB]
medical_train.pkl [9.8 MB]
__init__.py [72.0 B]
parameter_config.py [2.6 KB]
flask_predict.py [2.6 KB]
app.py [487.0 B]
interact.py [5.5 KB]
train.py [11.4 KB]
readme [1.9 KB]
functions_tools.py [3.3 KB]
📁 📁 9月24号
📁 📁 01-讲义(1)
SQL.pdf [29.0 KB]
01-Function Call的原理及应用.pdf [797.7 KB]
📁 📁 02-code
📁 📁 ChatGLM3_FunctionCall
📁 📁 __pycache__
📁 📁 sql
📁 📁 __pycache__
sql_function_tools.cpython-310.pyc [3.1 KB]
sql_zhipu.py [2.1 KB]
sql_function_tools.py [3.9 KB]
📁 📁 weather
📁 📁 __pycache__
tools.cpython-38.pyc [1.6 KB]
tools.cpython-310.pyc [1.6 KB]
weather_zhipu.py [2.2 KB]
tools.py [2.2 KB]
cityCode_use.json [117.0 B]
📁 📁 airplane
📁 📁 __pycache__
airplane_function_tools.cpython-38.pyc [742.0 B]
muti_utils.cpython-38.pyc [1.1 KB]
airplane_function_tools.cpython-310.pyc [732.0 B]
muti_utils.cpython-310.pyc [1.1 KB]
airplane_function_tools.py [1.4 KB]
muti_function_zhipu.py [2.9 KB]
muti_utils.py [1.1 KB]
📁 📁 9月28日
📁 📁 01-讲义
01-AI Agents的开发应用.pdf [1.1 MB]
01-LangChain基础知识入门.pdf [851.8 KB]
📁 📁 02-代码
📁 📁 Agent_Email_Generate
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
modules.xml [299.0 B]
workspace.xml [6.2 KB]
Agent_Email_Generate.iml [317.0 B]
misc.xml [268.0 B]
.gitignore [184.0 B]
📁 📁 tools
📁 📁 __pycache__
__init__.cpython-310.pyc [170.0 B]
custom_tools.cpython-310.pyc [2.0 KB]
custom_tools.cpython-312.pyc [2.8 KB]
__init__.cpython-312.pyc [244.0 B]
custom_tools.py [2.9 KB]
__init__(1).py
__init__.py
email_category.txt [943.0 B]
poie.txt [35.0 B]
test.py [124.0 B]
__init__.py
main.py [4.2 KB]
📁 📁 longchain
📁 📁 Prompts_module
demo_few_shot.py [1.3 KB]
demo_zero_shot.py [624.0 B]
📁 📁 Models_module
demo_chat_models.py [428.0 B]
demo_llms.py [313.0 B]
demo_embedding_models.py [622.0 B]
📁 📁 Memory_module
demo_up_memory.py [675.0 B]
demo_memory.py [225.0 B]
demo_message_dict.py [428.0 B]
📁 📁 Indexes_module
demo_vector.py [818.0 B]
pku.txt [4.2 KB]
衣服属性.txt [819.0 B]
demo_retriver.py [940.0 B]
demo_text_split.py [661.0 B]
demo_dataloader.py [506.0 B]
📁 📁 Chains_module
demo_use_LLMChain.py [615.0 B]
demo_use_simpleChain.py [1.1 KB]
📁 📁 Agents_module
demo_agent.py [1000.0 B]
📁 📁 9月26号
📁 📁 01-讲义
01-LLM基础知识.pdf [1.2 MB]
02-Assistant API的原理及应用.pdf [579.6 KB]
01-GPTs的介绍及应用.pdf [727.2 KB]
📁 📁 03-code
📁 📁 MiniMax_Assistant
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
.gitignore [184.0 B]
modules.xml [293.0 B]
MiniMax_Assistant.iml [291.0 B]
workspace.xml [5.1 KB]
misc.xml [310.0 B]
