知驿📋 资源介绍
本合集为人工智能与计算机视觉方向的系统学习资源，共2036个文件、约137GB，以视频课程为主，配套PDF讲义、数据集与代码压缩包。内容涵盖三大板块：①机器学习经典算法（回归、决策树与随机森林、贝叶斯、Xgboost、支持向量机、ARIMA时间序列、神经网络基础与架构、PCA降维与SVD、聚类、推荐系统、Word2Vec等81个视频）；②人工智能与计算机视觉课程（含CS271人工智能导论、计算机视觉专题、深度学习应用、神经网络与深度学习方向等）；③数据分析与大数据（Stanford Statistical Learning、数据科学、关系数据库与SQL、MapReduce、NoSQL、图分析等）。适合零基础转行AI、在校学生、算法工程师进阶及需要系统补足机器学习与CV基础的职场人。学完可掌握主流算法原理推导与实战调参，独立完成图像识别、推荐系统等典型项目。资源为网盘链接形式，购买后即可下载学习。
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🗂️ 完整文件清单（2036 个文件 / 137.0GB）
📁 第二阶段：机器学习经典算法[okl] 93 项
📁 01回归算法 7 项
- 📄 3.线性回归误差原理推导.mp431.1MB
- 📄 4.目标函数求解.mp429MB
- 📄 2.回归算法.mp447.1MB
- 📄 1.机器学习概述(1).mp423.9MB
- 📄 5.逻辑回归原理.mp414.7MB
- 📄 7.梯度下降原理.mp420.8MB
- 📄 6.梯度下降实例.mp440.2MB
📁 02决策树与随机森林 8 项
- 📄 7.随机森林.mp422MB
- 📄 4.信息增益.mp414.3MB
- 📄 1.决策树概述.mp423.4MB
- 📄 6.决策树剪枝.mp487MB
- 📄 2.熵原理形象解读.mp480.6MB
- 📄 5.信息增益率.mp442.8MB
- 📄 3.决策树构造实例.mp427.8MB
- 📄 8.案例决策树参数.mp458.7MB
📁 03贝叶斯算法 5 项
- 📄 3.贝叶斯拼写纠错实例[欢迎关注公众号：程序员共成长].mp429.3MB
- 📄 1.贝叶斯算法概述[欢迎关注公众号：程序员共成长].mp417.7MB
- 📄 2.贝叶斯推导实例[欢迎关注公众号：程序员共成长].mp443.7MB
- 📄 4.垃圾邮件过滤实例[欢迎关注公众号：程序员共成长].mp435.7MB
- 📄 5.贝叶斯实现拼写检查器[欢迎关注公众号：程序员共成长].mp448MB
📁 04 Xgboost 7 项
- 📄 2.xgboost基本原理.mp454.1MB
- 📄 7.Adaboost算法概述.mp433.7MB
- 📄 4.xgboost求解实例.mp430.2MB
- 📄 5.xgboost安装.mp411MB
- 📄 1.集成思想.mp413.3MB
- 📄 3.xgboost目标函数推导.mp430.3MB
- 📄 6.xgboost实战演示.mp4162MB
📁 05支持向量机算法 7 项
- 📄 5.支持向量的作用.mp421.9MB
- 📄 2.支持向量机求解目标.mp475.5MB
- 📄 1.支持向量机要解决的问题.mp412.5MB
- 📄 4.支持向量机求解例子.mp428.2MB
- 📄 7.核函数变换.mp439.4MB
- 📄 3.支持向量机目标函数求解.mp424.1MB
- 📄 6.软间隔支持向量机.mp416.9MB
📁 06时间序列AIRMA模型 5 项
- 📄 4.建立ARIMA模型.mp425.5MB
- 📄 3.相关函数评估方法.mp4102MB
- 📄 5.参数选择.mp443.2MB
- 📄 1.数据平稳性与差分法.mp430.8MB
- 📄 2.ARIMA模型.mp426MB
📁 07神经网络基础 11 项
- 📄 9.最优化形象解读.mp417.3MB
- 📄 11.反向传播.mp437.2MB
- 📄 4.超参数与交叉验证.mp426.4MB
- 📄 8.softmax分类器.mp477.1MB
- 📄 1.深度学习概述.mp432.7MB
- 📄 6.损失函数.mp423.3MB
- 📄 2.挑战与常规套路.mp464.4MB
- 📄 3.用K近邻来进行分类.mp425.4MB
- 📄 5.线性分类.mp450MB
- 📄 7.正则化惩罚项.mp417.6MB
- 📄 10.最优化问题细节.mp428.4MB
📁 08神经网络架构 4 项
- 📄 1.整体架构.mp424.6MB
- 📄 2.实例演示.mp449.1MB
- 📄 3.过拟合解决方案.mp439MB
- 📄 4.感受神经网络的强大.mp4161MB
📁 09PCA降维与SVD矩阵分解 4 项
- 📄 4.SVD推荐系统.mp429.6MB
- 📄 2.PCA降维实例.mp448MB
- 📄 3.SVD原理.mp442MB
- 📄 1.PCA问题.mp410.1MB
📁 10聚类算法 3 项
- 📄 1.聚类算法概述.mp442.9MB
- 📄 2.使用Kmeans进行图像压缩.mp467.4MB
- 📄 3.特征工程2.mp441.1MB
📁 11推荐系统 9 项
- 📄 8.隐语义模型求解.mp424.3MB
- 📄 3.推荐系统要完成的任务.mp416.4MB
- 📄 5.基于用户的协同过滤.mp451MB
- 📄 9.模型评估标准.mp417.9MB
- 📄 4.相似度计算.mp426.1MB
- 📄 2.推荐系统应用.mp474.6MB
- 📄 7.隐语义模型.mp418.5MB
- 📄 6.基于物品的协同过滤.mp435.3MB
- 📄 1.开场.mp45.4MB
📁 12Word2Vec 11 项
- 📄 3.语言模型.mp414.5MB
- 📄 8.CBOW模型实例.mp428.8MB
- 📄 11.负采样模型.mp417.1MB
- 📄 10.锑度上升求解.mp455.1MB
- 📄 9.CBOW求解目标.mp413.8MB
- 📄 7.Hierarchical Softmax.mp424.3MB
- 📄 5.词向量.mp441.6MB
- 📄 2.自然语言处理与深度学习.mp465.4MB
- 📄 6.神经网络模型.mp425MB
- 📄 1.开篇.mp412.7MB
- 📄 4.N-gram模型.mp421.4MB
📁 人工智能课程 851 项
📁 8天深入理解python教程-资料 2 项
- 📄 demo_python.tar.gz3.7MB
- 📄 深入理解python.pdf1.2MB
📁 计算机视觉 1 项
- 📄 计算机视觉.zip15.3MB
📁 模型 1 项
- 📄 模型.zip255MB
📁 人工智能 395 项
📁 大数据与信息传播 44 项
📁 第二讲 4 项
- 📄 2 - 4 - 话题一-4-传播效果（时长- 16-04）.mp440.7MB
- 📄 2 - 1 - 话题一-1-话题一综述（时长- 07-16）.mp415.2MB
- 📄 2 - 3 - 话题一-3-互联网世界的基本结构（时长- 15- 47）.mp434.1MB
- 📄 2 - 2 - 话题一-2-基本概念（时长- 28-53）.mp460.9MB
📁 第六讲 9 项
- 📄 6 - 9 - 话题五-9-案例分析（时长27-19）.mp461.1MB
- 📄 6 - 1 - 话题五-1-话题五综述（时长-06-16）.mp49.7MB
- 📄 6 - 5 - 话题五-5-口碑生成基本规律-1（时长-16-11）.mp433.5MB
- 📄 6 - 6 - 话题五-6-口碑生成基本规律-2（时长-16-15）.mp433MB
- 📄 6 - 2 - 话题五-2-基本概念-网络口碑&两级传播（时长-07-21）.mp412.6MB
- 📄 6 - 4 - 话题五-4-口碑生成基本要素（时长-13-26）.mp424.3MB
- 📄 6 - 7 - 话题五-7-口碑影响力测评（时长-06-54）.mp411.5MB
- 📄 6 - 3 - 话题五-3-基本概念-意见领袖，舆论&从众心理（时长-13-18）.mp423.9MB
- 📄 6 - 8 - 话题五-8-网络口碑的影响（时长-06-28）.mp410.3MB
📁 第三讲 9 项
- 📄 3 - 5 - 话题二-5-识别工具-InterestBase（时长-11-53）.mp425.6MB
- 📄 3 - 8 - 话题二-8-定向技术&技术两个层级的影响（时长-16-07）.mp435.2MB
- 📄 3 - 3 - 话题二-3-基本概念-用户生成内容数据&精准营销（时长-04-37）.mp410.5MB
- 📄 3 - 2 - 话题二-2-基本概念-人群识别&用户行为数据（时长- 08-16）.mp416.3MB
- 📄 3 - 1 - 话题二-1-话题二综述（时长- 04-54）.mp410.7MB
- 📄 3 - 4 - 话题二-4-基本概念-RTB&DSP&DMP（时长-11-48）.mp428MB
- 📄 3 - 7 - 话题二-7-识别工具-RAN&RFM 模型（时长-08-56）.mp419MB
- 📄 3 - 9 - 话题二-9-案例分析（时长-09-18）.mp419.2MB
- 📄 3 - 6 - 话题二-6-识别工具-Appinions（时长-12-49）.mp426.8MB
📁 第四讲 5 项
- 📄 4 - 2 - 话题三-2-基本概念-集群行为&人群聚合驱动力（时长-17-34）.mp439.4MB
- 📄 4 - 4 - 话题三-4-社会化媒体平台介绍（时长-17-13）.mp429.6MB
- 📄 4 - 6 - 话题三-6-案例分析（时长-12-28）.mp423.7MB
- 📄 4 - 3 - 话题三-3-基本概念-群体智慧&复杂社会网络（时长-12-31）.mp422.1MB
- 📄 4 - 1 - 话题三-1-话题三综述（时长-09-28）.mp416.2MB
📁 第五讲 7 项
- 📄 5 - 5 - 话题四-5-互动对话缘由-亲密程度&感情力量（时长-10-43）.mp416.4MB
- 📄 5 - 3 - 话题四-3-基本概念-测量关系的四个维度&社会互动（时长-12-28）.mp423.1MB
- 📄 5 - 2 - 话题四-2-基本概念-知识生产&弱连接（时长-08-44）.mp416.8MB
- 📄 5 - 6 - 话题四-6-互动对话缘由-互惠交换&时间契机（时长-10-23）.mp418.7MB
- 📄 5 - 1 - 话题四-1-话题四综述（时长-07-10）.mp411.6MB
- 📄 5 - 7 - 话题四-7-案例分析（时长-10-15）.mp432.7MB
- 📄 5 - 4 - 话题四-4-媒体平台特征（时长-13-48）.mp425.7MB
📁 第一讲 4 项
- 📄 1 - 1 - 序言-1－课程话题总述（时长：21-09）.mp451.8MB
- 📄 1 - 4 - 序言-4- 话题六 ~ 话题八概述（时长：27-17）.mp468.8MB
- 📄 1 - 3 - 序言-3- 话题三 ~ 话题五概述（时长：28-47）.mp469.8MB
- 📄 1 - 2 - 序言-2- 话题一 ~ 话题二概述（时长：31-52）.mp483MB
