【慕课专栏】全局视角系统学习《推荐系统》，实战中提升竞争力【完结】

{1}–第1章【前言】初探推荐系统 [91.41M]

[1.1]–1-1前言–关于这门课.mp4 [36.24M]

[1.2]–1-2推荐系统是什么.mp4 [36.35M]

[1.3]–1-3课程章节导览.mp4 [18.82M]

{2}–第2章【基础架构】推荐系统架构&项目搭建 [574.40M]

(2.1)–2-3推荐系统架构–如何设计一个推荐系统.pdf [126.50K]

(2.2)–2-5课程项目微服务api定义.pdf [96.71K]

(2.3)–2-12【梳理】推荐系统常用特征.pdf [82.11K]

(2.4)–2-13【梳理】重难点概览.pdf [68.92K]

[2.1]–2-1典型的推荐系统架构是什么样的（上）.mp4 [96.21M]

[2.2]–2-2典型的推荐系统架构是什么样的（下）.mp4 [51.85M]

[2.3]–2-4课程项目介绍和技术选型.mp4 [62.42M]

[2.4]–2-6后端服务框架搭建—召回服务（上）.mp4 [92.96M]

[2.5]–2-7后端服务框架搭建—召回服务（中）.mp4 [96.52M]

[2.6]–2-8后端服务框架搭建—召回服务（下）.mp4 [59.25M]

[2.7]–2-9后端服务框架搭建-排序与api服务.mp4 [91.82M]

[2.8]–2-11课程项目前端页面搭建.mp4 [22.99M]

{3}–第3章【特征工程】为推荐系统准备数据 [681.21M]

(3.1)–3-6数据爬虫的编订.pdf [102.18K]

(3.2)–3-17【梳理】特征处理方法.pdf [128.60K]

(3.3)–3-18【梳理】重难点概览.pdf [79.18K]

[3.1]–3-1特征工程—为推荐系统准备食材（上）.mp4 [44.86M]

[3.10]–3-12用spark处理特征（下）.mp4 [62.20M]

[3.11]–3-13如何采集用户行为数据.mp4 [47.95M]

[3.12]–3-14使用kafka和cassandra处理行为数据（上）.mp4 [67.10M]

[3.13]–3-15使用kafka和cassandra处理行为数据（下）.mp4 [50.26M]

[3.2]–3-2特征工程—为推荐系统准备食材（下）.mp4 [66.97M]

[3.3]–3-3如何做好特征工程（上）.mp4 [70.23M]

[3.4]–3-4如何做好特征工程（中）.mp4 [28.27M]

[3.5]–3-5如何做好特征工程（下）.mp4 [41.14M]

[3.6]–3-7用pandas可视化数据（上）.mp4 [42.40M]

[3.7]–3-8用pandas可视化数据（下）.mp4 [46.70M]

[3.8]–3-10spark—业界最流行的大数据框架.mp4 [40.45M]

[3.9]–3-11用spark处理特征（上）.mp4 [72.37M]

{4}–第4章【召回】筛选出用户的心头好 [549.40M]

(4.1)–4-15【梳理】重难点概览.pdf [220.43K]

[4.1]–4-1召回层—如何快速筛选出用户喜欢的物品（上）.mp4

[4.10]–4-11最近邻查找算法—如何使用embedding（下）.mp4 [32.71M]

[4.11]–4-12用faiss实现lsh.mp4 [52.83M]

[4.12]–4-14召回服务最终完善.mp4 [114.02M]

[4.2]–4-2召回层—如何快速筛选出用户喜欢的物品（下）.mp4 [24.71M]

[4.3]–4-3如何将word2vec用于推荐（上）.mp4 [22.25M]

[4.4]–4-4如何将word2vec用于推荐（下）.mp4 [27.03M]

[4.5]–4-5实现item2vec（上）.mp4 [45.37M]

[4.6]–4-6实现item2vec（中）.mp4 [54.52M]

