OR72 | EBA7388B56262A397F36E239B5E8D9B68E21A54B

OR72

Name	OR72	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	3.4 GB
Total Files	107
Last Seen
Hash	EBA7388B56262A397F36E239B5E8D9B68E21A54B

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001 - Install Anaconda, course materials, and create movie recommendations!.mp4	92.4 MB

002 - Course Roadmap.mp4	72.6 MB
003 - Types of Recommenders.mp4	15.6 MB
004 - Understanding You through Implicit and Explicit Ratings.mp4	9.9 MB
005 - Top-N Recommender Architecture.mp4	17.3 MB
006 - Review the basics of recommender systems..mp4	12.4 MB
007 - The Basics of Python.mp4	44.0 MB
008 - Data Structures in Python.mp4	13.8 MB
009 - Functions in Python.mp4	7.0 MB
010 - Booleans, loops, and a hands-on challenge.mp4	7.7 MB
011 - Train_Test and Cross Validation.mp4	24.3 MB
012 - Accuracy Metrics (RMSE, MAE).mp4	49.0 MB
013 - Top-N Hit Rate - Many Ways.mp4	12.7 MB
014 - Coverage, Diversity, and Novelty.mp4	8.3 MB
015 - Churn, Responsiveness, and A_B Tests.mp4	86.7 MB
016 - Review ways to measure your recommender..mp4	10.5 MB
017 - Walkthrough of RecommenderMetrics.py.mp4	46.6 MB
018 - Walkthrough of TestMetrics.py.mp4	32.7 MB
019 - Measure the Performance of SVD Recommendations.mp4	14.6 MB
020 - Our Recommender Engine Architecture.mp4	20.0 MB
021 - Recommender Engine Walkthrough, Part 1.mp4	24.3 MB
022 - Recommender Engine Walkthrough, Part 2.mp4	23.1 MB
023 - Review the Results of our Algorithm Evaluation..mp4	17.8 MB
024 - Content-Based Recommendations, and the Cosine Similarity Metric.mp4	40.3 MB
025 - K-Nearest-Neighbors and Content Recs.mp4	15.6 MB
026 - Producing and Evaluating Content-Based Movie Recommendations.mp4	34.0 MB
027 - Bleeding Edge Alert! Mise en Scene Recommendations.mp4	35.5 MB
028 - Dive Deeper into Content-Based Recommendations.mp4	13.2 MB
029 - Measuring Similarity, and Sparsity.mp4	73.1 MB
030 - Similarity Metrics.mp4	16.3 MB
031 - User-based Collaborative Filtering.mp4	24.2 MB
032 - User-based Collaborative Filtering, Hands-On.mp4	32.0 MB
033 - Item-based Collaborative Filtering.mp4	64.6 MB
034 - Item-based Collaborative Filtering, Hands-On.mp4	19.8 MB
035 - Tuning Collaborative Filtering Algorithms.mp4	12.2 MB
036 - Evaluating Collaborative Filtering Systems Offline.mp4	12.9 MB
037 - Measure the Hit Rate of Item-Based Collaborative Filtering.mp4	5.1 MB
038 - KNN Recommenders.mp4	22.9 MB
039 - Running User and Item-Based KNN on MovieLens.mp4	20.6 MB
040 - Experiment with different KNN parameters..mp4	40.7 MB
041 - Bleeding Edge Alert! Translation-Based Recommendations.mp4	20.6 MB
042 - Principal Component Analysis (PCA).mp4	68.1 MB
043 - Singular Value Decomposition.mp4	13.7 MB
044 - Running SVD and SVD++ on MovieLens.mp4	28.7 MB
045 - Improving on SVD.mp4	12.0 MB
046 - Tune the hyperparameters on SVD.mp4	10.8 MB
047 - Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4	22.1 MB
048 - Deep Learning Introduction.mp4	23.9 MB
049 - Deep Learning Pre-Requisites.mp4	20.1 MB
050 - History of Artificial Neural Networks.mp4	49.1 MB
051 - [Activity] Playing with Tensorflow.mp4	122.6 MB
052 - Training Neural Networks.mp4	23.5 MB
053 - Tuning Neural Networks.mp4	17.0 MB