📁 CS271 Introduction to Artificial Intelligence AI Class 60 项
- 📄 Problem_Set_2-subtitles.en.zip14.5KB
- 📄 printable_final.pdf258KB
- 📄 5._Machine_Learning-subtitles.en.zip124KB
- 📄 Problem_Set_5-subtitles.en.zip10.1KB
- 📄 16._Computer_Vision_I.zip187MB
- 📄 10._Reinforcement_Learning.zip150MB
- 📄 15._Advanced_Planning-subtitles.en.zip29.9KB
- 📄 12._MDP_Review-subtitles.en.zip7KB
- 📄 4._Probabilistic_Inference-subtitles.en.zip50.7KB
- 📄 8._Planning.zip219MB
- 📄 10._Reinforcement_Learning-subtitles.en.zip72KB
- 📄 Problem_Set_1-subtitles.en.zip8KB
- 📄 3._Probability_in_AI.zip322MB
- 📄 18._Computer_Vision_III-subtitles.en.zip18.8KB
- 📄 Problem_Set_5.zip29.7MB
- 📄 Problem_Set_6.zip62.6MB
- 📄 7._Representation_with_Logic.zip144MB
- 📄 17._Computer_Vision_II.zip141MB
- 📄 18._Computer_Vision_III.zip49.7MB
- 📄 19._Robotics_I.zip119MB
- 📄 1._Welcome_to_AI-subtitles.en.zip35.8KB
- 📄 Problem_Set_8-subtitles.en.zip17.2KB
- 📄 Problem_Set_4.zip80MB
- 📄 13._Games.zip151MB
- 📄 Problem_Set_7-subtitles.en.zip22.4KB
- 📄 Problem_Set_1.zip44.2MB
- 📄 20._Robotics_II-subtitles.en.zip38.5KB
- 📄 13._Games-subtitles.en.zip59.1KB
- 📄 14._Game_Theory.zip163MB
- 📄 Problem_Set_6-subtitles.en_2.zip22.3KB
- 📄 22._Natural_Language_Processing_II.zip154MB
- 📄 Problem_Set_3-subtitles.en.zip18.3KB
- 📄 20._Robotics_II.zip116MB
- 📄 8._Planning-subtitles.en.zip87.2KB
- 📄 16._Computer_Vision_I-subtitles.en.zip69.2KB
- 📄 3._Probability_in_AI-subtitles.en.zip95.2KB
- 📄 Office_Hours.zip606MB
- 📄 14._Game_Theory-subtitles.en.zip64.3KB
- 📄 7._Representation_with_Logic-subtitles.en.zip44.7KB
- 📄 Problem_Set_7.zip52.2MB
- 📄 11._HMMs_and_Filters.zip195MB
- 📄 Problem_Set_3.zip51MB
- 📄 printable_midterm.pdf2.2MB
- 📄 9._Planning_under_Uncertainty.zip175MB
- 📄 19._Robotics_I-subtitles.en.zip35.8KB
- 📄 21._Natural_Language_Processing.zip203MB
- 📄 5._Machine_Learning.zip377MB
- 📄 11._HMMs_and_Filters-subtitles.en.zip76.5KB
- 📄 6._Unsupervised_Learning-subtitles.en.zip95KB
- 📄 9._Planning_under_Uncertainty-subtitles.en.zip79.1KB
- 📄 Problem_Set_2.zip48.4MB
- 📄 12._MDP_Review.zip18.1MB
- 📄 Problem_Set_4-subtitles.en.zip26.9KB
- 📄 4._Probabilistic_Inference.zip157MB
- 📄 2._Problem_Solving-subtitles.en.zip81.1KB
- 📄 21._Natural_Language_Processing-subtitles.en.zip81.3KB
- 📄 Problem_Set_8.zip43.1MB
- 📄 15._Advanced_Planning.zip75.2MB
- 📄 2._Problem_Solving.zip232MB
- 📄 1._Welcome_to_AI.zip117MB
📁 Data Manipulation at Scale Systems and Algorithms 286 项
📁 01_data-science-context-and-concepts 50 项
📁 01_lesson-1-examples-and-the-diversity-of-data-science 12 项
- 📄 03_appetite-whetting-digital-humanities.mp417.6MB
- 📄 01_appetite-whetting-politics.srt8.6KB
- 📄 06_appetite-whetting-public-health-cont-d-earthquakes-legal.mp48.1MB
- 📄 06_appetite-whetting-public-health-cont-d-earthquakes-legal.srt5.6KB
- 📄 01_appetite-whetting-politics.mp412.1MB
- 📄 05_appetite-whetting-food-music-public-health.srt6.7KB
- 📄 05_appetite-whetting-food-music-public-health.mp410.8MB
- 📄 02_appetite-whetting-extreme-weather.srt2.7KB
- 📄 04_appetite-whetting-bibliometrics.srt5.5KB
- 📄 04_appetite-whetting-bibliometrics.mp48.7MB
- 📄 03_appetite-whetting-digital-humanities.srt11.1KB
- 📄 02_appetite-whetting-extreme-weather.mp45MB
📁 02_lesson-2-working-definitions-of-data-science 8 项
- 📄 02_characterizing-data-science-cont-d.srt8.1KB
- 📄 04_four-dimensions-of-data-science.mp411.1MB
- 📄 03_distinguishing-data-science-from-related-topics.srt6.7KB
- 📄 04_four-dimensions-of-data-science.srt9.7KB
- 📄 01_characterizing-data-science.srt7.8KB
- 📄 02_characterizing-data-science-cont-d.mp411.7MB
- 📄 03_distinguishing-data-science-from-related-topics.mp48.3MB
- 📄 01_characterizing-data-science.mp410.5MB
📁 03_lesson-3-characterizing-this-course 10 项
- 📄 02_desktop-scale-vs-cloud-scale.mp411.4MB
- 📄 01_tools-vs-abstractions.srt11.3KB
- 📄 01_tools-vs-abstractions.mp414.9MB
- 📄 04_structs-vs-stats.mp410.1MB
- 📄 04_structs-vs-stats.srt8.8KB
- 📄 03_hackers-vs-analysts.mp46.4MB
- 📄 03_hackers-vs-analysts.srt4.2KB
- 📄 02_desktop-scale-vs-cloud-scale.srt8.7KB
- 📄 05_structs-vs-stats-cont-d.srt7.9KB
- 📄 05_structs-vs-stats-cont-d.mp410.5MB
📁 04_lesson-4-related-topics 10 项
- 📄 01_a-fourth-paradigm-of-science.mp48.4MB
- 📄 03_big-data-and-the-3-vs.mp412.6MB
- 📄 04_big-data-definitions.srt7.8KB
- 📄 02_data-intensive-science-examples.srt9.2KB
- 📄 05_big-data-sources.srt11.6KB
- 📄 02_data-intensive-science-examples.mp412.6MB
- 📄 01_a-fourth-paradigm-of-science.srt6.6KB
- 📄 04_big-data-definitions.mp411.4MB
- 📄 03_big-data-and-the-3-vs.srt8KB
- 📄 05_big-data-sources.mp415.3MB
📁 05_lesson-5-course-logistics 2 项
- 📄 01_course-logistics.srt11.6KB
- 📄 01_course-logistics.mp414.1MB
📁 06_assignment-1-twitter-sentiment-analysis 2 项
- 📄 01_twitter-assignment-getting-started.mp440.2MB
- 📄 01_twitter-assignment-getting-started.srt18.5KB
📁 02_relational-databases-and-the-relational-algebra 52 项