fruit_price.txt [250.0 B]
minmax_assistant.py [5.3 KB]
📁 📁 04阶段：配套资料
📁 📁 10月14号
📁 📁 01-讲义
02-大模型prompt-Tuning方法进阶.pdf [1.5 MB]
📁 📁 10月15号
📁 📁 01-讲义
02-基于BERT+PET方式文本分类介绍.pdf [435.6 KB]
03-基于BERT+PET方式数据预处理介绍.pdf [387.3 KB]
01-项目背景介绍.pdf [1.6 MB]
📁 📁 02-代码
📁 📁 Gpt2_Chatbot
📁 📁 other_data
闲聊语料.pkl [68.8 MB]
闲聊语料.txt [65.0 MB]
📁 📁 data_preprocess
__init__.py [70.0 B]
dataset.py [2.1 KB]
preprocess.py [3.9 KB]
dataloader.py [4.4 KB]
📁 📁 config
config.json [875.0 B]
📁 📁 save_model
📁 📁 epoch97
pytorch_model.bin [378.5 MB]
config.json [838.0 B]
📁 📁 save_model1
📁 📁 min_ppl_model_bj
generation_config.json [119.0 B]
config.json [977.0 B]
model.safetensors [366.5 MB]
📁 📁 vocab
vocab2.txt [127.6 KB]
vocab.txt [74.3 KB]
📁 📁 gpt2
merges.txt [494.5 KB]
generation_config.json [130.0 B]
README.md [8.1 KB]
tokenizer.json [1.3 MB]
vocab.json [1017.9 KB]
📁 📁 data
medical_valid.txt [130.8 KB]
medical_valid.pkl [134.3 KB]
medical_train.pkl [9.8 MB]
medical_train.txt [9.5 MB]
📁 📁 templates
index1.html [1.9 KB]
index.html [694.0 B]
__init__.py [72.0 B]
readme [1.9 KB]
train.py [11.4 KB]
functions_tools.py [3.3 KB]
app.py [487.0 B]
interact.py [5.5 KB]
parameter_config.py [2.6 KB]
flask_predict.py [2.6 KB]
📁 📁 PET
📁 📁 __pycache__
pet_config.cpython-312.pyc [2.3 KB]
📁 📁 utils
📁 📁 __pycache__
common_utils.cpython-312.pyc [3.9 KB]
verbalizer.cpython-312.pyc [9.2 KB]
metirc_utils.cpython-312.pyc [6.1 KB]
__init__.cpython-312.pyc [245.0 B]
verbalizer.py [8.0 KB]
__init__.py
common_utils.py [5.1 KB]
metirc_utils.py [4.7 KB]
📁 📁 data_handle
📁 📁 __pycache__
__init__.cpython-312.pyc [251.0 B]
data_preprocess.cpython-312.pyc [4.7 KB]
template.cpython-312.pyc [5.8 KB]
data_loader.cpython-312.pyc [2.2 KB]
data_preprocess.py [5.5 KB]
template.py [5.0 KB]
__init__.py
data_loader.py [1.9 KB]
📁 📁 checkpoints
📁 📁 model_best_old
tokenizer.json [428.8 KB]
special_tokens_map.json [125.0 B]
tokenizer_config.json [1.2 KB]
vocab.txt [107.0 KB]
model.safetensors [390.2 MB]
pytorch_model.bin [390.3 MB]
generation_config.json [90.0 B]
config.json [866.0 B]
📁 📁 data
verbalizer.txt [139.0 B]
dev.txt [98.7 KB]
train.txt [9.6 KB]
prompt.txt [37.0 B]
pet_config.py [1.6 KB]
inference.py [3.9 KB]
__init__.py
train.py [7.8 KB]
📁 📁 预训练模型
📁 📁 bert-base-chinese
tokenizer.json [262.6 KB]
flax_model.msgpack [390.2 MB]
pytorch_model.bin [392.5 MB]
README.md [21.0 B]
vocab.txt [107.0 KB]
config.json [624.0 B]
tokenizer_config.json [29.0 B]
