课程介绍：
本套课程来自万门大学精品课程：人工智能、大数据与复杂系统一月特训班，本套课程是入门人工智能AI、大数据等领域的理想课程，由强大的导师阵容，课程官方售价3998元，课程共计78章829节，完整无加密版共32.9G。
课程截图：
课程目录：
01-复杂系统 [153.4M]
1.1物理预测的胜利与失效.mp4 [56.8M]
1.2预测失效原因.mp4 [19.9M]
1.3复杂系统引论.mp4 [40.7M]
1.4生活实例与本章答疑.mp4 [36M]
02-大数据与机器学习 [48M]
2.1大数据预测因为.mp4 [36.9M]
2.2大数据与机器学习.mp4 [11.1M]
03-人工智能的三个阶段 [673.7M]
3.10课程大纲（二）.mp4 [31.6M]
3.1规则阶段.mp4 [100.8M]
3.2机器学习阶段发展至连接主义阶段.mp4 [18.6M]
3.3课间答疑.mp4 [175.6M]
3.4连接主义阶段发展至学习阶段.mp4 [49.4M]
3.5三个阶段总结分析.mp4 [22.1M]
3.6人工智能的应用（一）.mp4 [43.3M]
3.7人工智能的应用（二）.mp4 [24.6M]
3.8课间答疑.mp4 [169.5M]
3.9课程大纲（一）.mp4 [38.2M]
04-高等数学—元素和极限 [509.7M]
4.10级数的收敛.mp4 [47.8M]
4.11极限的定义.mp4 [39.1M]
4.12极限的四则运算.mp4 [33.3M]
4.13极限的复合.mp4 [25.1M]
4.14连续性.mp4 [40.5M]
4.1实数的定义（一）.mp4 [32.8M]
4.2实数的定义（二）.mp4 [41.8M]
4.3实数的定义（三）.mp4 [36.7M]
4.4实数的元素个数（一）.mp4 [22.7M]
4.5实数的元素个数（二）.mp4 [37.5M]
4.6自然数个数少于实数个数（一）.mp4 [38.6M]
4.7自然数个数少于实数个数（二）.mp4 [44.6M]
4.8无穷大之比较（一）.mp4 [44.1M]
4.9无穷大之比较（二）.mp4 [25M]
05-复杂网络经济学应用 [218.9M]
5.1用网络的思维看经济结构.mp4 [46.1M]
5.2复杂网络认识前后.mp4 [56.3M]
5.3从网络结构看不同地区（一）.mp4 [75.1M]
5.4从网络结构看不同地区（二）.mp4 [41.4M]
06-机器学习与监督算法 [326.6M]
6.1什么是机器学习.mp4 [24.5M]
6.2机器学习的类型.mp4 [44.1M]
6.3简单回归实例（一）.mp4 [43M]
6.4简单回归实例（二）.mp4 [34.3M]
6.5简单回归实例（三）.mp4 [180.7M]
07-阿尔法狗与强化学习算法 [203.2M]
7.1人工智能的发展.mp4 [41.8M]
7.2强化学习算法（一）.mp4 [31.4M]
7.3强化学习算法（二）.mp4 [50.4M]
7.4强化学习算法（三）.mp4 [33M]
7.5Alphago给我们的启示.mp4 [21M]
7.6无监督学习.mp4 [25.6M]
08-高等数学—两个重要的极限定理 [158.1M]
8.1元素与极限的知识点回顾.mp4 [40.4M]
8.2第一个重要极限定理的证明（一）.mp4 [38.7M]
8.3第一个重要极限定理的证明（二）.mp4 [26.4M]
8.4夹逼定理.mp4 [24.8M]
8.5第二个重要极限定理的证明.mp4 [27.8M]
09-高等数学—导数 [338.3M]
9.10泰勒展开的证明.mp4 [37.6M]
9.1导数的定义.mp4 [38.6M]
9.2初等函数的导数.mp4 [45.3M]
9.3反函数的导数（一）.mp4 [20.9M]
9.4反函数的导数（二）.mp4 [26.9M]
9.5复合函数的导数.mp4 [28.7M]
9.6泰勒展开.mp4 [17M]
9.7罗尔定理.mp4 [25.7M]
9.8微分中值定理和柯西中值定理.mp4 [52.4M]
9.9洛比塔法则.mp4 [45.1M]
10-贝叶斯理论 [615M]
10.10贝叶斯于机器学习（一）.mp4 [47.6M]
10.11贝叶斯于机器学习（二）.mp4 [20.5M]
10.12贝叶斯决策（一）.mp4 [34.8M]
10.13贝叶斯决策（二）.mp4 [45M]
10.14贝叶斯决策（三）.mp4 [65.3M]
10.1梯度优化（一）.mp4 [61M]
10.2梯度优化（二）.mp4 [67.8M]
10.3概率基础.mp4 [35.6M]
10.4概率与事件.mp4 [37.4M]
10.5贝叶斯推理（一）.mp4 [35.9M]
10.6贝叶斯推理（二）.mp4 [37.1M]
10.7贝叶斯推理（三）.mp4 [31M]
10.8辛普森案件.mp4 [52.4M]
10.9贝叶斯推理深入.mp4 [43.6M]
11-高等数学—泰勒展开 [201.2M]
11.1泰勒展开.mp4 [41.1M]
11.2展开半径.mp4 [27.6M]
11.3欧拉公式.mp4 [48.3M]
11.4泰勒展开求极限（一）.mp4 [27.1M]
11.5泰勒展开求极限（二）.mp4 [57.2M]
12-高等数学—偏导数 [137.7M]
12.1偏导数的对称性.mp4 [34.8M]
12.2链式法则.mp4 [34.3M]
12.3梯度算符、拉氏算符.mp4 [68.6M]
13-高等数学—积分 [162M]
13.1黎曼积.mp4 [22.4M]
13.2微积分基本定理.mp4 [54.3M]
13.3分部积分（一）.mp4 [46.5M]
13.4分部积分（二）.mp4 [38.8M]
14-高等数学—正态分布 [188.9M]
14.1标准正态分布.mp4 [49.1M]
14.2中心极限定理.mp4 [34.1M]
14.3误差函数.mp4 [28.9M]
14.4二维正态分布.mp4 [44.1M]
14.5多维正态分布.mp4 [32.8M]
15-朴素贝叶斯和最大似然估计 [476.6M]
15.10朴素贝叶斯（三）.mp4 [63.8M]
15.11最大似然估计（一）.mp4 [24.7M]
15.12最大似然估计（二）.mp4 [51.4M]
15.1蒙特卡洛分析（一）.mp4 [49.2M]
15.2蒙特卡洛分析（二）.mp4 [34.8M]
15.3贝叶斯先验.mp4 [47.4M]
15.4先验到后验的过程.mp4 [21.7M]
15.5朴素贝叶斯（一）.mp4 [33.5M]
15.6朴素贝叶斯（二）.mp4 [40.5M]
15.7算法设计.mp4 [22.3M]
15.8TF-IDF（一）.mp4 [47.2M]
15.9TF-IDF（二）.mp4 [40M]
16-线 [456.8M]
16.10常规线空间.mp4 [52.4M]
16.11线关.mp4 [36.3M]
16.12秩.mp4 [55M]
16.1线代数概述.mp4 [36.2M]
16.2线代数应用方法论.mp4 [17.4M]