📁 01_lesson-6-principles-of-data-manipulation-and-management 10 项
- 📄 03_pre-relational-databases.mp410.1MB
- 📄 02_from-data-models-to-databases.srt7.6KB
- 📄 03_pre-relational-databases.srt7.8KB
- 📄 02_from-data-models-to-databases.mp49.9MB
- 📄 01_data-models-terminology.mp410.9MB
- 📄 05_relational-databases-key-ideas.mp49.4MB
- 📄 04_motivating-relational-databases.mp47.5MB
- 📄 05_relational-databases-key-ideas.srt6.6KB
- 📄 04_motivating-relational-databases.srt5.3KB
- 📄 01_data-models-terminology.srt9.6KB
📁 02_lesson-7-relational-algebra 14 项
- 📄 02_relational-algebra-overview.srt6.1KB
- 📄 06_relational-algebra-operators-outer-join.mp47.7MB
- 📄 03_relational-algebra-operators-union-difference-selection.srt8.4KB
- 📄 07_relational-algebra-operators-theta-join.srt6.5KB
- 📄 04_relational-algebra-operators-projection-cross-product.srt6.3KB
- 📄 05_relational-algebra-operators-cross-product-cont-d-join.srt8.3KB
- 📄 01_algebraic-optimization-overview.srt10.3KB
- 📄 07_relational-algebra-operators-theta-join.mp48.3MB
- 📄 04_relational-algebra-operators-projection-cross-product.mp49.5MB
- 📄 03_relational-algebra-operators-union-difference-selection.mp412MB
- 📄 02_relational-algebra-overview.mp49.3MB
- 📄 05_relational-algebra-operators-cross-product-cont-d-join.mp413.6MB
- 📄 01_algebraic-optimization-overview.mp413.2MB
- 📄 06_relational-algebra-operators-outer-join.srt5.6KB
📁 03_lesson-8-sql-for-data-science 12 项
- 📄 03_practical-sql-binning-timeseries.mp411.5MB
- 📄 02_thinking-in-ra-logical-query-plans.mp48.1MB
- 📄 06_support-for-user-defined-functions.srt6.1KB
- 📄 06_support-for-user-defined-functions.mp48MB
- 📄 01_from-sql-to-ra.srt8.9KB
- 📄 05_user-defined-functions.mp47.6MB
- 📄 04_practical-sql-genomic-intervals.srt9.7KB
- 📄 02_thinking-in-ra-logical-query-plans.srt6.3KB
- 📄 03_practical-sql-binning-timeseries.srt8.2KB
- 📄 05_user-defined-functions.srt5.4KB
- 📄 04_practical-sql-genomic-intervals.mp414.2MB
- 📄 01_from-sql-to-ra.mp411.9MB
📁 04_lesson-9-key-principles-of-relational-databases 12 项
- 📄 05_views-logical-data-independence.srt7.6KB
- 📄 04_declarative-languages-more-examples.mp49.5MB
- 📄 03_declarative-languages.srt7.6KB
- 📄 01_optimization-physical-query-plans.srt8.4KB
- 📄 04_declarative-languages-more-examples.srt6KB
- 📄 02_optimization-choosing-physical-plans.srt7.7KB
- 📄 02_optimization-choosing-physical-plans.mp49.6MB
- 📄 05_views-logical-data-independence.mp410.6MB
- 📄 06_indexes.mp413.4MB
- 📄 03_declarative-languages.mp411.3MB
- 📄 01_optimization-physical-query-plans.mp49.6MB
- 📄 06_indexes.srt10.8KB
📁 03_mapreduce-and-parallel-dataflow-programming 55 项
📁 01_lesson-10-reasoning-about-scale 10 项
- 📄 02_a-sketch-of-algorithmic-complexity.mp49.8MB
- 📄 01_what-does-scalable-mean.srt8.2KB
- 📄 02_a-sketch-of-algorithmic-complexity.srt7.2KB
- 📄 04_pleasingly-parallel-algorithms.srt7.3KB
- 📄 01_what-does-scalable-mean.mp413.3MB
- 📄 03_a-sketch-of-data-parallel-algorithms.mp410MB
- 📄 04_pleasingly-parallel-algorithms.mp48.4MB
- 📄 03_a-sketch-of-data-parallel-algorithms.srt8.2KB
- 📄 05_more-general-distributed-algorithms.srt6.5KB
- 📄 05_more-general-distributed-algorithms.mp48MB
📁 02_lesson-11-the-mapreduce-programming-model 14 项
- 📄 02_mapreduce-data-model.mp47.4MB
- 📄 03_map-and-reduce-functions.mp46.1MB
- 📄 06_mapreduce-example-word-length-histogram.mp48MB
- 📄 04_mapreduce-simple-example.srt4.9KB
- 📄 01_mapreduce-abstraction.mp49.4MB
- 📄 03_map-and-reduce-functions.srt4KB
- 📄 02_mapreduce-data-model.srt5.3KB
- 📄 01_mapreduce-abstraction.srt7KB
- 📄 05_mapreduce-simple-example-cont-d.srt4.4KB
- 📄 05_mapreduce-simple-example-cont-d.mp47.2MB
- 📄 04_mapreduce-simple-example.mp47.8MB
- 📄 07_mapreduce-examples-inverted-index-join.mp412.7MB
- 📄 06_mapreduce-example-word-length-histogram.srt4.4KB
- 📄 07_mapreduce-examples-inverted-index-join.srt8.5KB
📁 03_lesson-12-algorithms-in-mapreduce 16 项
- 📄 08_mapreduce-phases.srt8.7KB
- 📄 05_matrix-multiply-illustrated.mp47.9MB
- 📄 07_mapreduce-implementation.mp49.9MB
- 📄 01_relational-join-map-phase.srt5.9KB
- 📄 03_simple-social-network-analysis-counting-friends.mp47MB
- 📄 06_shared-nothing-computing.mp48.6MB
- 📄 02_relational-join-reduce-phase.mp49.6MB
- 📄 01_relational-join-map-phase.mp49.2MB
- 📄 04_matrix-multiply-overview.srt5.8KB
- 📄 04_matrix-multiply-overview.mp49.5MB
- 📄 07_mapreduce-implementation.srt7.6KB
- 📄 05_matrix-multiply-illustrated.srt4.7KB
- 📄 03_simple-social-network-analysis-counting-friends.srt5.1KB
- 📄 06_shared-nothing-computing.srt6.6KB
- 📄 02_relational-join-reduce-phase.srt6.7KB
- 📄 08_mapreduce-phases.mp411.2MB
📁 04_lesson-13-parallel-databases-vs-mapreduce 11 项
- 📄 03_teradata-example-mr-extensions.srt7.7KB
- 📄 03_teradata-example-mr-extensions.mp410MB
- 📄 05_rdbms-vs-hadoop-grep.srt7.6KB
- 📄 05_rdbms-vs-hadoop-grep.mp49.7MB
- 📄 06_rdbms-vs-hadoop-select-aggregate-join.srt4.4KB
- 📄 01_a-design-space-for-large-scale-data-systems.mp49.6MB