054 - Introduction to Tensorflow.mp4	54.0 MB
055 - [Activity] Handwriting Recognition with Tensorflow, part 1.mp4	112.8 MB
056 - [Activity] Handwriting Recognition with Tensorflow, part 2.mp4	31.7 MB
057 - Introduction to Keras.mp4	8.2 MB
058 - [Activity] Handwriting Recognition with Keras.mp4	56.8 MB
059 - Classifier Patterns with Keras.mp4	17.2 MB
060 - [Exercise] Predict Political Parties of Politicians with Keras.mp4	66.7 MB
061 - Intro to Convolutional Neural Networks (CNN's).mp4	46.3 MB
062 - CNN Architectures.mp4	11.0 MB
063 - [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4	54.3 MB
064 - Intro to Recurrent Neural Networks (RNN's).mp4	26.5 MB
065 - Training Recurrent Neural Networks.mp4	13.4 MB
066 - [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.mp4	92.8 MB
067 - Intro to Deep Learning for Recommenders.mp4	58.7 MB
068 - Restricted Boltzmann Machines (RBM's).mp4	17.1 MB
069 - [Activity] Recommendations with RBM's, part 1.mp4	60.9 MB
070 - [Activity] Recommendations with RBM's, part 2.mp4	33.2 MB
071 - [Activity] Evaluating the RBM Recommender.mp4	24.4 MB
072 - [Exercise] Tuning Restricted Boltzmann Machines.mp4	56.3 MB
073 - Exercise Results - Tuning a RBM Recommender.mp4	7.4 MB
074 - Auto-Encoders for Recommendations - Deep Learning for Recs.mp4	14.7 MB
075 - [Activity] Recommendations with Deep Neural Networks.mp4	45.6 MB
076 - Clickstream Recommendations with RNN's.mp4	31.0 MB
077 - [Exercise] Get GRU4Rec Working on your Desktop.mp4	4.6 MB
078 - Exercise Results - GRU4Rec in Action.mp4	45.5 MB
079 - Bleeding Edge Alert! Deep Factorization Machines.mp4	46.5 MB
080 - More Emerging Tech to Watch.mp4	17.6 MB
081 - [Activity] Introduction and Installation of Apache Spark.mp4	42.0 MB
082 - Apache Spark Architecture.mp4	9.9 MB
083 - [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4	29.9 MB
084 - [Activity] Recommendations from 20 million ratings with Spark.mp4	34.2 MB
085 - Amazon DSSTNE.mp4	43.4 MB
086 - DSSTNE in Action.mp4	73.8 MB
087 - Scaling Up DSSTNE.mp4	5.5 MB
088 - AWS SageMaker and Factorization Machines.mp4	8.8 MB
089 - SageMaker in Action - Factorization Machines on one million ratings, in the cloud.mp4	51.0 MB
090 - The Cold Start Problem (and solutions).mp4	13.3 MB
091 - [Exercise] Implement Random Exploration.mp4	1.3 MB
092 - Exercise Solution - Random Exploration.mp4	20.4 MB
093 - Stoplists.mp4	10.8 MB
094 - [Exercise] Implement a Stoplist.mp4	768.4 KB
095 - Exercise Solution - Implement a Stoplist.mp4	18.5 MB
096 - Filter Bubbles, Trust, and Outliers.mp4	26.6 MB
097 - [Exercise] Identify and Eliminate Outlier Users.mp4	1.0 MB
098 - Exercise Solution - Outlier Removal.mp4	20.8 MB
099 - Fraud, the Perils of Clickstream, and International Concerns.mp4	76.3 MB
100 - Temporal Effects, and Value-Aware Recommendations.mp4	85.6 MB
101 - Case Study - YouTube, Part 1.mp4	16.5 MB
102 - Case Study - YouTube, Part 2.mp4	14.4 MB
103 - Case Study - Netflix, Part 1.mp4	15.0 MB
104 - Case Study - Netflix, Part 2.mp4	11.8 MB
105 - Hybrid Recommenders and Exercise.mp4	8.5 MB
106 - Exercise Solution - Hybrid Recommenders.mp4	23.6 MB
107 - More to Explore.mp4	64.9 MB
Total files 107