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说明 2022-09-03 10:09:14 | |||
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资料 2022-09-03 10:09:14 | |||
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1-1 个性化推荐算法综述.mp4 | 2022-09-03 10:09:14 | 73.5 MB | |
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1-2 个性化召回算法综述.mp4 | 2022-09-03 10:09:14 | 46.49 MB | |
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2-1 LFM算法综述.mp4 | 2022-09-03 10:09:14 | 54.35 MB | |
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2-2 LFM算法的理论基础与公式推导.mp4 | 2022-09-03 10:09:14 | 78.02 MB | |
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2-3 基础工具函数的代码书写.mp4 | 2022-09-03 10:09:14 | 82.55 MB | |
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2-4 LFM算法训练数据抽取.mp4 | 2022-09-03 10:09:14 | 85.8 MB | |
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2-5 LFM模型训练.mp4 | 2022-09-03 10:09:14 | 106.2 MB | |
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2-6 基于LFM的用户个性化推荐与推荐结果分析.mp4 | 2022-09-03 10:09:14 | 78.06 MB | |
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3-1 personal rank算法的背景与物理意义.mp4 | 2022-09-03 10:09:14 | 71.32 MB | |
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3-2 personal rank 算法的数学公式推导.mp4 | 2022-09-03 10:09:14 | 49.45 MB | |
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3-3 代码构建用户物品二分图.mp4 | 2022-09-03 10:09:14 | 62.3 MB | |
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3-4 代码实战personal rank算法的基础版本.mp4 | 2022-09-03 10:09:14 | 127.74 MB | |
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3-5 代码实战personal rank算法矩阵版本上.mp4 | 2022-09-03 10:09:14 | 102.46 MB | |
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3-6 代码实战personal rank算法的矩阵版本下 -1.mp4 | 2022-09-03 10:09:14 | 14.06 MB | |
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3-7 代码实战personal rank算法的矩阵版本下-2.mp4 | 2022-09-03 10:09:14 | 64.18 MB | |
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4-1 item2vec算法的背景与物理意义.mp4 | 2022-09-03 10:09:14 | 81.24 MB | |
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4-2 item2vec依赖模型word2vec之cbow数学原理介绍.mp4 | 2022-09-03 10:09:14 | 88.1 MB | |
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4-3 item2vec依赖模型word2vec之skip gram数学原理介绍.mp4 | 2022-09-03 10:09:14 | 51.23 MB | |
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4-4 代码生成item2vec模型所需训练数据.mp4 | 2022-09-03 10:09:14 | 60.19 MB | |
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4-5 word2vec运行参数介绍与item embedding.mp4 | 2022-09-03 10:09:14 | 89.43 MB | |
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4-6 基于item bedding产出物品相似度矩阵与item2vec推荐流程梳理.mp4 | 2022-09-03 10:09:14 | 94.68 MB | |
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5-1 content based算法理论知识介绍.mp4 | 2022-09-03 10:09:14 | 59.45 MB | |
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5-2 content based算法代码实战之工具函数的书写.mp4 | 2022-09-03 10:09:14 | 106.07 MB | |
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5-3 用户刻画与基于内容推荐的代码实战。.mp4 | 2022-09-03 10:09:14 | 106.9 MB | |
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6-1 个性化召回算法总结与评估方法的介绍。.mp4 | 2022-09-03 10:09:14 | 67.81 MB | |
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7-1 学习排序综述.mp4 | 2022-09-03 10:09:14 | 75.51 MB | |
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8-1 逻辑回归模型的背景知识介绍.mp4 | 2022-09-03 10:09:14 | 78.52 MB | |
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8-10 LR模型训练之组合特征介绍.mp4 | 2022-09-03 10:09:14 | 92.99 MB | |
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8-2 逻辑回归模型的数学原理.mp4 | 2022-09-03 10:09:14 | 72.45 MB | |
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8-3 样本选择与特征选择相关知识.mp4 | 2022-09-03 10:09:14 | 58.31 MB | |
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8-4 代码实战LR之样本选择.mp4 | 2022-09-03 10:09:14 | 65.76 MB | |
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8-5 代码实战LR之离散特征处理.mp4 | 2022-09-03 10:09:14 | 111.17 MB | |
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8-6 代码实战LR之连续特征处理.mp4 | 2022-09-03 10:09:14 | 84.88 MB | |
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8-7 LR模型的训练.mp4 | 2022-09-03 10:09:14 | 86.87 MB | |
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8-8 LR模型在测试数据集上表现-上.mp4 | 2022-09-03 10:09:14 | 109.52 MB | |
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8-9 LR模型在测试数据集上表现-下.mp4 | 2022-09-03 10:09:14 | 115.29 MB | |
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9-1 背景知识介绍之决策树.mp4 | 2022-09-03 10:09:14 | 83.49 MB | |
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9-2 梯度提升树的数学原理与构建流程.mp4 | 2022-09-03 10:09:14 | 83.79 MB | |
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9-3 xgboost数学原理介绍.mp4 | 2022-09-03 10:09:14 | 62.54 MB | |
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9-4 gbdt与LR混合模型网络介绍.mp4 | 2022-09-03 10:09:14 | 41.3 MB | |
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9-5 代码训练gbdt模型.mp4 | 2022-09-03 10:09:14 | 88.05 MB | |
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9-6 gbdt模型最优参数选择.mp4 | 2022-09-03 10:09:14 | 57.57 MB | |
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9-7 代码训练gbdt与LR混合模型.mp4 | 2022-09-03 10:09:14 | 106.88 MB | |
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9-8 模型在测试数据集表现 上.mp4 | 2022-09-03 10:09:14 | 130.42 MB | |
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9-9 模型在测试数据集表现 下.mp4 | 2022-09-03 10:09:14 | 45.22 MB | |