- 📄 06_rdbms-vs-hadoop-select-aggregate-join.mp45.7MB
- 📄 02_parallel-and-distributed-query-processing.mp410.3MB
- 📄 04_rdbms-vs-mapreduce-features.mp412.8MB
- 📄 02_parallel-and-distributed-query-processing.srt8.3KB
- 📄 01_a-design-space-for-large-scale-data-systems.srt7KB
📁 04_nosql-systems-and-concepts 77 项
📁 01_lesson-14-what-problems-do-nosql-systems-aim-to-solve 12 项
- 📄 01_nosql-context-and-roadmap.srt6.8KB
- 📄 04_two-phase-commit-and-consensus-protocols.mp410.1MB
- 📄 03_relaxing-consistency-guarantees.srt5.8KB
- 📄 01_nosql-context-and-roadmap.mp48MB
- 📄 04_two-phase-commit-and-consensus-protocols.srt8.6KB
- 📄 05_eventual-consistency.srt7.3KB
- 📄 03_relaxing-consistency-guarantees.mp48.1MB
- 📄 02_nosql-roundup.srt6.6KB
- 📄 06_cap-theorem.srt6.6KB
- 📄 02_nosql-roundup.mp48.8MB
- 📄 06_cap-theorem.mp48.6MB
- 📄 05_eventual-consistency.mp410MB
📁 02_lesson-15-early-key-value-systems-and-key-concepts 11 项
- 📄 04_consistent-hashing-cont-d.mp48.2MB
- 📄 03_memcached-consistent-hashing.srt3.9KB
- 📄 02_acid-major-impact-systems.mp410.2MB
- 📄 03_memcached-consistent-hashing.mp45.6MB
- 📄 05_dynamodb-vector-clocks.srt6.2KB
- 📄 01_types-of-nosql-systems.srt6.8KB
- 📄 06_vector-clocks-cont-d.mp49.6MB
- 📄 01_types-of-nosql-systems.mp49.4MB
- 📄 02_acid-major-impact-systems.srt7.5KB
- 📄 05_dynamodb-vector-clocks.mp49MB
- 📄 04_consistent-hashing-cont-d.srt5.1KB
📁 03_lesson-16-document-stores-and-extensible-record-stores 8 项
- 📄 01_couchdb-overview.srt6.4KB
- 📄 01_couchdb-overview.mp48.8MB
- 📄 02_couchb-views.srt5.2KB
- 📄 03_bigtable-overview.mp410.5MB
- 📄 03_bigtable-overview.srt7.3KB
- 📄 04_bigtable-implementation.mp411.9MB
- 📄 04_bigtable-implementation.srt8.4KB
- 📄 02_couchb-views.mp47.4MB
📁 04_lesson-17-extended-nosql-systems 12 项
- 📄 02_spanner.srt9.2KB
- 📄 05_bringing-back-joins.srt6.7KB
- 📄 04_mapreduce-based-systems.mp49.6MB
- 📄 03_spanner-cont-d-google-systems.mp413.6MB
- 📄 01_hbase-megastore.mp47.8MB
- 📄 02_spanner.mp412.7MB
- 📄 04_mapreduce-based-systems.srt7.3KB
- 📄 01_hbase-megastore.srt5.6KB
- 📄 06_nosql-rebuttal.srt7.1KB
- 📄 05_bringing-back-joins.mp47.7MB
- 📄 06_nosql-rebuttal.mp49.5MB
- 📄 03_spanner-cont-d-google-systems.srt9.8KB
📁 05_lesson-18-pig-programming-with-relational-algebra 10 项
- 📄 04_load-filter-group.mp411.5MB
- 📄 04_load-filter-group.srt8.7KB
- 📄 03_data-model.mp46.1MB
- 📄 01_almost-sql-pig.srt7.8KB
- 📄 05_group-distinct-foreach-flatten.srt7.1KB
- 📄 05_group-distinct-foreach-flatten.mp49.6MB
- 📄 01_almost-sql-pig.mp48.8MB
- 📄 03_data-model.srt5.3KB
- 📄 02_pig-architecture-and-performance.mp45.9MB
- 📄 02_pig-architecture-and-performance.srt4.7KB
📁 06_lesson-19-pig-analytics 12 项
- 📄 05_evaluation-walkthrough.srt6KB
- 📄 04_other-commands.srt5KB
- 📄 06_review.mp411.8MB
- 📄 03_skew.srt7.7KB
- 📄 01_cogroup-join.mp46.9MB
- 📄 02_join-algorithms.mp46.2MB
- 📄 06_review.srt8.9KB
- 📄 02_join-algorithms.srt5KB
- 📄 01_cogroup-join.srt4.5KB
- 📄 05_evaluation-walkthrough.mp47.5MB
- 📄 03_skew.mp410MB
- 📄 04_other-commands.mp45.9MB
📁 07_lesson-20-spark 5 项
- 📄 02_spark-examples.mp410.2MB
- 📄 01_context.mp46.5MB
- 📄 02_spark-examples.srt7.3KB
- 📄 03_rdds-benefits.srt9.5KB
- 📄 03_rdds-benefits.mp49.8MB
📁 05_graph-analytics 47 项
📁 01_lesson-21-structural-tasks 8 项
- 📄 04_connectivity-and-centrality.srt7KB
- 📄 02_structural-analysis.srt5.9KB
- 📄 03_degree-histograms-structure-of-the-web.mp48.6MB
- 📄 01_graph-overview.mp412.4MB
- 📄 03_degree-histograms-structure-of-the-web.srt6KB
- 📄 04_connectivity-and-centrality.mp49MB
- 📄 01_graph-overview.srt10.7KB
- 📄 02_structural-analysis.mp48MB
📁 02_lesson-22-traversal-tasks 8 项
- 📄 03_traversal-tasks-spanning-trees-and-circuits.srt8.4KB
- 📄 04_traversal-tasks-maximum-flow.mp43.8MB
- 📄 01_pagerank.srt5.5KB
- 📄 02_pagerank-in-more-detail.mp45.8MB
- 📄 02_pagerank-in-more-detail.srt4.5KB
- 📄 01_pagerank.mp48.1MB
- 📄 04_traversal-tasks-maximum-flow.srt2.3KB
- 📄 03_traversal-tasks-spanning-trees-and-circuits.mp411MB
📁 03_lesson-23-pattern-matching-tasks-and-graph-query 10 项
- 📄 04_querying-hybrid-graph-relational-data.srt6KB
- 📄 02_querying-edge-tables.mp47.7MB
- 📄 03_relational-algebra-and-datalog-for-graphs.mp49.5MB
- 📄 01_pattern-matching.mp412MB
- 📄 02_querying-edge-tables.srt6.3KB
- 📄 05_graph-query-example-nsa.srt10.6KB
- 📄 05_graph-query-example-nsa.mp413.9MB
- 📄 01_pattern-matching.srt9.2KB
- 📄 03_relational-algebra-and-datalog-for-graphs.srt7.4KB
- 📄 04_querying-hybrid-graph-relational-data.mp48.8MB
📁 04_lesson-24-recursive-queries 8 项
- 📄 03_recursive-queries-in-mapreduce.mp49MB
- 📄 04_the-end-game-problem.srt5.4KB
- 📄 02_evaluation-of-recursive-programs.mp47.2MB
- 📄 04_the-end-game-problem.mp46.7MB
- 📄 01_graph-query-example-recursion.srt5.8KB
- 📄 01_graph-query-example-recursion.mp47.9MB
- 📄 02_evaluation-of-recursive-programs.srt5.9KB
- 📄 03_recursive-queries-in-mapreduce.srt6.5KB
📁 05_lesson-24-representations-and-algorithms 8 项