📁 📁 10月24号
📁 📁 01-讲义（同22号）
📁 📁 02-代码（同22号）
📁 📁 10月21号
📁 📁 02-代码
📁 📁 P-Tuning
📁 📁 data_handle
📁 📁 __pycache__
data_preprocess.cpython-312.pyc [5.0 KB]
data_loader.cpython-312.pyc [2.1 KB]
__init__.cpython-312.pyc [256.0 B]
data_preprocess.py [6.3 KB]
__init__.py
data_loader.py [1.6 KB]
📁 📁 __pycache__
ptune_config.cpython-312.pyc [2.2 KB]
📁 📁 data
verbalizer.txt [139.0 B]
dev.txt [70.2 KB]
train.txt [9.6 KB]
📁 📁 checkpoints
📁 📁 model_20
config.json [1.1 KB]
model.safetensors [340.6 MB]
generation_config.json [95.0 B]
📁 📁 model_old_best
special_tokens_map.json [125.0 B]
model.safetensors [390.2 MB]
pytorch_model.bin [390.3 MB]
tokenizer.json [428.8 KB]
config.json [867.0 B]
tokenizer_config.json [338.0 B]
vocab.txt [107.0 KB]
generation_config.json [90.0 B]
📁 📁 utils
📁 📁 __pycache__
__init__.cpython-312.pyc [250.0 B]
verbalizer.cpython-312.pyc [9.3 KB]
metirc_utils.cpython-312.pyc [6.3 KB]
common_utils.cpython-312.pyc [4.0 KB]
metirc_utils.py [4.6 KB]
common_utils.py [4.0 KB]
verbalizer.py [7.6 KB]
__init__.py
train.py [7.5 KB]
__init__.py
inference.py [3.2 KB]
ptune_config.py [1.5 KB]
📁 📁 01-讲义
07-基于BERT+P-Tuning方式文本分类模型搭建.pdf [427.3 KB]
06-基于BERT+P-Tuning方式数据预处理介绍.pdf [390.3 KB]
📁 📁 10月19号
📁 📁 02-代码
📁 📁 ptune_chatglm
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
modules.xml [285.0 B]
misc.xml [268.0 B]
ptune_chatglm.iml [480.0 B]
.gitignore [184.0 B]
workspace.xml [7.9 KB]
📁 📁 checkpoints
📁 📁 ptune
📁 📁 __pycache__
glm_config.cpython-312.pyc [2.1 KB]
📁 📁 data
mixed_dev_dataset.jsonl [64.9 KB]
dataset.jsonl [4.4 KB]
mixed_train_dataset.jsonl [496.8 KB]
📁 📁 data_handle
📁 📁 __pycache__
data_loader.cpython-312.pyc [2.0 KB]
data_preprocess.cpython-312.pyc [5.7 KB]
__init__.cpython-312.pyc [274.0 B]
data_loader.py [1.5 KB]
data_preprocess.py [6.2 KB]
__init__.py
📁 📁 utils
📁 📁 __pycache__
__init__.cpython-312.pyc [268.0 B]
common_utils.cpython-312.pyc [1.8 KB]
__init__.py
common_utils.py [966.0 B]
__init__.py [22.0 B]
train.py [7.0 KB]
inference.py [2.7 KB]
glm_config.py [1.4 KB]
📁 📁 chatglm-6b
tokenizer_config.json [441.0 B]
config.json [773.0 B]
pytorch_model-00004-of-00008.bin [1.8 GB]
pytorch_model.bin.index.json [32.6 KB]
pytorch_model-00003-of-00008.bin [1.8 GB]
MODEL_LICENSE [4.2 KB]
pytorch_model-00002-of-00008.bin [1.8 GB]
tokenization_chatglm.py [16.6 KB]
modeling_chatglm.py [56.3 KB]
test_modeling_chatglm.py [13.5 KB]
README.md [6.7 KB]
pytorch_model-00005-of-00008.bin [1.8 GB]
