人工智能與ChatGPT

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人工智能與ChatGPT 預覽內頁
  • 出版商: 清華大學
  • 出版日期: 2023-07-01
  • 定價: $594
  • 售價: 7.5 折 $446
  • 語言: 簡體中文
  • 頁數: 308
  • 裝訂: 平裝
  • ISBN: 7302638179
  • ISBN-13: 9787302638179
  • 相關分類: ChatGPT

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人們相信人工智能可以為這個時代的技術帶來突破，而ChatGPT則使這種希望成為現實。現在，許多人都渴望瞭解與ChatGPT相關的一切，包括技術的歷史和背景，其神奇的功能以及如何使用它。雖然ChatGPT的使用方法很簡單，但它具有無限的潛力。如果不去親身體驗，很難體會到它的強大之處。本書盡可能全面地介紹了與ChatGPT相關的內容，特別是許多應用示例，可以給讀者帶來啟發。 希望讀者通過這本書瞭解ChatGPT後，在自己的工作中也能充分利用它。本書適合希望瞭解和使用ChatGPT的人閱讀。

目錄大綱

目錄

 

第1章 人工智能概述 ·························································1

 

1.1 什麼是人工智能 ························1

 

1.2 人工智能的發展歷史 ··················2

 

1.3 人工智能的分類 ························4

 

1.4 機器學習 ································5

 

1.5 深度學習 ································6

 

1.6 通用人工智能（AGI） ·················9

 

1.7 自然語言處理 ··························10

 

1.8 生成式人工智能（AIGC） ············11

 

1.9 強化學習 ·······························12

 

第2章 自然語言處理 ·······················································15

 

2.1 自然語言處理的基本概念 ············15

 

2.2 自然語言處理的主要技術 ············15

 

2.3 自然語言處理的發展歷史 ············16

 

2.4 語言模型 ·······························19

 

2.5 文本分類和聚類 ·······················24

 

2.6 分詞和詞性標註 ·······················26

 

2.7 命名實體識別 ··························28

 

2.8 句法分析 ·······························29

 

2.9 情感分析 ·······························30

 

2.10 機器翻譯 ······························32

 

2.11 文本摘要 ······························33

 

2.12 自然語言處理的商業應用 ···········34

 

2.13 自然語言處理的發展趨勢 ···········39

 

第3章 OpenAI公司及其產品 ············································40

 

3.1 OpenAI公司簡介 ·····················40

 

3.2 OpenAI公司發展歷史 ················40

 

3.3 OpenAI和微軟的合作 ················41

 

3.4 OpenAI公司主要產品 ················42

 

第4章 ChatGPT關聯技術 ················································46

 

4.1 前饋神經網絡 ··························46

 

4.2 序列到序列模型（Seq2Seq） ·········47

 

4.3 自註意力機制 ··························47

 

4.4 多頭自註意力機制 ····················48

 

4.5 自監督學習 ····························48

 

4.6 Transformer 模型······················49

 

4.7 語言生成技術 ··························51

 

4.8 多語種語言模型 ·······················52

 

4.9 預訓練語言模型 ·······················53

 

4.10 生成式預訓練模型（GPT） ·········54

 

4.11 近端策略優化算法（PPO） ·········54

 

4.12 詞嵌入 ································55

 

4.13 Softmax分類器 ······················56

 

4.14 指示學習和提示學習 ················57

 

 

 

IV

4.15 人類反饋強化學習（RLHF） ·······584.16 多模態 ································594.17 生成式對抗網絡······················604.18 知識圖譜和實體鏈接 ················614.19 GPU、TPU與模型訓練 ·············61

第5章 ChatGPT介紹 ······················································665.1 ChatGPT的主要功能 ·················665.2 ChatGPT的開發歷史 ·················675.3 ChatGPT的開發目標 ·················675.4 GPT模型的演化 ······················685.5 GPT-3到ChatGPT的演化 ···········715.6 模型的突破davinci-002 ··············735.7 ChatGPT的模型調用 ·················745.8 ChatGPT的訓練過程 ·················745.9 預訓練素材來源 ·······················765.10 訓練數據集 ···························775.11 數據集標註 ···························785.12 RLHF應用 ···························795.13 計算資源與參數構成 ················815.14 ChatGPT存在的問題 ················82