- 📄 02_representation-adjacency-matrix.mp45MB
- 📄 01_representation-edge-table-adjacency-list.mp47.9MB
- 📄 01_representation-edge-table-adjacency-list.srt5.7KB
- 📄 03_pagerank-in-mapreduce.srt7.7KB
- 📄 04_pagerank-in-pregel.srt7.1KB
- 📄 03_pagerank-in-mapreduce.mp49.6MB
- 📄 02_representation-adjacency-matrix.srt3.3KB
- 📄 04_pagerank-in-pregel.mp49.4MB
- 📄 《人工智能与大数据》下——钟义信.mp494.8MB
- 📄 《人工智能与大数据》上——钟义信.mp4113MB
📁 深度学习新 9 项
- 📄 002_Neural Networks for Machine Learning.zip1.6GB
- 📄 042_Image and video processing.zip1.3GB
- 📄 neuralnets-2012-001.zip880MB
- 📄 004_Natural Language Processing Collins.zip1GB
- 📄 045_Computer Vision_计算机视觉.zip561MB
- 📄 《深度学习在互联网上的应用》——寒小阳.mp4124MB
- 📄 003_Probabilistic Graphical Models.zip1.5GB
- 📄 041_Audio Signal Processing for Music Applications.zip1.7GB
- 📄 Neural Networks for Machine Learning.zip717MB
📁 神经网络、深度学习方向 1 项
- 📄 神经网络、深度学习方向.zip456MB
📁 数据分析 391 项
📁 Stanford Statistical Learning 2014 69 项
- 📄 StatsLearning_Lect12b_111113.mov24.9MB
- 📄 StatsLearning_Lect11d_110913.mov28.5MB
- 📄 StatsLearning_Lect10_R-classification1_111213.mov23.5MB
- 📄 StatsLearning_Lect10_R-trees-B_111213.mov29.4MB
- 📄 StatsLearning_Lect10_R-modelselection_C_111213.mov9.5MB
- 📄 StatsLearning_Lect1-2a_111213_v2.mov43.8MB
- 📄 StatsLearning_Lect5a_112113.mov18.4MB
- 📄 StatsLearning_Lect10b_120213.mov16MB
- 📄 StatsLearning_Lect8j_110913.mov8.3MB
- 📄 StatsLearning_Lect11a_110913.mov23.6MB
- 📄 StatsLearning_Lect8g_110913.mov22.9MB
- 📄 StatsLearning_Lect5c_110613.mov24.7MB
- 📄 StatsLearning_R-Unsupervised_C_112713.mov13.5MB
- 📄 StatsLearning_Lect10a1_121213.mov20.1MB
- 📄 StatsLearning_Lect9a_110913.mov23.3MB
- 📄 StatsLearning_Lect1-2b_111213_v2.mov30.5MB
- 📄 StatsLearning_Lect12a_111113.mov22.2MB
- 📄 StatsLearning_Lect6e_110613.mov9.9MB
- 📄 StatsLearning_Lect8k_110913.mov24.5MB
- 📄 StatsLearning_Lect7e_110613.mov22.7MB
- 📄 StatsLearning_Lect6a_110613.mov15.6MB
- 📄 StatsLearning_Lect10_R-modelselection_D_111213.mov29.1MB
- 📄 StatsLearning_Lect3-4c_110613.mov15.1MB
- 📄 StatsLearning_Lect12_R-SVM-B_111213.mov19.4MB
- 📄 StatsLearning_Lect8de_110913.mov25.9MB
- 📄 StatsLearning_Lect12e_111113.mov13.4MB
- 📄 StatsLearning_Lect6f_111113new.mov10MB
- 📄 StatsLearning_Lect10d_120213.mov19.4MB
- 📄 StatsLearning_Lect8a_110913.mov26.2MB
- 📄 StatsLearning_Lect7d_110613.mov17.8MB
- 📄 StatsLearning_Lect11b_110913.mov11.7MB
- 📄 StatsLearning_Lect6d_110613.mov12.1MB
- 📄 StatsLearning_Lect6g_111113new.mov24.9MB
- 📄 StatsLearning_Lect6c_110613.mov15.3MB
- 📄 StatsLearning_Lect8i_110913.mov8.6MB
- 📄 Lecture+5_Trevor_Rstudio+v2+111113.mov23.7MB
- 📄 StatsLearning_Lect8f_110913.mov14.1MB
- 📄 StatsLearning_Lect12c_111113.mov27.1MB
- 📄 StatsLearning_Lect10_R-C-validation_111213.mov18.6MB
- 📄 StatsLearning_Lect5d2_110613.mov20.9MB
- 📄 StatsLearning_Lect10_R-modelselection_B_111213.mov18.8MB
- 📄 StatsLearning_Lect10_R-D-bootstrap_111213.mov12.1MB
- 📄 StatsLearning_Lect3-4b_110613.mov15.8MB
- 📄 lecture_7+r-regression+v2+111113.mov39.6MB
- 📄 StatsLearning_Lect10_R-modelselection_A_111213.mov20.4MB
- 📄 StatsLearning_R-Unsupervised_B_112713.mov12.8MB
- 📄 StatsLearning_Lect6h_111113.mov16MB
- 📄 StatsLearning_Lect10c_120213.mov23.7MB
- 📄 StatsLearning_Lect8h_110913.mov24.1MB
- 📄 JohnChambers_Interview_111213.mov42.1MB
- 📄 StatsLearning_Lect8c_110913.mov9.3MB
- 📄 StatsLearning_Lect5b_110613.mov11.4MB
- 📄 StatsLearning_Lect9c_110913.mov18.6MB
- 📄 StatsLearning_Lect11c_110913.mov27.4MB
- 📄 StatsLearning_Lect9d_110913.mov19.2MB
- 📄 StatsLearning_Lect9b_110913.mov17.9MB
- 📄 StatsLearning_Lect8b_110913.mov19.3MB
- 📄 StatsLearning_Lect7c_110613.mov14.7MB
- 📄 StatsLearning_Lect5d1_110613.mov19MB
- 📄 StatsLearning_Lect6b_110613.mov12.2MB
- 📄 StatsLearning_Lect7a_110613.mov23.9MB
- 📄 StatsLearning_Lect7b_110613.mov18.4MB
- 📄 StatsLearning_Lect12_R-SVM-A_111213.mov21.7MB
- 📄 StatsLearning_Lect3-4d_110613.mov32.5MB
- 📄 StatsLearning_Lect10a2_121213.mov16.3MB
- 📄 StatsLearning_Lect3-4a_110613.mov18.2MB
- 📄 StatsLearning_Lect10_R-trees-A_111213.mov19.5MB
- 📄 StatsLearning_Lect12d_111113.mov21.3MB
- 📄 StatsLearning_R-Unsupervised_A_112713.mov12.7MB
📁 The Data Scientist’s Toolbox 320 项
📁 01_Unit_0-_Introduction 21 项
- 📄 06_0-4_Course_Outline.mp46.1MB
- 📄 05_0-3-2_Big_Data.txt3.5KB
- 📄 03_0-2_Web-Scale_AI_and_Big_Data.srt4.1KB
- 📄 04_0-3-1_Web_Intelligence.srt3.1KB
- 📄 01_0-0_Preamble.txt2.8KB
- 📄 05_0-3-2_Big_Data.mp47.9MB
- 📄 06_0-4_Course_Outline.txt3.4KB
- 📄 01_0-0_Preamble.mp46MB
- 📄 02_0-1_Revisiting_Turings_Test.srt3.3KB
- 📄 03_0-2_Web-Scale_AI_and_Big_Data.mp45.2MB
- 📄 02_0-1_Revisiting_Turings_Test.mp44.2MB
- 📄 02_0-1_Revisiting_Turings_Test.txt2.1KB
- 📄 01_0-0_Preamble.srt4.3KB
- 📄 03_0-2_Web-Scale_AI_and_Big_Data.txt2.6KB
- 📄 07_0-5_Recap_and_Preview.srt3.1KB
- 📄 05_0-3-2_Big_Data.srt5.3KB
- 📄 07_0-5_Recap_and_Preview.txt2.1KB
- 📄 07_0-5_Recap_and_Preview.mp46.7MB
- 📄 06_0-4_Course_Outline.srt5.3KB