quantization.py [14.7 KB]
configuration_chatglm.py [4.2 KB]
pytorch_model-00002-of-00008(1).bin [1.8 GB]
pytorch_model-00008-of-00008.bin [1019.8 MB]
pytorch_model-00007-of-00008.bin [1.0 GB]
pytorch_model-00006-of-00008.bin [1.8 GB]
LICENSE [11.1 KB]
ice_text.model [2.6 MB]
📁 📁 01-讲义
新媒体行业评论智能分类与信息抽取系统.pdf [722.3 KB]
📁 📁 10月22号
📁 📁 02-代码
📁 📁 ptune_chatglm
📁 📁 utils
📁 📁 __pycache__
__init__.cpython-312.pyc [268.0 B]
common_utils.cpython-312.pyc [1.8 KB]
common_utils.py [966.0 B]
__init__.py
📁 📁 data
mixed_train_dataset.jsonl [496.8 KB]
mixed_dev_dataset.jsonl [64.9 KB]
dataset.jsonl [4.4 KB]
📁 📁 data_handle
📁 📁 __pycache__
__init__.cpython-312.pyc [274.0 B]
data_preprocess.cpython-312.pyc [5.7 KB]
data_loader.cpython-312.pyc [2.0 KB]
__init__.py
data_loader.py [1.5 KB]
data_preprocess.py [6.2 KB]
📁 📁 __pycache__
glm_config.cpython-312.pyc [2.1 KB]
📁 📁 checkpoints
📁 📁 ptune
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [268.0 B]
modules.xml [285.0 B]
.gitignore [184.0 B]
ptune_chatglm.iml [480.0 B]
workspace.xml [7.9 KB]
train.py [7.0 KB]
glm_config.py [1.4 KB]
__init__.py [22.0 B]
inference.py [2.7 KB]
📁 📁 chatglm-6b
config.json [773.0 B]
pytorch_model-00005-of-00008.bin [1.8 GB]
modeling_chatglm.py [56.3 KB]
pytorch_model-00001-of-00008.bin [1.6 GB]
pytorch_model-00004-of-00008.bin [1.8 GB]
MODEL_LICENSE [4.2 KB]
tokenization_chatglm.py [16.6 KB]
LICENSE [11.1 KB]
quantization.py [14.7 KB]
pytorch_model-00007-of-00008.bin [1.0 GB]
configuration_chatglm.py [4.2 KB]
pytorch_model-00003-of-00008.bin [1.8 GB]
tokenizer_config.json [441.0 B]
test_modeling_chatglm.py [13.5 KB]
pytorch_model.bin.index.json [32.6 KB]
pytorch_model-00006-of-00008.bin [1.8 GB]
pytorch_model-00008-of-00008.bin [1019.8 MB]
ice_text.model [2.6 MB]
README.md [6.7 KB]
📁 📁 01-讲义
新媒体行业评论智能分类与信息抽取系统.pdf [722.3 KB]
📁 📁 10月17号
📁 📁 01-讲义
06-基于BERT+P-Tuning方式数据预处理介绍.pdf [390.3 KB]
07-基于BERT+P-Tuning方式文本分类模型搭建.pdf [427.3 KB]
📁 📁 02-代码
📁 📁 P-Tuning
📁 📁 __pycache__
ptune_config.cpython-312.pyc [2.2 KB]
📁 📁 utils
📁 📁 __pycache__
common_utils.cpython-312.pyc [4.0 KB]
verbalizer.cpython-312.pyc [9.3 KB]
__init__.cpython-312.pyc [250.0 B]
metirc_utils.cpython-312.pyc [6.3 KB]
__init__.py
metirc_utils.py [4.6 KB]
verbalizer.py [7.6 KB]
common_utils.py [4.0 KB]
📁 📁 data_handle
📁 📁 __pycache__
__init__.cpython-312.pyc [256.0 B]
data_loader.cpython-312.pyc [2.1 KB]
data_preprocess.cpython-312.pyc [5.0 KB]