16.3线律.mp4 [44.4M]
16.4线空间.mp4 [17M]
16.5线空间八条法则（一）.mp4 [51.5M]
16.6线空间八条法则（二）.mp4 [46.9M]
16.7线空间八条法则（三）.mp4 [31.5M]
16.8连续傅.mp4 [26.8M]
16.9傅立.mp4 [41.3M]
17-数据科学和统计学（上） [515.8M]
17.10随机变量（二）.mp4 [15.3M]
17.11换门的概率模拟计算（一）.mp4 [59M]
17.12换门的概率模拟计算（二）.mp4 [37.8M]
17.13换门的概率模拟计算（三）.mp4 [50.5M]
17.1课程Overview.mp4 [36.4M]
17.2回顾统计学（一）.mp4 [65.3M]
17.3回顾统计学（二）.mp4 [52.9M]
17.4回顾统计学（三）.mp4 [28.1M]
17.5回顾数据科学（一）.mp4 [33.8M]
17.6回顾数据科学（二）和教材介绍.mp4 [60.8M]
17.7R和RStudio等介绍（一）.mp4 [25M]
17.8R和RStudio等介绍（二）.mp4 [29.7M]
17.9随机变量（一）.mp4 [21.3M]
18-线代数—矩阵、等价类和行列式 [570.3M]
18.10等价类.mp4 [57.4M]
18.11行列式（一）.mp4 [28.3M]
18.12行列式（二）.mp4 [38.2M]
18.13行列式（三）.mp4 [52.4M]
18.1线代数知识点回顾.mp4 [32.1M]
18.2矩阵表示线变化.mp4 [31.3M]
18.3可矩阵表示坐标变化.mp4 [64.9M]
18.4相似矩阵.mp4 [68.2M]
18.5相似矩阵表示相同线变化.mp4 [22.8M]
18.6线代数解微分方程.mp4 [67.4M]
18.7矩阵的运算—转秩（一）.mp4 [41.9M]
18.8矩阵的运算—转秩（二）.mp4 [34.8M]
18.9等价关系.mp4 [30.7M]
19-Python基础课程（上） [440.8M]
19.10变量类型—字符串类型（三）.mp4 [42.4M]
19.11变量类型—列表类型（一）.mp4 [25.4M]
19.12变量类型—列表类型（二）.mp4 [39.6M
]
19.13变量类型—列表类型（三）.mp4 [21.4M]
19.14变量类型—语言组类型、字典类型（一）.mp4 [29.9M]
19.15变量类型—字典类型（二）.mp4 [32M]
19.1Python介绍（一）.mp4 [31.6M]
19.2Python介绍（二）.mp4 [39.9M]
19.3变量—命名规范.mp4 [30.5M]
19.4变量—代码规范.mp4 [21.2M]
19.5变量类型—数值类型.mp4 [23.5M]
19.6变量类型—bool类型.mp4 [21.1M]
19.7变量类型—字符串类型（一）.mp4 [27.3M]
19.8课间答疑.mp4 [21.4M]
19.9变量类型—字符串类型（二）.mp4 [33.5M]
20-线代数—特征值与特征向量 [473.9M]
20.10线代数核心定理.mp4 [28.7M]
20.11对偶空间（一）.mp4 [28.6M]
20.12对偶空间（二）.mp4 [46.7M]
20.13欧氏空间与闵氏空间.mp4 [23.5M]
20.14厄米矩阵.mp4 [11.4M]
20.1线代数知识点回顾.mp4 [29.1M]
20.2例题讲解（一）.mp4 [34.5M]
20.3例题讲解（二）.mp4 [34.4M]
20.4例题讲解（三）.mp4 [40.6M]
20.5特征值与特征向量的物理意义.mp4 [68.8M]
20.6特征值与特征向量的性质（一）.mp4 [16.8M]
20.7特征值与特征向量的性质（二）.mp4 [47.6M]
20.8本征值的计算（一）.mp4 [31.4M]
20.9本征值的计算（二）.mp4 [31.8M]
21-监督学习框架 [468.3M]
21.10KNN(K最近邻)算法（二）.mp4 [39.8M]
21.11KNN(K最近邻)算法（三）.mp4 [19.8M]
21.12线性分类器.mp4 [28.4M]
21.13高斯判别模型（一）.mp4 [23.8M]
21.14高斯判别模型（二）.mp4 [34.2M]
21.1经验误差和泛化误差.mp4 [43.3M]
21.2最大后验估计.mp4 [42.6M]
21.3正则化.mp4 [18.8M]
21.4lasso回归.mp4 [45.8M]
21.5超参数（一）.mp4 [34.3M]
21.6超参数（二）.mp4 [26.8M]
21.7监督学习框架（一）.mp4 [32.1M]
21.8监督学习框架（二）.mp4 [42.4M]
21.9KNN(K最近邻)算法（一）.mp4 [36.2M]
22-Python基础课程（下） [378.3M]
22.10函数（三）.mp4 [28.5M]
22.11函数（四）.mp4 [33.9M]
22.12类（一）.mp4 [29.4M]
22.13类（二）.mp4 [26.8M]
22.14类（三）.mp4 [24.8M]
22.1条件判断（一）.mp4 [36.4M]
22.2条件判断（二）.mp4 [32.6M]
22.3循环（一）.mp4 [16.6M]
22.4循环（二）.mp4 [25.6M]
22.5课间答疑.mp4 [25.6M]
22.6循环（三）.mp4 [24.8M]
22.7循环（四）.mp4 [30.1M]
22.8函数（一）.mp4 [18.5M]
22.9函数（二）.mp4 [24.5M]
23-PCA、降维方法引入 [290.6M]
23.1无监督学习框架.mp4 [25.4M]
23.2降维存在的原因.mp4 [21.3M]
23.3PCA数学分析方法（一）.mp4 [31.3M]
23.4PCA数学分析方法（二）.mp4 [41.7M]
23.5PCA数学分析方法（三）.mp4 [29.1M]
23.6PCA数学分析方法（四）.mp4 [34.1M]
23.7PCA之外的降维方法—LDA.mp4 [16.3M]
23.8PCA背后的假设（一）.mp4 [41.9M]
23.9PCA背后的假设（二）.mp4 [49.6M]
24-数据科学和统计学（下） [327.5M]
24.10参数估计（一）.mp4 [26.4M]
24.11参数估计（二）.mp4 [20.7M]
24.12假设检验（一）.mp4 [16.3M]
24.13假设检验（二）.mp4 [23.4M]
24.1课程Overview.mp4 [21.3M]
24.2理解统计思想（一）.mp4 [22.2M]
24.3理解统计思想（二）.mp4 [54M]
24.4理解统计思想（三）.mp4 [21.8M]
24.5概率空间.mp4 [14.8M]
24.6随机变量（一）.mp4 [32.3M]
24.7随机变量（二）.mp4 [16.8M]
24.8随机变量（三）.mp4 [45M]
24.9随机变量（四）.mp4 [12.4M]