- 📄 04_0-3-1_Web_Intelligence.txt2KB
- 📄 04_0-3-1_Web_Intelligence.mp44.6MB
📁 02_Unit_1-_Look 51 项
- 📄 02_1-2_Index_Creation.srt5.9KB
- 📄 06_1-5-1_Page_Rank_and_Memory.srt5.7KB
- 📄 14_1-7-5_LSH_Intuition.txt2.5KB
- 📄 09_1-6-2_Searching_Structured_Data.txt4.1KB
- 📄 11_1-7-2_Locality_Sensitive_Hashing.mp46.2MB
- 📄 02_1-2_Index_Creation.txt3.8KB
- 📄 16_1-7-7_Associative_Memories.srt1.1KB
- 📄 07_1-5-2_Google_and_the_Mind.srt4.8KB
- 📄 12_1-7-3_LSH_Example_-_1.srt2.5KB
- 📄 16_1-7-7_Associative_Memories.txt693B
- 📄 08_1-6-1_Enterprise_Search.txt3.1KB
- 📄 17_1-7-8_Recap_and_Preview.mp43MB
- 📄 01_1-1_Basic_Indexing.mp49.1MB
- 📄 13_1-7-4_LSH_Example_-_2.srt1.8KB
- 📄 12_1-7-3_LSH_Example_-_1.txt1.7KB
- 📄 06_1-5-1_Page_Rank_and_Memory.txt3.6KB
- 📄 11_1-7-2_Locality_Sensitive_Hashing.srt4.4KB
- 📄 15_1-7-6_High-dimensional_Objects.txt4.9KB
- 📄 15_1-7-6_High-dimensional_Objects.srt7.6KB
- 📄 03_1-3_Complexity_of_Index_Creation.srt3.6KB
- 📄 12_1-7-3_LSH_Example_-_1.mp44.8MB
- 📄 10_1-7-1_Object_Search.txt3.4KB
- 📄 04_1-4-1_Ranking_-_1.mp45.4MB
- 📄 17_1-7-8_Recap_and_Preview.txt1.7KB
- 📄 08_1-6-1_Enterprise_Search.mp46MB
- 📄 08_1-6-1_Enterprise_Search.srt4.9KB
- 📄 09_1-6-2_Searching_Structured_Data.mp48.6MB
- 📄 05_1-4-2_Ranking_-_2.mp46.4MB
- 📄 07_1-5-2_Google_and_the_Mind.mp47.1MB
- 📄 04_1-4-1_Ranking_-_1.txt2.9KB
- 📄 13_1-7-4_LSH_Example_-_2.mp42.9MB
- 📄 03_1-3_Complexity_of_Index_Creation.mp44.8MB
- 📄 06_1-5-1_Page_Rank_and_Memory.mp48MB
- 📄 04_1-4-1_Ranking_-_1.srt4.3KB
- 📄 01_1-1_Basic_Indexing.srt7.2KB
- 📄 10_1-7-1_Object_Search.mp46.5MB
- 📄 03_1-3_Complexity_of_Index_Creation.txt2.4KB
- 📄 17_1-7-8_Recap_and_Preview.srt2.7KB
- 📄 14_1-7-5_LSH_Intuition.srt3.8KB
- 📄 10_1-7-1_Object_Search.srt5.2KB
- 📄 01_1-1_Basic_Indexing.txt4.7KB
- 📄 15_1-7-6_High-dimensional_Objects.mp410.4MB
- 📄 02_1-2_Index_Creation.mp47.2MB
- 📄 14_1-7-5_LSH_Intuition.mp45.1MB
- 📄 13_1-7-4_LSH_Example_-_2.txt1.2KB
- 📄 05_1-4-2_Ranking_-_2.srt4.9KB
- 📄 09_1-6-2_Searching_Structured_Data.srt6.2KB
- 📄 16_1-7-7_Associative_Memories.mp47.7MB
- 📄 05_1-4-2_Ranking_-_2.txt3.2KB
- 📄 07_1-5-2_Google_and_the_Mind.txt3.1KB
- 📄 11_1-7-2_Locality_Sensitive_Hashing.txt2.8KB
📁 03_Unit_2-_Listen 39 项
- 📄 05_2-5_TF-IDF_Example.srt6.6KB
- 📄 12_2-11_Machine_Learning_-_Limits.txt6.8KB
- 📄 11_2-10_Mutual_Information.mp412.1MB
- 📄 04_2-4_TF-IDF.mp410.8MB
- 📄 13_2-12_Recap_and_Preview.srt3.5KB
- 📄 09_2-8-2_Naive_Bayes.srt8.3KB
- 📄 11_2-10_Mutual_Information.txt6.1KB
- 📄 04_2-4_TF-IDF.srt8.6KB
- 📄 03_2-3_Information_and_Advertising.mp47.4MB
- 📄 12_2-11_Machine_Learning_-_Limits.srt10.5KB
- 📄 09_2-8-2_Naive_Bayes.txt5.4KB
- 📄 03_2-3_Information_and_Advertising.txt3.5KB
- 📄 08_2-8-1_Bayes_Rule.srt4.7KB
- 📄 01_2-1_Preamble_-_Listen.txt1.8KB
- 📄 07_2-7_Machine_Learning_Intro.srt8.7KB
- 📄 05_2-5_TF-IDF_Example.mp48MB
- 📄 10_2-9_Sentiment_Analysis.txt5.2KB
- 📄 08_2-8-1_Bayes_Rule.mp46.2MB
- 📄 03_2-3_Information_and_Advertising.srt5.3KB
- 📄 02_2-2_Shannon_Information.srt5.5KB
- 📄 06_2-6_Language_and_Information.txt5.6KB
- 📄 13_2-12_Recap_and_Preview.mp44.3MB
- 📄 04_2-4_TF-IDF.txt5.6KB
- 📄 10_2-9_Sentiment_Analysis.mp410.1MB
- 📄 02_2-2_Shannon_Information.txt3.6KB
- 📄 07_2-7_Machine_Learning_Intro.mp411.7MB
- 📄 05_2-5_TF-IDF_Example.txt4.4KB
- 📄 09_2-8-2_Naive_Bayes.mp410.8MB
- 📄 01_2-1_Preamble_-_Listen.srt2.8KB
- 📄 10_2-9_Sentiment_Analysis.srt8KB
- 📄 02_2-2_Shannon_Information.mp48.2MB
- 📄 13_2-12_Recap_and_Preview.txt2.4KB
- 📄 12_2-11_Machine_Learning_-_Limits.mp413.7MB
- 📄 01_2-1_Preamble_-_Listen.mp43.9MB
- 📄 11_2-10_Mutual_Information.srt9.3KB
- 📄 06_2-6_Language_and_Information.mp411.7MB
- 📄 06_2-6_Language_and_Information.srt8.6KB
- 📄 07_2-7_Machine_Learning_Intro.txt5.7KB
- 📄 08_2-8-1_Bayes_Rule.txt3KB
📁 04_Unit_3-_Load_-_I 24 项
- 📄 07_3-6_Parallel_Efficiency_of_Map-Reduce.srt5.9KB
- 📄 08_3-7_Inside_Map-Reduce.txt6.7KB
- 📄 08_3-7_Inside_Map-Reduce.mp412.1MB
- 📄 01_3-1_Preamble.mp45.8MB
- 📄 03_3-3_Map-Reduce.txt7.8KB
- 📄 04_3-4_Map-Reduce_Example_in_Octo.srt11.6KB
- 📄 05_3-4-1_Map-Reduce_Example_in_Mincemeat.mp42.8MB
- 📄 06_3-5_Map-Reduce_Applications.srt14.3KB
- 📄 06_3-5_Map-Reduce_Applications.txt9.2KB
- 📄 07_3-6_Parallel_Efficiency_of_Map-Reduce.txt3.8KB
- 📄 07_3-6_Parallel_Efficiency_of_Map-Reduce.mp411.1MB
- 📄 01_3-1_Preamble.srt5.2KB
- 📄 03_3-3_Map-Reduce.srt12KB
- 📄 02_3-2_Parallel_Computing.srt9.5KB
- 📄 03_3-3_Map-Reduce.mp414.9MB
- 📄 01_3-1_Preamble.txt3.4KB
- 📄 06_3-5_Map-Reduce_Applications.mp417.9MB
- 📄 05_3-4-1_Map-Reduce_Example_in_Mincemeat.srt2.6KB
- 📄 02_3-2_Parallel_Computing.mp410.7MB
- 📄 02_3-2_Parallel_Computing.txt6.3KB
- 📄 05_3-4-1_Map-Reduce_Example_in_Mincemeat.txt1.8KB
- 📄 08_3-7_Inside_Map-Reduce.srt10.1KB
- 📄 04_3-4_Map-Reduce_Example_in_Octo.mp415.7MB
- 📄 04_3-4_Map-Reduce_Example_in_Octo.txt7.6KB
📁 05_Unit_4-_Load_-_II 30 项
- 📄 04_4-3_Evolution_of_Databases.mp411MB
