种子简介

种子名称： [FreeCourseSite.com] Udemy - Building Recommender Systems with Machine Learning and AI
文件类型： 视频
文件数目： 109个文件
文件大小： 4.48 GB
收录时间： 2019-2-17 09:54
已经下载： 3次
资源热度： 224
最近下载： 2024-12-26 20:53

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:b80e4012e2f75f1d9ccebb0727f380b94f979940&dn=[FreeCourseSite.com] Udemy - Building Recommender Systems with Machine Learning and AI 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[FreeCourseSite.com] Udemy - Building Recommender Systems with Machine Learning and AI.torrent

01 Getting Started/001 Udemy 101 Getting the Most From This Course.mp419.71MB

01 Getting Started/002 [Activity] Install Anaconda course materials and create movie recommendations.mp4104.08MB

01 Getting Started/003 Course Roadmap.mp427.58MB

01 Getting Started/004 Types of Recommenders.mp426.82MB

01 Getting Started/005 Understanding You through Implicit and Explicit Ratings.mp420.72MB

01 Getting Started/006 Top-N Recommender Architecture.mp437.09MB

01 Getting Started/007 [Quiz] Review the basics of recommender systems..mp421.3MB

02 Introduction to Python [Optional]/008 [Activity] The Basics of Python.mp443.03MB

02 Introduction to Python [Optional]/009 Data Structures in Python.mp424.41MB

02 Introduction to Python [Optional]/010 Functions in Python.mp412.27MB

02 Introduction to Python [Optional]/011 [Exercise] Booleans loops and a hands-on challenge.mp413.86MB

03 Evaluating Recommender Systems/012 TrainTest and Cross Validation.mp429.05MB

03 Evaluating Recommender Systems/013 Accuracy Metrics (RMSE MAE).mp440.28MB

03 Evaluating Recommender Systems/014 Top-N Hit Rate - Many Ways.mp424.53MB

03 Evaluating Recommender Systems/015 Coverage Diversity and Novelty.mp413.73MB

03 Evaluating Recommender Systems/016 Churn Responsiveness and AB Tests.mp460.94MB

03 Evaluating Recommender Systems/017 [Quiz] Review ways to measure your recommender..mp412.83MB

03 Evaluating Recommender Systems/018 [Activity] Walkthrough of RecommenderMetrics.py.mp464.3MB

03 Evaluating Recommender Systems/019 [Activity] Walkthrough of TestMetrics.py.mp454.36MB

03 Evaluating Recommender Systems/020 [Activity] Measure the Performance of SVD Recommendations.mp421.56MB

04 A Recommender Engine Framework/021 Our Recommender Engine Architecture.mp432.73MB

04 A Recommender Engine Framework/022 [Activity] Recommender Engine Walkthrough Part 1.mp437.88MB

04 A Recommender Engine Framework/023 [Activity] Recommender Engine Walkthrough Part 2.mp439.59MB

04 A Recommender Engine Framework/024 [Activity] Review the Results of our Algorithm Evaluation..mp434.57MB

05 Content-Based Filtering/025 Content-Based Recommendations and the Cosine Similarity Metric.mp461.58MB

05 Content-Based Filtering/026 K-Nearest-Neighbors and Content Recs.mp419.61MB

05 Content-Based Filtering/027 [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp452.36MB

05 Content-Based Filtering/028 [Activity] Bleeding Edge Alert Mise en Scene Recommendations.mp446.52MB

05 Content-Based Filtering/029 [Exercise] Dive Deeper into Content-Based Recommendations.mp424.11MB

06 Neighborhood-Based Collaborative Filtering/030 Measuring Similarity and Sparsity.mp459.1MB

06 Neighborhood-Based Collaborative Filtering/031 Similarity Metrics.mp430.68MB

06 Neighborhood-Based Collaborative Filtering/032 User-based Collaborative Filtering.mp434.21MB

06 Neighborhood-Based Collaborative Filtering/033 [Activity] User-based Collaborative Filtering Hands-On.mp448.65MB

06 Neighborhood-Based Collaborative Filtering/034 Item-based Collaborative Filtering.mp452.23MB

06 Neighborhood-Based Collaborative Filtering/035 [Activity] Item-based Collaborative Filtering Hands-On.mp426.81MB

06 Neighborhood-Based Collaborative Filtering/036 [Exercise] Tuning Collaborative Filtering Algorithms.mp419.7MB

06 Neighborhood-Based Collaborative Filtering/037 [Activity] Evaluating Collaborative Filtering Systems Offline.mp415.43MB