25-Python操作数据库、 Python爬虫 [764.1M]
25.10Python操作数据库（二）.mp4 [39.1M]
25.11Python操作数据库（三）.mp4 [22.9M]
25.12Python操作数据库（四）.mp4 [47.9M]
25.13Python爬虫（一）.mp4 [65.3M]
25.14Python爬虫（二）.mp4 [84.9M]
25.15Python爬虫（三）.mp4 [59.2M]
25.16Python爬虫（四）.mp4 [57.8M]
25.17Python爬虫（五）.mp4 [69.9M]
25.1课程介绍.mp4 [23M]
25.2认识关系型数据库（一）.mp4 [46M]
25.3认识关系型数据库（二）.mp4 [45.1M]
25.4MySQL数据库与Excel的不同.mp4 [25.9M]
25.5命令行操作数据库（一）.mp4 [43.9M]
25.6命令行操作数据库（二）.mp4 [41M]
25.7命令行操作数据库（三）.mp4 [19.8M]
25.8命令行操作数据库（四）.mp4 [39.6M]
25.9Python操作数据库（一）.mp4 [32.9M]
26-线分类器 [396.5M]
26.10Perceptron（三）.mp4 [31.5M]
26.11Perceptron（四）.mp4 [31M]
26.12熵与信息（一）.mp4 [22.9M]
26.13熵与信息（二）.mp4 [25.3M]
26.1Lasso：alpha参数与准确率（一）.mp4 [24.7M]
26.2Lasso：alpha参数与准确率（二）.mp4 [16M]
26.3Lasso：alpha参数与准确率（三）.mp4 [62.1M]
26.4线分类器.mp4 [24M]
26.5LDA（一）.mp4 [24.8M]
26.6LDA（二）.mp4 [27.1M]
26.7LDA（三）.mp4 [32.6M]
26.8Perceptron（一）.mp4 [45.2M]
26.9Perceptron（二）.mp4 [29.1M]
27-Python进阶（上） [440.1M]
27.10Pandas基本操作（四）.mp4 [26.1M]
27.11Pandas绘图（一）.mp4 [34.4M]
27.12Pandas绘图（二）.mp4 [37.9M]
27.13Pandas绘图（三）.mp4 [23.5M]
27.14Pandas绘图（四）.mp4 [46.9M]
27.1NumPy基本操作（一）.mp4 [31M]
27.2NumPy基本操作（二）.mp4 [24.5M]
27.3NumPy基本操作（三）.mp4 [27.4M]
27.4NumPy基本操作（四）.mp4 [18.2M]
27.5NumPy基本操作（五）.mp4 [28.9M]
27.6NumPy基本操作（六）.mp4 [25.7M]
27.7Pandas基本操作（一）.mp4 [42.1M]
27.8Pandas基本操作（二）.mp4 [35M]
27.9Pandas基本操作（三）.mp4 [38.4M]
28-Scikit-Learn [296.2M]
28.1课程介绍.mp4 [29.7M]
28.2Scikit-Learn介绍.mp4 [12.6M]
28.3数据处理（一）.mp4 [38.7M]
28.4数据处理（二）.mp4 [54.6M]
28.5模型实例、模型选择（一）.mp4 [37.9M]
28.6模型实例、模型选择（二）.mp4 [24.2M]
28.7模型实例、模型选择（三）.mp4 [21.3M]
28.8模型实例、模型选择（四）.mp4 [45.1M]
28.9模型实例、模型选择（五）.mp4 [32.2M]
29-熵、逻辑斯蒂回归、SVM引入 [396.5M]
29.10逻辑斯蒂回归（三）.mp4 [40.6M]
29.11逻辑斯蒂回归（四）.mp4 [40.6M]
29.12逻辑斯蒂回归（五）.mp4 [24.4M]
29.13SVM引入.mp4 [15.5M]
29.1熵（一）.mp4 [35.9M]
29.2熵（二）.mp4 [38.1M]
29.3熵（三）.mp4 [30.2M]
29.4熵（四）.mp4 [32.7M]
29.5熵（五）.mp4 [20.2M]
29.6熵（六）.mp4 [31.3M]
29.7熵（七）.mp4 [10.8M]
29.8逻辑斯蒂回归（一）.mp4 [38.7M]
29.9逻辑斯蒂回归（二）.mp4 [37.5M]
30-Python进阶（下） [270.4M]
30.1泰坦尼克数据处理与分析（一）.mp4 [29.3M]
30.2泰坦尼克数据处理与分析（二）.mp4 [22.1M]
30.3泰坦尼克数据处理与分析（三）.mp4 [22.3M]
30.4泰坦尼克数据处理与分析（四）.mp4 [28.4M]
30.5泰坦尼克数据处理与分析（五）.mp4 [27.5M]
30.6泰坦尼克数据处理与分析（六）.mp4 [20.7M]
30.7泰坦尼克数据处理与分析（七）.mp4 [38.1M]
30.8泰坦尼克数据处理与分析（八）.mp4 [40.5M]
30.9泰坦尼克数据处理与分析（九）.mp4 [41.5M]
31-决策树 [118M]
31.1决策树（一）.mp4 [21.3M]
31.2决策树（二）.mp4 [32.6M]
31.3决策树（三）.mp4 [37M]
31.4决策树（四）.mp4 [27.1M]
32-数据呈现基础 [216.4M]
32.1课程安排.mp4 [48.8M]
32.2什么是数据可视化.mp4 [15.8M]
32.3设计原则.mp4 [24M]
32.4数据可视化流程.mp4 [24.4M]
32.5视觉编码.mp4 [34.1M]
32.6图形选择（一）.mp4 [26.5M]
32.7图形选择（
二）.mp4 [20.2M]
32.8图形选择（三）.mp4 [22.5M]
33-云计算初步 [572.2M]
33.1Hadoop介绍.mp4 [30.2M]
33.2Hdfs应用（一）.mp4 [70.6M]
33.3Hdfs应用（二）.mp4 [59.6M]
33.4MapReduce（一）.mp4 [41.3M]
33.5MapReduce（二）.mp4 [27.8M]
33.6Hive应用（一）.mp4 [69.5M]
33.7Hive应用（二）.mp4 [82.5M]
33.8Hive应用（三）.mp4 [103.4M]
33.9Hive应用（四）.mp4 [87.2M]
34-D-Park实战 [922.4M]
34.10Spark应用（四）.mp4 [79M]
34.11Spark应用（五）.mp4 [94.6M]
34.12Spark应用（六）.mp4 [118.6M]
34.13Spark应用（七）.mp4 [102.7M]
34.1Pig应用（一）.mp4 [60.7M]
34.2Pig应用（二）.mp4 [57.6M]
34.3Pig应用（三）.mp4 [62.5M]
34.4Pig应用（四）.mp4 [58.2M]
34.5Pig应用（五）.mp4 [55.6M]
34.6Pig应用（六）.mp4 [25M]