- 📄 06_4-5_NoSQL_and_Eventual_Consistency.mp416.4MB
- 📄 03_4-2_Database_Technology.mp416.1MB
- 📄 10_4-9_Database_Trends_and_Summary.srt9.4KB
- 📄 09_4-8_Relational_vs_Big-Data_Technologies.srt11.3KB
- 📄 05_4-4_Big-Table_and_HBase.txt7KB
- 📄 02_4-1_Distributed_File_Systems.srt13.1KB
- 📄 07_4-6_Future_of_NoSQL_and_Dremel.srt10.3KB
- 📄 08_4-7_Evolution_of_SQL_and_Map-Reduce.mp411.8MB
- 📄 08_4-7_Evolution_of_SQL_and_Map-Reduce.txt7KB
- 📄 01_4-0_Preamble.mp42MB
- 📄 08_4-7_Evolution_of_SQL_and_Map-Reduce.srt10.7KB
- 📄 01_4-0_Preamble.srt1.9KB
- 📄 03_4-2_Database_Technology.txt8.6KB
- 📄 04_4-3_Evolution_of_Databases.srt9.2KB
- 📄 07_4-6_Future_of_NoSQL_and_Dremel.mp411.7MB
- 📄 10_4-9_Database_Trends_and_Summary.txt6.4KB
- 📄 04_4-3_Evolution_of_Databases.txt6KB
- 📄 06_4-5_NoSQL_and_Eventual_Consistency.srt12.3KB
- 📄 03_4-2_Database_Technology.srt13KB
- 📄 10_4-9_Database_Trends_and_Summary.mp411.9MB
- 📄 02_4-1_Distributed_File_Systems.txt8.6KB
- 📄 09_4-8_Relational_vs_Big-Data_Technologies.mp414.4MB
- 📄 01_4-0_Preamble.txt1.3KB
- 📄 09_4-8_Relational_vs_Big-Data_Technologies.txt7.6KB
- 📄 07_4-6_Future_of_NoSQL_and_Dremel.txt6.7KB
- 📄 02_4-1_Distributed_File_Systems.mp414.7MB
- 📄 05_4-4_Big-Table_and_HBase.srt10.7KB
- 📄 05_4-4_Big-Table_and_HBase.mp412.1MB
- 📄 06_4-5_NoSQL_and_Eventual_Consistency.txt8.2KB
📁 06_Guest_Lecture_1-_Graph_Databases 18 项
- 📄 06_G6_Q_amp_A.mp47.9MB
- 📄 05_G5_Graph_Data_Management.txt8.1KB
- 📄 01_G1_Introduction_to_Graph_Data.mp417.3MB
- 📄 06_G6_Q_amp_A.txt4KB
- 📄 03_G3_Linked_Open_Data.srt15.3KB
- 📄 01_G1_Introduction_to_Graph_Data.srt14.8KB
- 📄 03_G3_Linked_Open_Data.txt9.7KB
- 📄 02_G2_Graph_Query_Languages.srt17.9KB
- 📄 04_G4_Challenges_and_Efficiency.mp413.7MB
- 📄 05_G5_Graph_Data_Management.mp415.8MB
- 📄 06_G6_Q_amp_A.srt6.6KB
- 📄 04_G4_Challenges_and_Efficiency.txt7.3KB
- 📄 02_G2_Graph_Query_Languages.mp421.1MB
- 📄 01_G1_Introduction_to_Graph_Data.txt9.7KB
- 📄 03_G3_Linked_Open_Data.mp420.3MB
- 📄 05_G5_Graph_Data_Management.srt12.6KB
- 📄 04_G4_Challenges_and_Efficiency.srt11.1KB
- 📄 02_G2_Graph_Query_Languages.txt11.4KB
📁 07_Unit_5-_Learn 27 项
- 📄 07_5-7_Learning_Latent_Models.srt16.3KB
- 📄 09_5-9_Recap_and_Preview.srt2.6KB
- 📄 05_5-5_Association_Rule_Mining.txt5.8KB
- 📄 01_5-1_Preamble.mp44MB
- 📄 05_5-5_Association_Rule_Mining.mp411.3MB
- 📄 08_5-8_Grounded_Learning.srt6.1KB
- 📄 02_5-2_Classification_Re-visited.srt13.6KB
- 📄 08_5-8_Grounded_Learning.mp47.4MB
- 📄 02_5-2_Classification_Re-visited.mp417MB
- 📄 03_5-3_Learning_Groupings_-_Clustering.mp416.1MB
- 📄 09_5-9_Recap_and_Preview.txt1.7KB
- 📄 01_5-1_Preamble.txt2KB
- 📄 03_5-3_Learning_Groupings_-_Clustering.srt13.7KB
- 📄 08_5-8_Grounded_Learning.txt4KB
- 📄 02_5-2_Classification_Re-visited.txt8.8KB
- 📄 04_5-4_Learning_Rules.txt6.8KB
- 📄 04_5-4_Learning_Rules.srt10.4KB
- 📄 06_5-6_Learning_with_Big_Data.txt5.6KB
- 📄 01_5-1_Preamble.srt3.1KB
- 📄 06_5-6_Learning_with_Big_Data.srt8.6KB
- 📄 03_5-3_Learning_Groupings_-_Clustering.txt9KB
- 📄 07_5-7_Learning_Latent_Models.mp419.6MB
- 📄 05_5-5_Association_Rule_Mining.srt8.9KB
- 📄 06_5-6_Learning_with_Big_Data.mp410.4MB
- 📄 09_5-9_Recap_and_Preview.mp43.2MB
- 📄 07_5-7_Learning_Latent_Models.txt10.6KB
- 📄 04_5-4_Learning_Rules.mp413.1MB
📁 08_Unit_6-_Connect 36 项
- 📄 07_6-7_Naive_Bayes_Revisited.srt12.8KB
- 📄 01_6-1_Preamble.mp411.7MB
- 📄 08_6-8-1_Bayesian_Networks_-_1.srt10.9KB
- 📄 04_6-4_Semantic_Web.mp48.3MB
- 📄 11_6-10_Recap_and_Preview.mp49.1MB
- 📄 05_6-5_Logic_and_Uncertainty.srt8.3KB
- 📄 08_6-8-1_Bayesian_Networks_-_1.txt7.1KB
- 📄 10_6-9_Information_Extraction.mp416.6MB
- 📄 12_6-11-Programming_HW_6.srt4.8KB
- 📄 01_6-1_Preamble.txt6.6KB
- 📄 12_6-11-Programming_HW_6.txt3.2KB
- 📄 02_6-2_Logical_Inference.mp412.7MB
- 📄 06_6-6_Algebra_of_Potentials.txt7.7KB
- 📄 02_6-2_Logical_Inference.srt9KB
- 📄 05_6-5_Logic_and_Uncertainty.mp411.6MB
- 📄 01_6-1_Preamble.srt10.2KB
- 📄 04_6-4_Semantic_Web.srt6.4KB
- 📄 08_6-8-1_Bayesian_Networks_-_1.mp411.8MB
- 📄 05_6-5_Logic_and_Uncertainty.txt5.4KB
- 📄 07_6-7_Naive_Bayes_Revisited.txt8.4KB
- 📄 09_6-8-2_Bayesian_Networks_-_2.srt6.1KB
- 📄 06_6-6_Algebra_of_Potentials.srt11.7KB
- 📄 04_6-4_Semantic_Web.txt4.2KB
- 📄 09_6-8-2_Bayesian_Networks_-_2.txt4KB
- 📄 12_6-11-Programming_HW_6.mp45.9MB
- 📄 09_6-8-2_Bayesian_Networks_-_2.mp47.2MB
- 📄 02_6-2_Logical_Inference.txt5.9KB
- 📄 06_6-6_Algebra_of_Potentials.mp415.3MB
- 📄 11_6-10_Recap_and_Preview.txt5.1KB
- 📄 03_6-3_Resolution_and_its_Limits.txt10.2KB
- 📄 07_6-7_Naive_Bayes_Revisited.mp414.4MB
- 📄 11_6-10_Recap_and_Preview.srt7.6KB
- 📄 03_6-3_Resolution_and_its_Limits.srt15.7KB
- 📄 10_6-9_Information_Extraction.srt13.8KB
- 📄 03_6-3_Resolution_and_its_Limits.mp420.3MB
- 📄 10_6-9_Information_Extraction.txt9.1KB
📁 09_Unit_7-_Predict 31 项
- 📄 10_7-10_Blackboard_Architecture.txt7KB
- 📄 04_7-4_Nonlinear_Models.srt10.2KB
- 📄 08_7-8_Hierarchical_Temporal_Memory_-_I.srt10.1KB
- 📄 01_7-1_Preamble.mp43MB
