商品描述
隨著大模型及智能機器人技術的飛速發展，人工智能在人類生產生活中發揮著越來越重要的作用。人工智能是以模擬和擴展人類的智能和行動為目的，以現代科學和技術為手段的一門新興科技。本書從先進性和實用性出發，面向人工智能在感知、認知、決策等方向的應用需求，從基於 Python 的面向對象程序設計基本語法出發，對典型人工智能應用開發背後的理論和實現路徑進行具體介紹。全書共分為 10 章，主要內容包括 Python 簡介、Python 語法與面向對象程序設計、Python 人工智能程序設計、基於語音合成技術的行程播報系統、基於強化學習的自主遊戲、基於數據挖掘的醫藥問答系統、面向無人駕駛的目標檢測、基於 3D 高斯潑濺的三維重建系統、基於 AIGC 的圖像生成系統、基於大模型的具身智能系統。本書配套電子課件、程序代碼等教學資源。
目錄大綱
第 1 章 Python 簡介 ···················.1
1.1 Python 程序設計語言簡介 ······.1
1.1.1 Python 的起源與發展 ···········.1
1.1.2 Python 的特點 ····················.1
1.1.3 Python 的廣泛應用 ··············.2
1.2 Python 開發環境準備 ············.2
1.2.1 安裝 Python ·······················.2
1.2.2 Python 集成開發環境 ···········.4
1.3 第一個 Python 程序 ··············.5
思考題 ·····································.6
第 2 章 Python 語法與面向對象
程序設計 ························.7
2.1 Python 基礎語法 ··················.7
2.1.1 標識符和變量 ····················.7
2.1.2 行和縮進 ··························.8
2.1.3 多行語句與單行多條語句 ·····.8
2.1.4 註釋 ································.9
2.1.5 引號 ································.9
2.1.6 空行 ·······························.10
2.2 基本數據類型 ····················.10
2.2.1 基本數據類型簡介 ·············.10
2.2.2 運算符 ····························.10
2.2.3 鍵盤輸入 ·························.14
2.2.4 浮點數計算的限制 ·············.14
2.3 復雜數據類型 ····················.15
2.3.1 字符串 ····························.15
2.3.2 列表 ·······························.22
2.3.3 元組 ·······························.25
2.3.4 集合 ·······························.26
2.3.5 字典 ·······························.26
2.3.6 序列對象與其他對象的比較 ·.28
2.4 控制語句 ···························.28
2.4.1 if 語句 ····························.28
2.4.2 while 語句 ·······················.30
2.4.3 for 語句 ···························.31
2.4.4 break 語句和 continue 語句 ···.34
2.4.5 序列推導式 ······················.34
2.5 函數 ·································.36
2.5.1 函數的定義和調用 ·············.36
2.5.2 命名空間和作用域 ············.37
2.5.3 可變對象和不可變對象的
傳參 ······························.39
2.5.4 函數參數的形式 ···············.40
2.5.5 函數的不定長參數 ············.42
2.5.6 解包函數實參 ··················.43
2.5.7 匿名函數 ························.44
2.5.8 函數的遞歸 ·····················.45
2.5.9 裝飾器 ···························.47
2.5.10 函數註解 ·······················.48
2.6 模塊和包 ··························.48
2.6.1 模塊 ······························.48
2.6.2 模塊搜索路徑 ··················.51
2.6.3 包 ·································.51
2.7 錯誤和異常 ·······················.54
2.7.1 語法錯誤 ························.54
2.7.2 異常 ······························.54
2.7.3 斷言 ······························.55
2.7.4 拋出異常 ························.56
2.7.5 異常的處理 ·····················.56
2.7.6 異常鏈 ···························.57
2.7.7 清理操作 ························.58
2.8 類和對象 ··························.59
2.8.1 類的定義 ························.59
2.8.2 實例對象 ························.60
2.8.3 私有屬性與私有方法 ·········.61
2.8.4 繼承 ······························.61
2.8.5 疊代器和生成器 ···············.63
2.8.6 運算符重載 ·····················.66
2.9 文件讀寫 ··························.68
2.10 PEP 編碼規範 ···················.70
思考題 ····································.71
第 3 章 Python 人工智能程序
設計 ·····························.72
3.1 人工智能與機器學習簡介 ·····.72
3.2 常見的 Python 人工智能庫 ····.76
3.2.1 NumPy：Python 科學計算的
基石 ·······························.76
3.2.2 Pandas：數據處理與分析的
利器 ······························.76
3.2.3 Scikit-learn：機器學習的
綜合性工具包 ···················.77
3.2.4 PyTorch：深度學習的前沿
框架 ·······························.78
3.3 人工智能輔助編程工具 ········.79
思考題 ····································.84
第 4 章 基於語音合成技術的行程
播報系統 ·······················.85
4.1 項目背景 ···························.85
4.2 需求分析 ···························.85