data_preprocess.py [6.3 KB]
data_loader.py [1.6 KB]
__init__.py
📁 📁 checkpoints
📁 📁 model_old_best
config.json [867.0 B]
tokenizer_config.json [338.0 B]
model.safetensors [390.2 MB]
vocab.txt [107.0 KB]
pytorch_model.bin [390.3 MB]
special_tokens_map.json [125.0 B]
generation_config.json [90.0 B]
tokenizer.json [428.8 KB]
📁 📁 model_20
model.safetensors [340.6 MB]
generation_config.json [95.0 B]
config.json [1.1 KB]
📁 📁 data
dev.txt [70.2 KB]
verbalizer.txt [139.0 B]
train.txt [9.6 KB]
ptune_config.py [1.5 KB]
inference.py [3.2 KB]
__init__.py
train.py [7.5 KB]
📁 📁 10月26号
📁 📁 01-讲义
01-GPTs的介绍及应用.pdf [727.2 KB]
02-Assistant API的原理及应用.pdf [579.6 KB]
01-LLM基础知识.pdf [1.2 MB]
📁 📁 02-代码
📁 📁 01阶段：配套资料
📁 📁 07
📁 📁 01-讲义
02-LLM主要架构介绍.pdf [7.7 MB]
01-LLM基础知识.pdf [11.4 MB]
📁 📁 02-代码
03-PPL.py [365.0 B]
02-rouge.py [178.0 B]
01-bleu.py [545.0 B]
大模型项目研发流程.pdf [279.6 KB]
LLM背景介绍.pdf [42.7 KB]
📁 📁 02
📁 📁 04-拓展
拓展2_Pytorch-CUDA环境配置.pdf [485.3 KB]
拓展1_深度学习拓展.pdf [1.4 MB]
📁 📁 03-代码
📁 📁 02-神经网络
📁 📁 dataset
手机价格预测.csv [119.5 KB]
phone.pth [145.3 KB]
📁 📁 model
phone.pth [145.8 KB]
05-参数初始化.py [1.5 KB]
11-正则化.py [459.0 B]
04-激活函数-Softmax.py [187.0 B]
07-损失函数.py [2.6 KB]
10-学习率衰减方法.py [3.8 KB]
08-反向传播BP算法.py [1.7 KB]
03-激活函数-ReLU.py [463.0 B]
01-激活函数-sigmoid.py [588.0 B]
12-案例-价格分类.py [4.4 KB]
09-梯度下降优化方法.py [3.9 KB]
13-Transformer汉译英.py [367.0 B]
02-激活函数-tanh.py [460.0 B]
06-搭建神经网络.py [1.9 KB]
📁 📁 01-Pytroch基本使用
07-张量的拼接.py [205.0 B]
08-案例-线性回归模型构建.py [3.1 KB]
04-张量的运算函数.py [412.0 B]
06-张量的形状操作.py [712.0 B]
03-张量的数值计算.py [276.0 B]
02-张量类型转换.py [561.0 B]
05-张量的索引操作.py [406.0 B]
01-张量创建.py [849.0 B]
📁 📁 02-笔记
深度学习基础.pdf [310.7 KB]
📁 📁 01-讲义
01-PyTorch基本使用.pdf [1.1 MB]
00-深度学习简介.pdf [631.0 KB]
📁 📁 08
📁 📁 01-讲义
01-LLM主要架构介绍.pdf [7.7 MB]
02-ChatGPT模型原理介绍.pdf [14.3 MB]
📁 📁 09
📁 📁 01-讲义
01-LLM主流开源大模型介绍.pdf [11.4 MB]
开源的LLM.pdf [32.6 KB]
📁 📁 05
📁 📁 讲义
大模型应用工具实战01.pdf [6.4 MB]
📁 📁 软件
VSCodeUserSetup-x64-1.89.1.exe [94.9 MB]
📁 📁 01
📁 📁 02-软件
📁 📁 Anaconda
Anaconda3-2023.09-0-Windows-x86_64.exe [1.0 GB]
📁 📁 PyCharm
pycharm-professional-2021.2.1.exe [463.6 MB]
📁 📁 01-讲义
Python入门教程.pdf [1.9 MB]
📁 📁 03-代码
📁 📁 【5月21日】代码
11-Python中的转义字符.py [452.0 B]
04-Python中变量定义.py [785.0 B]
05-Python中的变量命名规则.py [278.0 B]
03-Python中的多行注释.py [154.0 B]
02-Python中的单行注释.py [240.0 B]
10-Python中变量的格式化输出.py [580.0 B]
07-Python中运算符.py [486.0 B]
01-Python程序入门.py [20.0 B]
06-Python中变量7种数据类型.py [1.0 KB]
08-Python中的输入操作.py [709.0 B]
09-Python中的普通输出操作.py [248.0 B]