34.7Spark应用（一）.mp4 [70.7M]
34.8Spark应用（二）.mp4 [38.4M]
34.9Spark应用（三）.mp4 [98.8M]
35-第四范式分享 [206.6M]
35.1推荐技术的介绍.mp4 [24.5M]
35.2人是如何推荐商品的.mp4 [24.7M]
35.3推荐系统的形式化以及如何评价推荐结果.mp4 [17.3M]
35.4求解—从数据到模型.mp4 [23.8M]
35.5数据拆分与特征工程.mp4 [26.8M]
35.6推荐系统机器学习模型.mp4 [35.1M]
35.7评估模型.mp4 [24.8M]
35.8建模过程的演示与课间答疑.mp4 [29.7M]
36-决策树到随机森林 [403.2M]
36.10Bagging与决策树（一）.mp4 [25.4M]
36.11Bagging与决策树（二）.mp4 [29.9M]
36.12Boosting方法（一）.mp4 [31.6M]
36.13Boosting方法（二）.mp4 [17.3M]
36.14Boosting方法（三）.mp4 [35.5M]
36.15Boosting方法（四）.mp4 [30M]
36.1决策树.mp4 [16.7M]
36.2随机森林.mp4 [29.7M]
36.3在Scikit-Learn里面如何用随机森林做预测（一）.mp4 [35.6M]
36.4在Scikit-Learn里面如何用随机森林做预测（二）.mp4 [36M]
36.5模型参数的介绍.mp4 [26.4M]
36.6集成方法（一）.mp4 [28M]
36.7集成方法（二）.mp4 [26M]
36.8Blending.mp4 [17.4M]
36.9gt多样化.mp4 [17.6M]
37-数据呈现进阶 [605.8M]
37.10D3（三）.mp4 [24.1M]
37.11div.html.mp4 [20.9M]
37.12svg.html.mp4 [68.3M]
37.13D3支持的数据类型.mp4 [59.4M]
37.14Make a map（一）.mp4 [57.5M]
37.15Make a map（二）.mp4 [17.9M]
37.1静态信息图（一）.mp4 [24.8M]
37.2静态信息图（二）.mp4 [34.1M]
37.3静态信息图（三）.mp4 [61.2M]
37.4静态信息图（四）.mp4 [38.4M]
37.5静态信息图（五）.mp4 [41.6M]
37.6HTML、CSS和JavaScript基础介绍.mp4 [48.3M]
37.7DOM和开发者工具.mp4 [28.7M]
37.8D3（一）.mp4 [40.3M]
37.9D3（二）.mp4 [40.4M]
38-强化学习（上） [356.7M]
38.10Policy Learning（二）.mp4 [23.5M]
38.11Policy Learning（三）.mp4 [33M]
38.12Policy Learning（四）.mp4 [27.7M]
38.13Policy Learning（五）.mp4 [17.6M]
38.14Policy Learning（六）.mp4 [37.1M]
38.1你所了解的强化学习是什么.mp4 [27.7M]
38.2经典条件反射（一）.mp4 [17.5M]
38.3经典条件反射（二）.mp4 [29.5M]
38.4操作性条件反射.mp4 [27.8M]
38.5Evaluation Problem（一）.mp4 [26.5M]
38.6Evaluation Problem（二）.mp4 [14.8M]
38.7Evaluation Problem（三）.mp4 [20M]
38.8Evaluation Problem（四）.mp4 [30.8M]
38.9Policy Learning（一）.mp4 [23.3M]
39-强化学习（下） [424M]
39.10大脑中的强化学习算法（三）.mp4 [13.1M]
39.11大脑中的强化学习算法（四）.mp4 [24.7M]
39.12大脑中的强化学习算法（五）.mp4 [25.5M]
39.13RL in alphaGo（一）.mp4 [27.5M]
39.14RL in alphaGo（二）.mp4 [27.9M]
39.15RL in alphaGo（三）.mp4 [18.1M]
39.16RL in alphaGo（四）.mp4 [42.3M]
39.1Policy Learning总结.mp4 [25.1M]
39.2基于模型的RL（一）.mp4 [37.3M]
39.3基于模型的RL（二）.mp4 [14.1M]
39.4基于模型的RL（三）.mp4 [36.6M]
39.5基于模型的RL（四）.mp4 [36.5M]
39.6基于模型的RL（五）.mp4 [20M]
39.7基于模型的RL（六）.mp4 [16.2M]
39.8大脑中的强化学习算法（一）.mp4 [38.3M]
39.9大脑中的强化学习算法（二）.mp4 [21.1M]
40-SVM和网络引入 [514.7M]
40.10SVM（九）.mp4 [37.6M]
40.11SVM（十）.mp4 [48.4M]
40.12SVM（十一）.mp4 [45.3M]
40.13SVM（十二）和网络引入.mp4 [52M]
40.1VC维.mp4 [34.1M]
40.2SVM（一）.mp4 [37.2M]
40.3SVM（二）.mp4 [45.9M]
40.4SVM（三）.mp4 [27.9M]
40.5SVM（四）.mp4 [40.2M]
40.6SVM（五）.mp4 [36M]
40.7SVM（六）.mp4 [29.5M]
40.8SVM（七）.mp4 [24.7M]
40.9SVM（八）.mp4 [55.8M]
41-集成模型总结和GDBT理解及其衍生应用 [741.6M]
41.10GDBT理解及其衍生应用（五）.mp4 [43.2M]
41.11GDBT理解及其衍生应用（六）.mp4 [49M]
41.12GDBT理解及其衍生应用（七）.mp4 [43.9M]
41.13GDBT理解及其衍生应用（八）.mp4 [87.3M]
41.14GDBT理解及其衍生应用（九）.mp4 [28.8M]
41.15GDBT理解及其衍生应用（十）.mp4 [60.8M]
41.1集成模型总结（一）.mp4 [38.1M]
41.2集成模型总结（二）.mp4 [41M]
41.3集成模型总结（三）.mp4 [46.2M]
41.4集成模型总结（四）.mp4 [39M]
41.5集成模型总结（五）.mp4 [77.4M]
41.6GDBT理解及其衍生应用（一）.mp4 [39.2M]
41.7GDBT理解及其衍生应用（二）.mp4 [53.7M]
41.8GDBT理解及其衍生应用（三）.mp4 [30.9M]
41.9GDBT理解及其衍生应用（四）.mp4 [63M]
42-网络 [252.4M]
42.1SVM比较其他分类起代码（一）.mp4 [38.2M]
42.2SVM比较其他分类起代码（二）.mp4 [55.6M]
42.3网络（一）.mp4 [32M]