[4.7]–4-7实现item2vec（下）.mp4 [95.70M]

[4.8]–4-9用redis存储embedding.mp4 [44.55M]

[4.9]–4-10最近邻查找算法—如何使用embedding（上）.mp4 [35.50M]

{5}–第5章【排序】对推荐结果进行精确排序 [1.13G]

(5.1)–5-22【梳理】重难点梳理.pdf [127.78K]

[5.1]–5-1排序层—如何活动最精确的结果排序.mp4 [22.23M]

[5.10]–5-11深度学习需要的特征如何处理（下）.mp4 [76.53M]

[5.11]–5-12如何保存线上服务特征.mp4 [65.36M]

[5.12]–5-13搭建并训练mlp模型（上）.mp4 [38.74M]

[5.13]–5-14搭建并训练mlp模型（中）.mp4 [96.46M]

[5.14]–5-15搭建并训练mlp模型（下）.mp4 [78.76M]

[5.15]–5-16模型调优怎么做（1）.mp4 [18.49M]

[5.16]–5-17模型调优怎么做（2）.mp4 [55.29M]

[5.17]–5-18模型调优怎么做（3）.mp4 [52.79M]

[5.18]–5-19模型调优怎么做（4）.mp4

[5.19]–5-21利用深度学习模型完善排序服务.mp4 [164.99M]

[5.2]–5-2协同过滤—最经典的排序算法.mp4 [43.04M]

[5.3]–5-3协同过滤算法实现.mp4 [113.96M]

[5.4]–5-5深度学习—革命性的机器学习模型.mp4 [84.40M]

[5.5]–5-6tensorflow—业界最著名的深度学习框架.mp4

[5.6]–5-7用三个例子体验tensorflow（上）.mp4 [82.47M]

[5.7]–5-8用三个例子体验tensorflow（下）.mp4 [44.07M]

[5.8]–5-9mlp—最经典的深度学习模型.mp4 [43.09M]

[5.9]–5-10深度学习需要的特征如何处理（上）.mp4 [78.23M]

{6}–第6章【效果评估】衡量推荐结果的好坏 [275.64M]

(6.1)–6-7【梳理】推荐模型离线评估.pdf [82.52K]

[6.1]–6-1如何衡量推荐系统的好坏（上）.mp4 [30.17M]

[6.2]–6-2如何衡量推荐系统的好坏（下）.mp4 [30.87M]

[6.3]–6-4在线评价系统的方法：ab测试.mp4 [33.12M]

[6.4]–6-5代码实现ab测试功能（上）.mp4 [92.62M]

[6.5]–6-6代码实现ab测试功能（下）.mp4 [88.78M]

{7}–第7章【深入学习】工程中的实践问题探讨 [389.07M]

(7.1)–7-9【拓展】flink中的时间.pdf [86.82K]

[7.1]–7-1实践问题—如何解决冷启动（上）.mp4 [24.60M]

[7.2]–7-2实践问题—如何解决冷启动（下）.mp4 [41.02M]

[7.3]–7-3实践问题—如何增强系统实时性（上）.mp4 [25.08M]

[7.4]–7-4实践问题—如何增强系统实时性（下）.mp4 [17.14M]

[7.5]–7-5用flink处理用户实时行为反馈（上）.mp4 [61.99M]

[7.6]–7-6用flink处理用户实时行为反馈（中）.mp4 [110.66M]

[7.7]–7-7用flink处理用户实时行为反馈（下）.mp4 [108.50M]

{8}–第8章【结语】前沿拓展 [140.12M]

[8.1]–8-1拓展篇之强化学习.mp4 [45.88M]

[8.2]–8-2前沿拓展之wide&cross模型（上）.mp4 [28.12M]

[8.3]–8-3前沿拓展之wide&cross模型（下）.mp4 [30.33M]

[8.4]–8-4回顾+结语.mp4 [35.78M]

课件.zip [89.90M]