📁 📁 【5月26日】代码
03-Python中函数的返回值.py [614.0 B]
20-Python中对象成员方法的self关键词.py [637.0 B]
17-Python中的匿名函数.py [760.0 B]
04-Python中return返回值.py [546.0 B]
09-Python中局部变量的访问范围.py [591.0 B]
19-Python中类的定义与实例化.py [429.0 B]
26-Python中的魔术方法__call__.py [380.0 B]
30-Python中的重写机制.py [1.0 KB]
31-Python中的super()方法.py [1.3 KB]
25-Python中使用__del__()魔术方法.py [824.0 B]
02-Python中函数的参数.py [783.0 B]
33-Python中多继承（继承链）.py [474.0 B]
14-Python中不定长参数.py [679.0 B]
12-Python中函数的两种传参方式.py [576.0 B]
01-Python函数的基本概念.py [811.0 B]
34-Python中的继承关系（继承链）.py [621.0 B]
22-Python中魔术方法.py [854.0 B]
15-Python中不定长参数混用的情况.py [364.0 B]
21-Python中成员属性的定义.py [590.0 B]
06-Python中使用函数生成一个4位长度的验证码.py [1.2 KB]
07-Python中变量的作用域.py [690.0 B]
24-Python中使用__str__()魔术方法.py [762.0 B]
32-Python中的多继承.py [733.0 B]
29-Python中继承的实现.py [712.0 B]
18-Python中带参数的lambda表达式.py [385.0 B]
11-Python中函数的两种的参数.py [458.0 B]
16-Python中的不定长参数接收容器类型的参数.py [497.0 B]
08-Python中全局变量的访问范围.py [321.0 B]
13-Python中默认值参数.py [578.0 B]
28-Python中私有方法.py [560.0 B]
23-Python中使用魔术方法实现属性的定义.py [564.0 B]
27-Python中的公有属性和私有属性.py [1.1 KB]
05-Python中return返回值返回多个结果.py [316.0 B]
10-Python中的global关键字.py [501.0 B]
📁 📁 【5月23日】代码
08-Python中的模块.py [597.0 B]
09-Python中的循环结构.py [563.0 B]
15-Python中列表的其他操作.py [285.0 B]
06-Python中if嵌套结构.py [1.2 KB]
14-Python中的列表容器.py [768.0 B]
07-Python中猜拳游戏实现.py [1.3 KB]
16-Python中列表的切片操作（字符串元组也可以使用）.py [846.0 B]
10-Python中实现指定次数的循环.py [599.0 B]
04-Python中的if...else选择结构.py [572.0 B]
05-Python中if...elif...else结构.py [721.0 B]
03-Python中的if...else选择结构.py [474.0 B]
01-Python中的编程语言的流程结构.py [253.0 B]
12-Python中循环的两大关键词.py [1.2 KB]
18-Python中的字典类型.py [900.0 B]
13-Python中猜数字游戏的开发.py [1.0 KB]
19-Python中的集合类型.py [250.0 B]
11-Python中实现求1-100累加的结果.py [314.0 B]
17-Python中元组的定义与使用.py [573.0 B]
02-Python中的选择结构.py [800.0 B]
📁 📁 06
📁 📁 软件
StreamingTool-7.6.2-x64.exe [355.0 MB]
yuan-live Setup 2.6.2.exe [123.9 MB]
作业.txt [264.0 B]
大模型应用工具实战02.pdf [7.7 MB]
📁 📁 04
📁 📁 02-笔记
深度学习基础0601.pdf [3.3 MB]
📁 📁 03-代码
📁 📁 04-循环神经网络
📁 📁 data
jaychou_lyrics.txt [167.2 KB]
lyrics_model_10.pth [5.7 MB]
02-RNN层的使用.py [639.0 B]
01-词嵌入层API.py [896.0 B]
03-RNN实现周杰伦歌词生成.py [6.7 KB]
lyrics_model_10.pth [5.7 MB]
📁 📁 02-神经网络
📁 📁 model
phone.pth [145.8 KB]
📁 📁 dataset
phone.pth [145.3 KB]
手机价格预测.csv [119.5 KB]
12-案例-价格分类.py [4.4 KB]
08-反向传播BP算法.py [1.7 KB]
05-参数初始化.py [1.5 KB]
04-激活函数-Softmax.py [187.0 B]
06-搭建神经网络.py [1.9 KB]
10-学习率衰减方法.py [3.8 KB]
03-激活函数-ReLU.py [463.0 B]