06 Neighborhood-Based Collaborative Filtering/038 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp49.5MB

06 Neighborhood-Based Collaborative Filtering/039 KNN Recommenders.mp424.85MB

06 Neighborhood-Based Collaborative Filtering/040 [Activity] Running User and Item-Based KNN on MovieLens.mp423.76MB

06 Neighborhood-Based Collaborative Filtering/041 [Exercise] Experiment with different KNN parameters..mp441.26MB

06 Neighborhood-Based Collaborative Filtering/042 Bleeding Edge Alert Translation-Based Recommendations.mp421.49MB

07 Matrix Factorization Methods/043 Principal Component Analysis (PCA).mp461.2MB

07 Matrix Factorization Methods/044 Singular Value Decomposition.mp425.07MB

07 Matrix Factorization Methods/045 [Activity] Running SVD and SVD on MovieLens.mp437.49MB

07 Matrix Factorization Methods/046 Improving on SVD.mp423.07MB

07 Matrix Factorization Methods/047 [Exercise] Tune the hyperparameters on SVD.mp412.46MB

07 Matrix Factorization Methods/048 Bleeding Edge Alert Sparse Linear Methods (SLIM).mp426.46MB

08 Introduction to Deep Learning [Optional]/049 Deep Learning Introduction.mp417.62MB

08 Introduction to Deep Learning [Optional]/050 Deep Learning Pre-Requisites.mp437.05MB

08 Introduction to Deep Learning [Optional]/051 History of Artificial Neural Networks.mp484.21MB

08 Introduction to Deep Learning [Optional]/052 [Activity] Playing with Tensorflow.mp4145.4MB

08 Introduction to Deep Learning [Optional]/053 Training Neural Networks.mp438.35MB

08 Introduction to Deep Learning [Optional]/054 Tuning Neural Networks.mp431.27MB

08 Introduction to Deep Learning [Optional]/055 Introduction to Tensorflow.mp492.47MB

08 Introduction to Deep Learning [Optional]/056 [Activity] Handwriting Recognition with Tensorflow part 1.mp4181.85MB

08 Introduction to Deep Learning [Optional]/057 [Activity] Handwriting Recognition with Tensorflow part 2.mp457.58MB

08 Introduction to Deep Learning [Optional]/058 Introduction to Keras.mp416.45MB

08 Introduction to Deep Learning [Optional]/059 [Activity] Handwriting Recognition with Keras.mp488.71MB

08 Introduction to Deep Learning [Optional]/060 Classifier Patterns with Keras.mp424.84MB

08 Introduction to Deep Learning [Optional]/061 [Exercise] Predict Political Parties of Politicians with Keras.mp4100.21MB

08 Introduction to Deep Learning [Optional]/062 Intro to Convolutional Neural Networks (CNNs).mp478.19MB

08 Introduction to Deep Learning [Optional]/063 CNN Architectures.mp422.54MB

08 Introduction to Deep Learning [Optional]/064 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp482.28MB

08 Introduction to Deep Learning [Optional]/065 Intro to Recurrent Neural Networks (RNNs).mp449.64MB

08 Introduction to Deep Learning [Optional]/066 Training Recurrent Neural Networks.mp420.72MB

08 Introduction to Deep Learning [Optional]/067 [Activity] Sentiment Analysis of Movie Reviews using RNNs and Keras.mp4119.78MB