42.4网络（二）.mp4 [43M]
42.5网络（三）.mp4 [35.9M]
42.6网络（四）.mp4 [47.7M]
43-监督学习－回归 [361.1M]
43.10经验分享（一）.mp4 [28.5M]
43.11经验分享（二）.mp4 [38.6M]
43.12经验分享（三）.mp4 [34.2M]
43.1机器学习的概念和监督学习.mp4 [21.6M]
43.2机器学习工作流程（一）.mp4 [11.7M]
43.3机器学习工作流程（二）.mp4 [21M]
43.4机器学习工作流程（三）.mp4 [20M]
43.5机器学习工作流程（四）.mp4 [26.7M]
43.6案例分析（一）.mp4 [17.1M]
43.7案例分析（二）.mp4 [40.8M]
43.8案例分析（三）.mp4 [39.3M]
43.9案例分析（四）.mp4 [61.6M]
44-监督学习－分类 [519.5M]
44.10模型训练与选择（二）.mp4 [51.8M]
44.11Airbnb数据探索过程（一）.mp4 [40.7M]
44.12Airbnb数据探索过程（二）.mp4 [59.2M]
44.13地震数据可视化过程（一）.mp4 [33.6M]
44.14地震数据可视化过程（二）.mp4 [32.6M]
44.1常用的分类算法.mp4 [18.6M]
44.2模型评估标准和案例分析.mp4 [28.8M]
44.3数据探索（一）.mp4 [27.1M]
44.4数据探索（二）.mp4 [41.2M]
44.5数据探索（三）.mp4 [33.3M]
44.6数据探索（四）.mp4 [27.6M]
44.7数据探索（五）.mp4 [52.3M]
44.8数据探索（六）.mp4 [37.9M]
44.9模型训练与选择（一）.mp4 [34.8M]
45-网络基础与卷积网络 [621.7M]
45.10网络（十）.mp4 [40.8M]
45.11图像处理基础.mp4 [29.8M]
45.12卷积（一）.mp4 [76.3M]
45.13卷积（二）.mp4 [43.9M]
45.1网络（一）.mp4 [38.9M]
45.2网络（二）.mp4 [26.8M]
45.3网络（三）.mp4 [21.7M]
45.4网络（四）.mp4 [100.5M]
45.6网络（六）.mp4 [36M]
45.7网络（七）.mp4 [26.2M]
45.8网络（八）.mp4 [33.4M]
45.9网络（九）.mp4 [39.6M]
45.网络（五）.mp4 [107.9M]
46-时间序列
预测 [470.9M]
46.10长短期记忆网络（LSTM）案例分析.mp4 [44.2M]
46.11Facebook开源的新预测工具—Prophet（一）.mp4 [44.2M]
46.12Facebook开源的新预测工具—Prophet（二）.mp4 [50M]
46.13课程答疑.mp4 [43.9M]
46.1时间序列预测概述（一）.mp4 [21.1M]
46.2时间序列预测概述（二）.mp4 [25.4M]
46.3差分自回归移动平均模型(ARIMA).mp4 [31.2M]
46.4差分自回归移动平均模型(ARIMA)案例分析（一）.mp4 [46.2M]
46.5差分自回归移动平均模型(ARIMA)案例分析（二）.mp4 [52.1M]
46.6差分自回归移动平均模型(ARIMA)案例分析（三）.mp4 [26.2M]
46.7差分自回归移动平均模型(ARIMA)案例分析（四）.mp4 [44.5M]
46.8长短期记忆网络（LSTM）（一）.mp4 [21.1M]
46.9长短期记忆网络（LSTM）（二）.mp4 [20.7M]
47-人工智能金融应用 [292.5M]
47.1人工智能金融应用（一）.mp4 [28.7M]
47.2人工智能金融应用（二）.mp4 [40.7M]
47.3人工智能金融应用（三）.mp4 [37.1M]
47.4人工智能金融应用（四）.mp4 [47.7M]
47.5机器学习方法（一）.mp4 [35.2M]
47.6机器学习方法（二）.mp4 [28.6M]
47.7机器学习方法（三）.mp4 [31.3M]
47.8机器学习方法（四）.mp4 [43.1M]
48-计算机视觉深度学习入门目的篇 [801.7M]
48.1计算机视觉深度学习入门概述.mp4 [103.6M]
48.2计算机视觉领域正在关心的问题（一）.mp4 [133.8M]
48.3计算机视觉领域正在关心的问题（二）.mp4 [73.8M]
48.4实际问题转化为具体问题并用深度学习解决（一）.mp4 [119.2M]
48.5实际问题转化为具体问题并用深度学习解决（二）.mp4 [153.3M]
48.6实际问题转化为具体问题并用深度学习解决（三）.mp4 [94.6M]
48.7实际问题转化为具体问题并用深度学习解决（四）.mp4 [123.4M]
49-计算机视觉深度学习入门结构篇 [1.4G]
49.10结构之间的优劣评判以及实验结果（五）.mp4 [99.6M]
49.11结构之间的优劣评判以及实验结果（六）.mp4 [77.5M]
49.12结构之间的以及实验结果（七）.mp4 [108.3M]
49.13结构之间的优劣评判以及实验结果（八）.mp4 [111.1M]
49.1复习计算机视觉最主要的负责特征提取的结构CNN.mp4 [131.9M]
49.2特征如何组织（一）.mp4 [126.2M]
49.3特征如何组织（二）.mp4 [86M]
49.4特征如何组织（三）.mp4 [92.9M]
49.5特征如何组织（四）.mp4 [113.4M]
49.6结构之间的优劣评判以及实验结果（一）.mp4 [116.3M]
49.7结构之间的优劣评判以及实验结果（二）.mp4 [87.7M]
49.8结构之间的优劣评判以及实验结果（三）.mp4 [148.2M]
49.9结构之间的优劣评判以及实验结果（四）.mp4 [91.3M]
50-计算机视觉学习入门优化篇 [675.5M]
50.1计算机视觉学习入门：优化篇概述.mp4 [70.4M]
50.2CNN模型的一阶优化逻辑.mp4 [148.7M]
50.3稳定性：Annealing和Momentum.mp4 [69.5M]
50.4拟合：从Dropout到Weight Decay.mp4 [128M]
50.5优化器和多机并行.mp4 [134.9M]
50.6手动超参优化逻辑以及超参优化往何处去.mp4 [124M]
51-计算机视觉深度学习入门数据篇 [429.8M]
51.1计算机视觉领域的常用竞赛数据集.mp4 [104.3M]
51.2对数据常用的预处理工作和后处理工作如何提高竞赛成绩（一）.mp4 [104.7M]
51.3对数据常用的预处理工作和后处理工作如何提高竞赛成绩（二）.mp4 [81.4M]
51.4如何使用端到端深度学习的方法.mp4 [139.3M]
52-计算机视觉深度学习入门工具篇 [257M]
52.1计算机视觉深度学习入门工具篇（一）.mp4 [93.5M]
52.2计算机视觉深度学习入门工具篇（二）.mp4 [110.2M]