4.3 總體設計 ···························.86
4.4 基於 OpenVoice 模型的語音
合成技術 ···························.86
4.4.1 基礎語音合成模塊 ·············.87
4.4.2 音色轉換器 ······················.87
4.5 項目實現 ···························.88
4.5.1 課程表模塊設計 ················.88
4.5.2 出行建議模塊設計 ·············.90
4.5.3 語音合成模塊設計 ·············.95
思考題 ··································.102
第 5 章 基於強化學習的自主遊戲 103
5.1 項目背景 ·························.103
5.2 需求分析 ·························.103
5.2.1 遊戲系統需求 ·················.103
5.2.2 強化學習系統需求 ···········.103
5.3 總體設計 ·························.104
5.3.1 遊戲設計 ·······················.104
5.3.2 算法設計 ·······················.104
5.4 強化學習的基本概念 ·········.104
5.4.1 馬爾可夫決策過程與策略 ··.105
5.4.2 價值函數與最優策略 ········.106
5.4.3 Q-learning 算法與 DQN
算法 ·····························.107
5.5 項目實現 ·························.111
5.5.1 貪吃蛇遊戲系統實現 ·········.111
5.5.2 遊戲核心邏輯 ·················.113
5.5.3 畫面更新機制 ·················.115
5.6 基於 DQN 算法的智能體 ····.116
5.6.1 狀態建模與網絡設計 ········.116
5.6.2 智能體行為實現 ··············.118
5.6.3 強化學習訓練過程 ···········.121
思考題 ··································.125
第 6 章 基於數據挖掘的醫藥問答
系統 ········.126
6.1 項目背景 ·························.126
6.2 需求分析 ·························.126
6.3 總體設計 ·························.127
6.4 知識圖譜和問答系統基本
概念 ·······························.128
6.4.1 知識圖譜 ·······················.128
6.4.2 問答系統 ·······················.129
6.5 項目實現 ·························.133
6.5.1 知識圖譜構建 ·················.133
6.5.2 問答系統構建 ·················.139
6.5.3 界面設計 ·······················.143
思考題 ···································.144
第 7 章 面向無人駕駛的目標檢測 .145
7.1 項目背景 ·························.145
7.2 需求分析 ·························.145
7.3 總體設計 ·························.146
7.4 基於 YOLOv8 的目標檢測····.146
7.4.1 目標檢測算法概述 ···········.147
7.4.2 YOLOv8 網絡結構 ···········.148
7.4.3 YOLOv8 損失函數 ···········.152
7.5 項目實現 ·························.153
7.5.1 數據預處理模塊 ··············.153
7.5.2 模型訓練模塊 ·················.160
7.5.3 模型測試與結果展示模塊 ··.165
思考題 ···································.166
第 8 章 基於 3D 高斯潑濺的三維
重建系統 ·····················.167
8.1 項目背景 ·························.167
8.2 需求分析 ·························.167
8.3 總體設計 ·························.167
8.3.1 網絡架構 ·······················.167
8.3.2 功能設計 ·······················.168
8.4 基於 3DGS 的三維重建 ·······.169
8.4.1 三維重建方法與 3DGS
表示 ·····························.169
8.4.2 3DGS 原理 ·····················.171
8.4.3 3DGS 算法流程 ···············.173
8.5 項目實現 ·························.174
8.5.1 3DGS 模型及初始化 ·········.174
8.5.2 高斯模型的優化與訓練 ·····.176
8.5.3 前後端實現與結果展示 ·····.181
思考題 ··································.184
第 9 章 基於 AIGC 的圖像生成
系統 ···························.185
9.1 項目背景 ·························.185
9.2 需求分析 ·························.185
9.3 總體設計 ·························.186
9.4 Stable Diffusion ·················.186
9.4.1 Stable Diffusion 原理 ········.187
9.4.2 Stable Diffusion 文生圖
技術 ·····························.188
9.5 項目實現 ·························.192
9.5.1 QT 框架實現 ··················.192
9.5.2 文字表征代碼實現 ···········.196
9.5.3 圖像生成代碼實現 ···········.197
思考題 ··································.201
第 10 章 基於大模型的具身智能
系統 ·······.202
10.1 項目背景························.202
10.2 需求分析························.202
10.3 總體設計························.202
10.4 具身智能和大模型 ···········.203
10.4.1 具身智能 ······················.203
10.4.2 大模型 ·························.204
10.5 項目實現························.206
10.5.1 機械臂模型構建與仿真
環境搭建 ······················.206
10.5.2 大模型指令生成 ·············.214
10.5.3 視覺識別與物體抓取 ·······.215
思考題 ···································.222
參考文獻 ··································.223