09 Deep Learning for Recommender Systems/068 Intro to Deep Learning for Recommenders.mp442.62MB

09 Deep Learning for Recommender Systems/069 Restricted Boltzmann Machines (RBMs).mp431.67MB

09 Deep Learning for Recommender Systems/070 [Activity] Recommendations with RBMs part 1.mp4144.44MB

09 Deep Learning for Recommender Systems/071 [Activity] Recommendations with RBMs part 2.mp476.73MB

09 Deep Learning for Recommender Systems/072 [Activity] Evaluating the RBM Recommender.mp437.67MB

09 Deep Learning for Recommender Systems/073 [Exercise] Tuning Restricted Boltzmann Machines.mp433.61MB

09 Deep Learning for Recommender Systems/074 Exercise Results Tuning a RBM Recommender.mp411.85MB

09 Deep Learning for Recommender Systems/075 Auto-Encoders for Recommendations Deep Learning for Recs.mp426.91MB

09 Deep Learning for Recommender Systems/076 [Activity] Recommendations with Deep Neural Networks.mp475.41MB

09 Deep Learning for Recommender Systems/077 Clickstream Recommendations with RNNs.mp448.72MB

09 Deep Learning for Recommender Systems/078 [Exercise] Get GRU4Rec Working on your Desktop.mp47.46MB

09 Deep Learning for Recommender Systems/079 Exercise Results GRU4Rec in Action.mp462.65MB

09 Deep Learning for Recommender Systems/080 Bleeding Edge Alert Deep Factorization Machines.mp457.36MB

09 Deep Learning for Recommender Systems/081 More Emerging Tech to Watch.mp427.64MB

10 Scaling it Up/082 [Activity] Introduction and Installation of Apache Spark.mp453.32MB

10 Scaling it Up/083 Apache Spark Architecture.mp417.36MB

10 Scaling it Up/084 [Activity] Movie Recommendations with Spark Matrix Factorization and ALS.mp455.61MB

10 Scaling it Up/085 [Activity] Recommendations from 20 million ratings with Spark.mp450.67MB

10 Scaling it Up/086 Amazon DSSTNE.mp442.31MB

10 Scaling it Up/087 DSSTNE in Action.mp4116.66MB

10 Scaling it Up/088 Scaling Up DSSTNE.mp410.44MB

10 Scaling it Up/089 AWS SageMaker and Factorization Machines.mp415.58MB

10 Scaling it Up/090 SageMaker in Action Factorization Machines on one million ratings in the cloud.mp468.34MB

11 Real-World Challenges of Recommender Systems/091 The Cold Start Problem (and solutions).mp427.79MB

11 Real-World Challenges of Recommender Systems/092 [Exercise] Implement Random Exploration.mp42.2MB

11 Real-World Challenges of Recommender Systems/093 Exercise Solution Random Exploration.mp424.17MB

11 Real-World Challenges of Recommender Systems/094 Stoplists.mp419.91MB

11 Real-World Challenges of Recommender Systems/095 [Exercise] Implement a Stoplist.mp41.35MB

11 Real-World Challenges of Recommender Systems/096 Exercise Solution Implement a Stoplist.mp426.71MB

11 Real-World Challenges of Recommender Systems/097 Filter Bubbles Trust and Outliers.mp492.41MB

11 Real-World Challenges of Recommender Systems/098 [Exercise] Identify and Eliminate Outlier Users.mp41.77MB

11 Real-World Challenges of Recommender Systems/099 Exercise Solution Outlier Removal.mp438.49MB

11 Real-World Challenges of Recommender Systems/100 Fraud The Perils of Clickstream and International Concerns.mp458.23MB

11 Real-World Challenges of Recommender Systems/101 Temporal Effects and Value-Aware Recommendations.mp454.02MB

12 Case Studies/102 Case Study YouTube Part 1.mp426.91MB

12 Case Studies/103 Case Study YouTube Part 2.mp426.26MB

12 Case Studies/104 Case Study Netflix Part 1.mp427.55MB

12 Case Studies/105 Case Study Netflix Part 2.mp426.57MB

13 Hybrid Approaches/106 Hybrid Recommenders and Exercise.mp418.4MB

13 Hybrid Approaches/107 Exercise Solution Hybrid Recommenders.mp433.18MB

14 Wrapping Up/108 More to Explore.mp438.93MB

14 Wrapping Up/109 Bonus Lecture Companion Book and More Courses from Sundog Education.mp421.12MB