52.3计算机视觉深度学习入门工具篇（三）.mp4 [53.2M]
53-个化推荐算法 [313.6M]
53.10工程望.mp4 [33.7M]
53.1个化推荐的发展.mp4 [26.1M]
53.2推荐算法的演进（一）.mp4 [25.5M]
53.3推荐算法的演进（二）.mp4 [35.8M]
53.4推荐算法的演进（三）.mp4 [27.9M]
53.5推荐算法的演进（四）.mp4 [40.2M]
53.6建模step by step（一）.mp4 [34M]
53.7建模step by step（二）.mp4 [38.7M]
53.8建模step by step（三）.mp4 [32.7M]
53.9算法评估和迭代.mp4 [18.9M]
54-Pig和Spark巩固 [799.7M]
54.10Spark巩固（五）.mp4 [101.7M]
54.1Pig巩固（一）.mp4 [43.8M]
54.2Pig巩固（二）.mp4 [115.3M]
54.3Pig巩固（三）.mp4 [89.1M]
54.4Pig巩固（四）.mp4 [83M]
54.5Pig巩固（五）.mp4 [70.1M]
54.6Spark巩固（一）.mp4 [65.6M]
54.7Spark巩固（二）.mp4 [105.4M]
54.8Spark巩固（三）.mp4 [70.6M]
54.9Spark巩固（四）.mp4 [54.9M]
55-人工智能与设计 [261.3M]
55.10使用人工智能的方式.mp4 [26.1M]
55.1智能存在的意义是什么.mp4 [19.5M]
55.2已有人工智的设计应用.mp4 [18.6M]
55.3人的智能（一）.mp4 [17M]
55.4人的智能（二）.mp4 [28.7M]
55.5人的智能的特点（一）.mp4 [29.6M]
55.6人的智能的特点（二）.mp4 [27.9M]
55.7人的智能的特点（三）.mp4 [41.6M]
55.8人工智能（一）.mp4 [27.6M]
55.9人工智能（二）.mp4 [24.6M]
56-网络 [190.9M]
56.1卷积的本质.mp4 [27.8M]
56.2卷积的三大特点.mp4 [34.1M]
56.3Pooling.mp4 [17M]
56.4数字识别（一）.mp4 [33.2M]
56.5数字识别（二）.mp4 [32M]
56.6感受野.mp4 [23.3M]
56.7RNN.mp4 [23.5M]
57-线动力学 [180.4M]
57.1非线动力学.mp4 [23.1M]
57.2线动力系统.mp4 [40M]
57.3线动力学与非线动力学系统（一）.mp4 [40.4M]
57.4线动力学与非线动力学系统（二）.mp4 [39.8M]
57.6Poincare引理.mp4 [37.1M]
58-订单流模型 [183.5M]
58.1交易.mp4 [20.2M]
58.2点过程基础（一）.mp4 [13.5M]
58.3点过程基础（二）.mp4 [24.2M]
58.4点过程基础（三）.mp4 [17.9M]
58.5订单流数据分析（一）.mp4 [22.3M]
58.6订单流数据分析（二）.mp4 [20.9M]
58.7订单流数据分析（三）.mp4 [17.7M]
58.8订单流数据分析（四）.mp4 [20.6M]
58.9订单流数据分析（五）.mp4 [26.2M]
59-区块链一场革命 [86M]
59.1比特币（一）.mp4 [23M]
59.2比特币（二）.mp4 [15.7M]
59.3比特币（三）.mp4 [32.1M]
59.4以太坊简介及ICO.mp4 [15.2M]
60-统计物理专题（一） [413.1M]
60.10证明理想气体方程.mp4 [23.3M]
60.11化学势.mp4 [41.5M]
60.12四大热力学势（一）.mp4 [30.1M]
60.13 四大热力学势（二）.mp4 [38.4M]
60.1统计物理的开端(一).mp4 [33M]
60.2统计物理的开端（二）.mp4 [24.2M]
60.3抛硬币抛出正态分布（一）.mp4 [19.5M]
60.4抛硬币抛出正态分布（二）.mp4 [35.7M]
60.5再造整个世界（一）.mp4 [30.5M]
60.6再造整个世界（二）.mp4 [35.1M]
60.7温度的本质（一）.mp4 [41M]
60.8温度的本质（二）.mp4 [27.3M]
60.9.mp4 [33.6M]
61-统计物理专题（二） [150M]
61.1神奇公式.mp4.mp4 [34.7M]
61.2信息熵（一）.mp4 [17.9M]
61.3信息熵（二）.mp4 [28.6M]
61.4Boltzmann分布.mp4 [30.2M]
61.5配分函数Z.mp4 [38.8M]
62-复杂网络简介 [133.7M]
62.1Networks in real worlds.mp4 [14.3M]
62.2BasicConcepts（一）.mp4 [19.8M]
62.3BasicConcepts（二）.mp4 [13.3M]
62.4Models（一）.mp4 [12.1M]
62.5Models（二）.mp4 [14M]
62.6Algorithms（一）.mp4 [25.4M]
62.7Algorithms（二）.mp4 [34.7M]
63-ABM简介及金融市场建模 [582.6M]
63.10ABM与复杂系统建模-交通系统（一）.mp4 [27.2M]
63.11ABM与复杂系统建模-交
通系统（二）.mp4 [37.9M]
63.12ABM金融市场-SFI股票市场模型（一）.mp4 [32M]
63.13ABM金融市场-SFI股票市场模型（二）.mp4 [25.4M]
63.14ABM金融市场-genova市场模型.mp4 [31.9M]
63.15ABM金融市场-Agent及其行为.mp4 [31.8M]
63.16学习模型.mp4 [35.7M]
63.17ABM金融市场-价格形成机制.mp4 [15.8M]
63.18ABM的特点.mp4 [29.3M]
63.1课程介绍.mp4 [26.9M]
63.2系统与系统建模.mp4 [39.5M]
63.3ABM与复杂系统建模（一）.mp4 [36.3M]
63.4ABM与复杂系统建模（二）.mp4 [45.1M]
63.5ABM与复杂系统建模（三）.mp4 [36.8M]
63.6ABM为经济系统建模.mp4 [30.4M]
63.7经典经济学如何给市场建模.mp4 [35.3M]
63.8ABM与复杂系统建模-市场交易.mp4 [40.1M]
63.9ABM与复杂系统建模-技术扩散.mp4 [25.3M]
64-用伊辛模型理解复杂系统 [518M]
64.10（网络中的）投票模型.mp4 [24.2M]
64.11观念动力学.mp4 [29.8M]
64.12集体运动Vicsek模型.mp4 [38.3M]
64.13自旋玻璃.mp4 [18.2M]
64.14Hopfield神经网络.mp4 [23.3M]
64.15限制Boltzmann机.mp4 [30.2M]