第6章 GPT–3.5引擎介紹 ·················································846.1 GPT-3引擎 ····························846.2 GPT-3.5引擎 ··························856.3 ChatGPT和GPT-3的區別 ···········856.4 預訓練 ··································856.5 詞嵌入應用 ····························866.6 多層Transformer模塊 ···············876.7 模型變體 ·······························88

第7章 ChatGPT使用指南 ················································907.1 如何訪問ChatGPT····················907.2 如何更有效地提問 ····················917.3 提問技巧 ·······························957.4 會話線程 ·······························967.5 上下文 ··································977.6 重生成答案 ····························987.7 應對回答字數限制 ····················997.8 使用小技巧 ···························103

第8章 ChatGPT應用形式 ··············································1048.1 計算 ····································1048.2 寫代碼 ·································1068.3 解釋代碼 ······························1078.4 高級語言轉換成匯編語言 ···········1088.5 反匯編 ·································1108.6 程序文檔生成 ·························1118.7 程序語言轉換 ·························1128.8 程序模擬運行 ·························1138.9 代碼增加註釋 ·························1138.10 時間復雜度計算·····················1148.11 代碼優化方案 ·······················1158.12 修復代碼Bug ·······················1168.13 查詢公式 ·····························1178.14 生成復雜公式 ·······················1198.15 生成圖片（通過代碼運行） ········1208.16 生成表格 ·····························122

人工智能與ChatGPT 4校 文前.indd 42023/6/24 18:13:37

 

目 錄

 

V

 

8.17 生成數據庫文檔·····················123

 

8.18 自動生成SQL代碼 ·················123

 

8.19 不同數據庫SQL命令轉換 ········124

 

8.20 提取關鍵字 ··························126

 

8.21 取名 ··································126

 

8.22 轉換人稱 ·····························127

 

8.23 整理文字 ·····························127

 

8.24 生成流程圖 ··························128

 

8.25 英語論文摘要 ·······················130

 

第9章 OpenAI API ························································132

 

9.1 API概論 ·······························132

 

9.2 交互方式 ······························132

 

9.3 關鍵概念 ······························133

 

9.4 Playground工具 ······················135

 

9.5 API例子 ·······························136

 

9.6 API訪問 ·······························137

 

9.7 API使用 ·······························138

 

9.8 API參數 ·······························139

 

9.9 API功能模塊 ·························142

 

9.10 API端點（Endpoints） ·············143

 

9.11 文本生成 ·····························144

 

9.12 語言翻譯 ·····························145

 

9.13 情感分析 ·····························145

 

9.14 文本摘要 ·····························147

 

9.15 文本相似度 ··························149

 

9.16 文本分類 ·····························149

 

9.17 命名實體識別 ·······················152

 

9.18 聊天機器人 ··························153

 

9.19 設置API響應字符數 ···············155

 

9.20 API應用案例 ························156

 

第10章 構建自己的ChatGPT模型 ···································160

 

10.1 為什麼需要 ··························160

 

10.2 如何訓練 ·····························160

 

10.3 如何使用 ·····························161

 

10.4 訓練代碼示例 ·······················161

 

10.5 模型使用代碼示例 ··················163

 

10.6 訓練數據集格式·····················164

 

10.7 企業專有模型構建 ··················164

 

第11章 ChatGPT用於數據分析 ·······································167

 

11.1 數據分析簡介 ·······················167

 

11.2 數據準備 ·····························167

 

11.3 數據的可視化 ·······················170

 

11.4 聚類分析 ·····························180

 

11.5 相關性分析 ··························184

 

11.6 預測 ··································186

 

第12章 ChatGPT在不同領域的應用 ································190

 

12.1 工業領域 ·····························190

 

12.2 醫療領域 ·····························192

 

12.3 金融領域 ·····························193

 

12.4 教育領域 ·····························194

 

12.5 知識產權領域 ·······················195

 

 

 

VI

第13章綜合應用示例 ····················································19813.1 籌備會議 ·····························19813.2 擬訂方案 ·····························20413.3 申請專利 ·····························20913.4 軟件開發 ·····························21813.5 解決生產技術問題 ··················238

第14章教育行業應用示例 ··············································24614.1 擬定教學大綱 ·······················24614.2 撰寫教案 ·····························25414.3 製作教學PPT ·······················26414.4 出試捲 ·······························26714.5 編寫畢業設計材料 ··················27314.6 撰寫畢業論文 ·······················28614.7 準備新建專業材料 ··················295

參考文獻········································································299

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