- 📄 03_7-3_Least_Squares.mp415.4MB
- 📄 08_7-8_Hierarchical_Temporal_Memory_-_I.txt6.6KB
- 📄 09_7-9_Hierarchical_Temporal_Memory_-_II.srt12.2KB
- 📄 07_7-7_Which_Technique.txt5.3KB
- 📄 05_7-5_Learning_Parameters.mp416.1MB
- 📄 02_7-2_Linear_Prediction.txt7.9KB
- 📄 03_7-3_Least_Squares.srt14KB
- 📄 10_7-10_Blackboard_Architecture.srt10.7KB
- 📄 02_7-2_Linear_Prediction.mp413.6MB
- 📄 06_7-6_Prediction_Applications.txt6.6KB
- 📄 06_7-6_Prediction_Applications.mp410.9MB
- 📄 02_7-2_Linear_Prediction.srt12KB
- 📄 09_7-9_Hierarchical_Temporal_Memory_-_II.txt7.9KB
- 📄 01_7-1_Preamble.srt2.9KB
- 📄 01_7-1_Preamble.txt1.9KB
- 📄 07_7-7_Which_Technique.mp48MB
- 📄 03_7-3_Least_Squares.txt9KB
- 📄 09_7-9_Hierarchical_Temporal_Memory_-_II.mp413.8MB
- 📄 05_7-5_Learning_Parameters.txt9.7KB
- 📄 04_7-4_Nonlinear_Models.mp411.6MB
- 📄 04_7-4_Nonlinear_Models.txt6.7KB
- 📄 11_7-11_Homework_Assignment-_Genomic_Data_Analysis.mp419.8MB
- 📄 08_7-8_Hierarchical_Temporal_Memory_-_I.mp411.2MB
- 📄 07_7-7_Which_Technique.srt8KB
- 📄 10_7-10_Blackboard_Architecture.mp412.1MB
- 📄 05_7-5_Learning_Parameters.srt14.8KB
- 📄 06_7-6_Prediction_Applications.srt10.2KB
📁 10_Guest_Lecture_2-_Markov_Logic 9 项
- 📄 05_M5_Related_Models.mp415.4MB
- 📄 09_M9_Research_Directions_in_Markov_Logic.mp48MB
- 📄 01_M1_Motivation.mp415.3MB
- 📄 07_M7_Entity_Resolution_Example_-_2.mp415.2MB
- 📄 08_M8_Social_Network_Analysis_using_MLN.mp410.2MB
- 📄 03_M3_Markov_Logic_via_an_Example.mp411.8MB
- 📄 06_M6_Entity_Resolution_Example_-_1.mp413MB
- 📄 04_M4_Markov_Logic_Formalism.mp416.7MB
- 📄 02_M2_Markov_Networks_and_Logic.mp412MB
📁 11_Wrap_up_and_Final_Exam 3 项
- 📄 01_Course_Recap_and_Pointers.txt7.5KB
- 📄 01_Course_Recap_and_Pointers.mp411.5MB
- 📄 01_Course_Recap_and_Pointers.srt11.6KB
📁 Lecture Slides 8 项
- 📄 8-Predict Lecture Slides.pdf3.8MB
- 📄 3-Load-Lecture-Slides.pdf1.7MB
- 📄 6-Connect Lecture Slides.pdf318KB
- 📄 4-Load Lecture Slides.pdf820KB
- 📄 2-Listen Lecture Slides.pdf1.5MB
- 📄 1-Look Lecture Slides.pdf1.2MB
- 📄 0-Introduction Lecture Slides.pdf842KB
- 📄 5-Learn Lecture Slides.pdf542KB
📁 Tools 3 项
- 📄 octopy-0.1.zip5.2KB
- 📄 michaelfairley-mincemeatpy-v0.1.2-0-gfb642f3.zip6.5KB
- 📄 orange-win-w-python-snapshot-hg-2013-03-25-py2.7.exe103MB
- 📄 hw3data.zip2.6MB
- 📄 web intelligence and big data--笔记-2012.pdf1.3MB
- 📄 homework2.pdf28.5KB
- 📄 genestrain.tab.zip6.2MB
- 📄 stopwords.py1.7KB
- 📄 genesblind.tab.zip1.2MB
- 📄 HW6.pdf147KB
📁 斯坦福NLP课程 1 项
- 📄 斯坦福NLP课程.zip1.8GB
📁 推荐系统新 6 项
- 📄 recommender-systems.z014GB
- 📄 recommender-systems.zip2.4GB
- 📄 推荐系统视频.zip227MB
- 📄 CS101 Intro to Computer Science Building a Search Engine.zip4GB
- 📄 Introduction to RecSys 2013.zip2.8GB
- 📄 011_Introduction to Recommender Systems.zip2.7GB
📁 自然语言处理（NLP）新 2 项
- 📄 自然语言处理(NLP).zip954MB
- 📄 自然语言处理(NLP).z014GB
📁 CNCC 2016 演讲 1 项
- 📄 CNCC 2016 演讲.zip25.7MB
📁 Python入门课程 25 项
📁 04_Week_4 4 项
- 📄 02_str-_indexing_and_slicing_4-53.srt5.4KB
- 📄 05_IDLEs_Debugger_4-00.mp44.2MB
- 📄 01_More_str_operators_2-16.pdf168KB
- 📄 01_More_str_operators_2-16.srt2.5KB
📁 07_Week_7 19 项
- 📄 02_Type_dict_9-53.srt11.6KB
- 📄 02_Type_dict_9-53.mp49.2MB
- 📄 01_Tuples_2-07.html3.6KB
- 📄 02_Type_dict_9-53.html6.2KB
- 📄 04_Populating_a_Dictionary_7-18.mp48MB
- 📄 04_Populating_a_Dictionary_7-18.srt8.5KB
- 📄 03_Inverting_a_Dictionary_4-28.mp44.5MB
- 📄 03_Inverting_a_Dictionary_4-28.pdf191KB
- 📄 03_Inverting_a_Dictionary_4-28.txt2.9KB
- 📄 01_Tuples_2-07.pdf311KB
- 📄 02_Type_dict_9-53_0_Quiz_1.pdf148KB
- 📄 03_Inverting_a_Dictionary_4-28.html3.3KB
- 📄 02_Type_dict_9-53.txt7.5KB
- 📄 02_Type_dict_9-53.pdf254KB
- 📄 01_Tuples_2-07.mp42.1MB
- 📄 01_Tuples_2-07.srt2.1KB
- 📄 04_Populating_a_Dictionary_7-18.txt5.6KB
- 📄 01_Tuples_2-07.txt1.3KB
- 📄 03_Inverting_a_Dictionary_4-28.srt4.6KB
📁 TensorFlow教程(1) 3 项
📁 tensorflow安装、技巧，教材等问题 1 项
- 📄 tensorflow安装、技巧，教材等问题.zip450MB
- 📄 TensorFlow教程.zip25.6MB
📁 搜索引擎 36 项
📁 搜索引擎搜索技巧[MP4] 13 项
- 📄 使用说明.txt294B
- 📄 7.Google Schoolar检索语法.mp473.8MB
- 📄 12.百度搜索技巧.mp4171MB
- 📄 1.百度基本检索与浏览.mp467.4MB
- 📄 2.百度检索语法.mp4101MB
- 📄 10.百度专利搜索.mp445.5MB
- 📄 免责声明.txt568B
- 📄 3.Bing搜索引擎.mp4108MB
- 📄 6.google Scholar基本检索与浏览.mp457.3MB
- 📄 4.Google基本检索与浏览.mp482.6MB
- 📄 8.期刊界基本检索与浏览.mp436.7MB
- 📄 5.百度必应与Google.mp418.7MB
- 📄 13.google搜索技巧.mp470.8MB
📁 SEO教程 11 项
- 📄 第二节：SEO关键词.mp4374MB
- 📄 第六节：网站结构优化.mp4500MB
- 📄 第九节：http状态码.mp4497MB
- 📄 第八节：网站权重.mp4563MB
- 📄 第四节：域名和空间.mp4933MB
- 📄 第七节：网站内容优化.mp4944MB
- 📄 第十节：404页面.mp4413MB
- 📄 第三节：设置网站标题.avi160MB
- 📄 第一节：初识SEO.mp4578MB
- 📄 第十一节：外链建设.mp41.4GB
- 📄 第十二节：友链建设.mp4758MB
📁 SEO网站优化视频教程 8 项
- 📄 seowhy-02.f4v230MB
- 📄 seowhy-01.f4v174MB
- 📄 seowhy-03.f4v198MB
- 📄 seowhy-06.f4v91.8MB
- 📄 seowhy-07.f4v161MB
- 📄 seowhy-04.f4v224MB
- 📄 @备注.txt802B
- 📄 seowhy-05.f4v134MB
- 📄 这就是搜索引擎 核心技术详解.pdf34.2MB
📁 搜索引擎(1) 10 项
- 📄 2-第二讲_社区与垂直搜索.pdf1.9MB
- 📄 5-第五讲：搜索引擎推广与商务智能.pdf3.3MB
- 📄 1.2-第一讲_商用搜索引擎的架构与原理-排序策略-2.pdf1.4MB
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