64.16深度学习与重正化群（一）.mp4 [35.4M]
64.17深度学习与重正化群（二）.mp4 [21.9M]
64.18总结.mp4 [30.7M]
64.19答疑.mp4 [17.4M]
64.1伊辛模型的背景及格气模型.mp4 [24.4M]
64.2伊辛模型（一）.mp4 [19.2M]
64.3伊辛模型（二）.mp4 [19.6M]
64.4从能量到统计分布及Monte Carlo模拟.mp4 [23.6M]
64.5Ising Model(2D).mp4 [26M]
64.6相变和临界现象.mp4 [43.6M]
64.7Critical Exponents.mp4 [26.7M]
64.8正问题和反问题.mp4 [29.1M]
64.9（空间中的）投票模型.mp4 [36.4M]
65-金融市场的复杂性 [639.6M]
65.10Classical Benchmarks（五）.mp4 [29.7M]
65.11Endogenous Risk（一）.mp4 [42.8M]
65.12Endogenous Risk（二）.mp4 [36.8M]
65.13Endogenous Risk（三）.mp4 [40.2M]
65.14Endogenous Risk（四）.mp4 [18.3M]
65.15Endogenous Risk（五）.mp4 [35.5M]
65.16Endogenous Risk（六）.mp4 [37.1M]
65.17Heterogeneous Beliefs（一）.mp4 [42.4M]
65.18Heterogeneous Beliefs（二）.mp4 [44.8M]
65.19总结.mp4 [21.1M]
65.1导论（一）.mp4 [39M]
65.2导论（二）.mp4 [39.7M]
65.3导论（三）.mp4 [21.4M]
65.4导论（四）.mp4 [30.3M]
65.5导论（五）.mp4 [37.6M]
65.6Classical Benchmarks（一）.mp4 [31.9M]
65.7Classical Benchmarks（二）.mp4 [28.6M]
65.8Classical Benchmarks（三）.mp4 [40M]
65.9Classical Benchmarks（四）.mp4 [22.4M]
66-广泛出现的幂律分布 [241.8M]
66.1界（一）.mp4 [29M]
66.2界（二）.mp4 [24.3M]
66.3界（三）.mp4 [22.9M]
66.4界（四）.mp4 [31M]
66.5城市、商业（一）.mp4 [33.9M]
66.6城市、商业（二）.mp4 [33.3M]
66.7启示（一）.mp4 [31.5M]
66.8启示（二）.mp4 [17.6M]
66.9总结.mp4 [18.3M]
67-自然启发算法 [499.6M]
67.10粒子群算法（一）.mp4 [37.2M]
67.11粒子群算法（二）.mp4 [38.2M]
67.12粒子群算法（三）.mp4 [33.6M]
67.13遗传算法和PSO的比较.mp4 [25.7M]
67.14更多的类似的算法（一）.mp4 [34.9M]
67.15更多的类似的算法（二）.mp4 [27.4M]
67.16答疑.mp4 [35.4M]
67.1课程回顾及答疑.mp4 [29.7M]
67.2概括（一）.mp4 [29.2M]
67.3概括（二）.mp4 [15.9M]
67.4模拟退火算法（一）.mp4 [40.2M]
67.5模拟退火算法（二）.mp4 [32.8M]
67.6进化相关的算法（一）.mp4 [26.4M]
67.7进化相关的算法（二）.mp4 [29.7M]
67.8进化相关的算法（三）.mp4 [36M]
67.9进化相关的算法（四）.mp4 [27.6M]
68-机器学习的方法 [575.4M]
68.10输出是最好的学习（二）.mp4 [16M]
68.11案例（一）.mp4 [27.6M]
68.12案例（二）.mp4 [18.3M]
68.13案例（三）.mp4 [20.5M]
68.14案例（四）.mp4 [38M]
68.15案例（五）.mp4 [16.4M]
68.1为什么要讲学习方法.mp4 [24.7M]
68.2阅读论文.mp4 [19.9M]
68.3综述式文章举例（一）.mp4 [88.3M]
68.4综述式文章举例（二）.mp4 [150.5M]
68.5碎片化时间学习及书籍.mp4 [51.6M]
68.6视频学习资源及做思维导图.mp4 [33.8M]
68.7铁哥答疑（一）.mp4 [28.5M]
68.8铁哥答疑（二）.mp4 [19.2M]
68.9输出是最好的学习（一）.mp4 [22.2M]
69-模型可视化工程管理 [889.3M]
69.10定制化可视化系统—Jupyter Dashboard（一）.mp4 [30.8M]
69.11定制化可视化系统—Jupyter Dashboard（二）.mp4 [38.7M]
69.12变身前端—seaboarn+Bokeh+Echarts.mp4 [71.3M]
69.13日志管理系统—ELK.mp4 [50.4M]
69.14极速Bi系统—superset.mp4 [40.2M]
69.15Dashboard补充.mp4 [55.4M]
69.16ELK补充.mp4 [63.3M]
69.17Superset补充.mp4 [60.9M]
69.18Superset补充及总结.mp4 [20.4M]
69.1课程简介.mp4 [20.7M]
69.2虚拟换环境—Anaconda&docker（一）.mp4 [28.7M]
69.3虚拟换环境—Anaconda&docker（二）.mp4 [30M]
69.4虚拟换环境—Anaconda&docker（三）.mp4 [59.3M]
69.5虚拟换环境—Anaconda&docker（四）.mp4 [34.5M]
69.6虚拟换环境—Anaconda&docker（五）.mp4 [53.3M]
69.7虚拟换环境—Anaconda&docker（六）.mp4 [38.2M]
69.8虚拟换环境—Anaconda&docker（七）.mp4 [54.6M]
69.9虚拟换环境—Anaconda&docker（八）.mp4 [138.5M]
70-Value Iteration Networks [109.9M]
70.1Background&Motivation.mp4 [23M]
70.2Value Iteration.mp4 [36.7M]
70.3Grid—world Domain.mp4 [23.8M]
70.4总结及答疑.mp4 [26.4M]
70-最新回放 [841.5M]
0822 CNN RNN回顾 非线性动力学引入.mp4 [469.9M]
0822 高频订单流模型、区块链介绍.mp4 [371.7M]
71-线动力学系统（上） [891M]
71.10混沌（一）.mp4 [28.6M]
71.11混沌（二）.mp4 [24.6M]
71.12混沌（三）.mp4 [21.4M]
71.13混沌（四）.mp4 [24M]