07-损失函数.py [2.6 KB]
11-正则化.py [459.0 B]
13-Transformer汉译英.py [367.0 B]
02-激活函数-tanh.py [460.0 B]
01-激活函数-sigmoid.py [588.0 B]
09-梯度下降优化方法.py [4.6 KB]
📁 📁 03-卷积神经网络
📁 📁 data
📁 📁 cifar-10-batches-py
test_batch [29.6 MB]
data_batch_4 [29.6 MB]
batches.meta [158.0 B]
data_batch_2 [29.6 MB]
data_batch_1 [29.6 MB]
readme.html [88.0 B]
data_batch_3 [29.6 MB]
data_batch_5 [29.6 MB]
img.jpg [44.4 KB]
image_classification.pth [320.9 KB]
03-pytorch池化API.py [1.0 KB]
04-案例-卷积神经网络实现图像分类.py [4.4 KB]
01-matplotlib图像加载.py [623.0 B]
02-pytorch卷积层API.py [872.0 B]
📁 📁 01-讲义
02-神经网络基础.pdf [2.1 MB]
05-循环神经网络.pdf [933.5 KB]
04-卷积神经网络.pdf [1.2 MB]
03-Transformer详解.pdf [3.7 MB]
📁 📁 03
📁 📁 03-代码
📁 📁 02-神经网络
📁 📁 model
phone.pth [145.8 KB]
📁 📁 dataset
手机价格预测.csv [119.5 KB]
phone.pth [145.3 KB]
09-梯度下降优化方法.py [4.6 KB]
06-搭建神经网络.py [1.9 KB]
11-正则化.py [459.0 B]
07-损失函数.py [2.6 KB]
04-激活函数-Softmax.py [187.0 B]
01-激活函数-sigmoid.py [588.0 B]
05-参数初始化.py [1.5 KB]
08-反向传播BP算法.py [1.7 KB]
10-学习率衰减方法.py [3.8 KB]
12-案例-价格分类.py [4.4 KB]
02-激活函数-tanh.py [460.0 B]
13-Transformer汉译英.py [367.0 B]
📁 📁 02-神经网络
03-激活函数-ReLU.py [463.0 B]
📁 📁 04-循环神经网络
📁 📁 data
jaychou_lyrics.txt [167.2 KB]
lyrics_model_10.pth [5.7 MB]
02-RNN层的使用.py [639.0 B]
01-词嵌入层API.py [896.0 B]
03-RNN实现周杰伦歌词生成.py [6.7 KB]
📁 📁 03-卷积神经网络
📁 📁 data
📁 📁 cifar-10-batches-py
data_batch_3 [29.6 MB]
batches.meta [158.0 B]
test_batch [29.6 MB]
data_batch_5 [29.6 MB]
data_batch_2 [29.6 MB]
readme.html [88.0 B]
data_batch_1 [29.6 MB]
data_batch_4 [29.6 MB]
img.jpg [44.4 KB]
image_classification.pth [320.9 KB]
01-matplotlib图像加载.py [623.0 B]
03-pytorch池化API.py [1.0 KB]
02-pytorch卷积层API.py [872.0 B]
04-案例-卷积神经网络实现图像分类.py [4.4 KB]
📁 📁 02-笔记
深度学习基础0530.pdf [898.7 KB]
📁 📁 01-讲义
03-Transformer详解.pdf [3.7 MB]
02-神经网络基础.pdf [2.1 MB]
📁 📁 04-拓展
拓展3_Pycharm配置Anaconda环境.pdf [658.6 KB]
📁 📁 05阶段：配套资料
📁 📁 项目资料
📁 📁 02-代码
📁 📁 img_Plaidshirtprogrammer
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00081-2356360208.txt [24.0 B]
00064-4096775220.txt [24.0 B]
00072-2356360199.txt [24.0 B]
00070-2356360197.png [453.9 KB]
00083-2356360210.png [329.9 KB]
00074-2356360201.png [380.1 KB]
📁 📁 weights
glass.safetensors [36.1 MB]
model-plaidshirtprogrammer.ckpt [2.0 GB]
aigc_demo_origin.zip [6.4 MB]
📁 📁 01-讲义
03-stableDiffusion详解.pdf [4.9 MB]
05-腾讯云AI绘画.pdf [13.5 MB]
04-StableDiffusion实践.pdf [2.3 MB]适合人群
- AI领域初学者
- Python爱好者
- 对大模型感兴趣的编程者
🎯 学习收获
- 掌握大模型基础知识
- 精通Python语言在大模型中的应用
- 提升AI领域技能
🎉 祝您学习愉快！