71.14混沌（五）.mp4 [32.4M]
71.15混沌（六）.mp4 [86.2M]
71.16混沌（七）.mp4 [157.7M]
71.17混沌（八）.mp4 [31.5M]
71.18混沌（九）.mp4 [31.5M]
71.19混沌（十）.mp4 [19.8M]
71.1线动力学系统（一）.mp4 [28M]
71.20混沌（十一）.mp4 [125.6M]
71.2线动力学系统（二）.mp4 [33.7M]
71.3二维系统动力学综述—Poincare引理.mp4 [33.7M]
71.4Bifurcation（一）.mp4 [13.7M]
71.5Bifurcation（二）.mp4 [34.2M]
71.6Bifurcation（三）.mp4 [32M]
71.7Bifurcation（四）.mp4 [28.7M]
71.8Bifurcation（五）.mp4 [38M]
71.9Bifurcation（六）.mp4 [65.6M]
72-线动力学系统（下） [130.3M]
72.1自然语言处理（一）.mp4 [30.9M]
72.2自然语言处理（二）.mp4 [34.1M]
72.3RNN.mp4 [34.4M]
72.4RNN及.mp4 [30.8M]
73-自然语言处理导入 [447.4M]
73.1中文分词.mp4 [27.7M]
73.2中文分词、依存文法分析.mp4 [26.7M]
73.3篇章分析、自动摘要、知识提取、文本相似度计算.mp4 [40.9M]
73.4知识库构建、问答系统.mp4 [42.7M]
73.5示范2的豆瓣评论词云（一）.mp4 [56.9M]
73.6示范2的豆瓣评论词云（二）.mp4 [54.8M]
73.7示范2的豆瓣评论词云（三）.mp4 [62.7M]
73.8示范2的豆瓣评论词云（四）.mp4 [72.2M]
73.9示范2的豆瓣评论词云（五）.mp4 [62.9M]
74-复杂网络上的物理传输过程 [523.6M]
74.10一些传播动力学模型（七）.mp4 [32.4M]
74.11一些传播动力学模型（八）.mp4 [23.5M]
74.12仿真模型的建立过程（一）.mp4 [45M]
74.13仿真模型的建立过程（二）.mp4 [44.3M]
74.14仿真模型的建立过程（三）.mp4 [63.7M]
74.15仿真模型的建立过程（四）.m
p4 [44.2M]
74.16Combining complex networks and data mining.mp4 [31.9M]
74.1一些基本概念.mp4 [18.5M]
74.2常用的统计描述物理量.mp4 [14.4M]
74.3四种网络模型.mp4 [30.3M]
74.4一些传播动力学模型（一）.mp4 [28.2M]
74.5一些传播动力学模型（二）.mp4 [28.9M]
74.6一些传播动力学模型（三）.mp4 [29.6M]
74.7一些传播动力学模型（四）.mp4 [31.4M]
74.8一些传播动力学模型（五）.mp4 [29.5M]
74.9一些传播动力学模型（六）.mp4 [27.8M]
75-RNN及LSTM [537.6M]
75.10梯度消失与梯度爆炸（二）.mp4 [23.9M]
75.11Reservoir computing—偷懒方法.mp4 [19.8M]
75.12LSTM.mp4 [32.5M]
75.13LSTM、Use Examples.mp4 [36.5M]
75.14词向量、Deep RNN.mp4 [22M]
75.15Encoder Decoder Structure.mp4 [20.2M]
75.16LSTM Text Generation（一）.mp4 [44.1M]
75.17LSTM Text Generation（二）.mp4 [53.5M]
75.18LSTM Text Generation（三）.mp4 [54.8M]
75.1RNN—序列处理器（一）.mp4 [23.3M]
75.2RNN—序列处理器（二）.mp4 [31.4M]
75.3A simple enough case.mp4 [29M]
75.4A dance between fix points.mp4 [30.7M]
75.5Fix point、Train Chaos.mp4 [26.8M]
75.6RNN作为生成模型（动力系统）.mp4 [24.6M]
75.7RNN训练—BPTT（一）.mp4 [22.9M]
75.8RNN训练—BPTT（二）.mp4 [18.8M]
75.9梯度消失与梯度爆炸（一）.mp4 [22.7M]
76-漫谈人工智能创业 [755.2M]
76.10三个战略管理学商业模型（三）.mp4 [39.7M]
76.11三个战略管理学商业模型（四）.mp4 [41.5M]
76.12三个战略管理学商业模型（五）.mp4 [31.4M]
76.13三个战略管理学商业模型（六）.mp4 [96.1M]
76.14三个战略管理学商业模型（七）.mp4 [33.2M]
76.15三个战略管理学商业模型（八）.mp4 [22.9M]
76.16三个战略管理学商业模型（九）.mp4 [27M]
76.17关于Entrepreneurship.mp4 [13.3M]
76.1人工智能对我们生活的影响（一）.mp4 [51.7M]
76.2人工智能对我们生活的影响（二）.mp4 [38.1M]
76.3人工智能对我们生活的影响（三）.mp4 [47.4M]
76.4人工智能对我们生活的影响（四）.mp4 [67.9M]
76.5人工智能对我们生活的影响（五）.mp4 [70.7M]
76.6人工智能对我们生活的影响（六）.mp4 [105.5M]
76.7人工智能创业中的商业思维.mp4 [29.7M]
76.8三个战略管理学商业模型（一）.mp4 [19.8M]
76.9三个战略管理学商业模型（二）.mp4 [19.4M]
77-学习其他主题 [369.1M]
77.1.mp4 [30.3M]
77.10程序讲解（三）.mp4 [47.9M]
77.2玻尔兹曼机—联想的机器.mp4 [27.7M]
77.3玻尔兹曼机.mp4 [36.2M]
77.4学习（一）.mp4 [26.4M]
77.5学习（二）.mp4 [24.2M]
77.6学习（三）.mp4 [24.4M]
77.7学习（四）.mp4 [54.5M]
77.8程序讲解（一）.mp4 [43M]
77.9程序讲解（二）.mp4 [54.5M]
78-课程总结 [408M]
78.10课程总结（二）.mp4 [132.7M]
78.1开场.mp4 [21.2M]
78.2Attention实例—Spatial Transformer.mp4 [60.9M]
78.3猫狗大战—CNN实战（一）.mp4 [42M]
78.4猫狗大战—CNN实战（二）.mp4 [35.4M]
78.5RNN诗人.mp4 [28.9M]
78.6课程复习.mp4 [33.2M]
78.7课程大纲（一）.mp4 [18.4M]
78.8课程大纲（二）.mp4 [18.8M]
78.9课程总结（一）.mp4 [16.5M]
