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Deep Learning Recommendation Algorithms with Python

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Name:Deep Learning Recommendation Algorithms with Python

Infohash: 0C6313645BACBEBDCBC5B6B44614CCC832ACABCE

Total Size: 4.22 GB

Seeds: 1

Leechers: 2

Stream: Watch Full Movies @ LimeMovies

Last Updated: 2025-10-25 07:54:45 (Update Now)

Torrent added: 2022-09-18 04:30:06






Torrent Files List


[TutsNode.net] - Deep Learning Recommendation Algorithms with Python (Size: 4.22 GB) (Files: 168)

 [TutsNode.net] - Deep Learning Recommendation Algorithms with Python

  3 - 00c Introduction to Python Prerequisite

   21 - 14 Classes And Objects.mp4

223.26 MB

   25 - Source Code.html

0.03 KB

   18 - 11 Loops.mp4

167.03 MB

   11 - 03 Operators.mp4

161.46 MB

   22 - 15 Inheritance Examples.mp4

130.64 MB

   17 - 10 If Statement Examples.mp4

118.03 MB

   13 - 05 List Examples.mp4

109.75 MB

   9 - 01 Variables.mp4

106.45 MB

   15 - 07 Dictionaries Examples.mp4

87.28 MB

   19 - 12 Functions.mp4

86.44 MB

   23 - 16 Static Members Examples.mp4

78.61 MB

   20 - 13 Parameters And Return Values Examples.mp4

78.52 MB

   10 - 02 Type Conversion Examples.mp4

59.84 MB

   8 - 00 Intro To Course And Python.mp4

57.22 MB

   14 - 06 Tuples Examples.mp4

52.40 MB

   12 - 04 Collections.mp4

41.63 MB

   16 - 09 Conditionals.mp4

38.05 MB

   24 - 17 Summary And Outro.mp4

20.84 MB

  15 - 12 Build a Neural Network to Predict Ratings

   72 - SourceFiles.zip

683.92 KB

   72 - Source File.html

0.03 KB

   70 - 04 Build A Neural Network.mp4

95.59 MB

   71 - 05 Train The Neural Network.mp4

87.43 MB

  1 - 00a Introduction to Recommender Systems

   5 - Source Files.html

0.03 KB

   2 - 02 How To Evaluate Recommender Systems.mp4

51.03 MB

   1 - 01 Introduction To Recommender Systems.mp4

33.42 MB

   3 - 03 Content Based Recommendations.mp4

17.88 MB

   4 - 04 Neighborhood Based Collaborative Filtering.mp4

14.34 MB

   5 - SourceFiles.zip

4.60 MB

  4 - 01 Build a Basic Movie Recommender System

   30 - Source Files.html

0.03 KB

   26 - 01 Load Data As Pandas Dataframes.mp4

95.71 MB

   27 - 02 Merge Movies And Ratings Dataframes.mp4

58.60 MB

   29 - 04 Test The Recommender.mp4

49.61 MB

   28 - 03 Build A Correlation Matrix.mp4

45.47 MB

   30 - SourceFiles.zip

1.65 MB

  7 - 04 Introduction to User Similarity

   39 - Source Files.html

0.03 KB

   36 - 02 Find A Recommendation Based On Different Movie Features.mp4

104.47 MB

   35 - 01 Load Data Into Dataframes.mp4

49.77 MB

   38 - 04 Find Similar Users With Euclidean Distance.mp4

47.44 MB

   37 - 03 Calculate Distance Between Users.mp4

34.97 MB

   39 - SourceFiles.zip

1.66 MB

  8 - 05 Recommend a Movie Based on User Similarity

   43 - Source Files.html

0.03 KB

   43 - SourceFiles.zip

851.30 KB

   41 - 06 Find Top Similar Users.mp4

47.17 MB

   42 - 07 Recommend A Movie Based On User Similarity.mp4

42.77 MB

   40 - 05 Define Similarity Between Users.mp4

37.19 MB

  9 - 06 Recommend a Movie with a K Nearest Neighbors Classifier

   48 - Source Files.html

0.03 KB

   48 - SourceFiles.zip

852.74 KB

   45 - 08B Recommend A Movie With A K Nearest Neighbors Classifier.mp4

70.88 MB

   46 - 09 Create A Sample User For Testing.mp4

55.09 MB

   44 - 08A What Is K Nearest Neighbours.mp4

37.61 MB

   47 - 10 Recommend Movies To Sample User.mp4

20.54 MB

  11 - 08 Data Processing Profiles and Items

   53 - Source Files.html

0.03 KB

   53 - SourceFiles.zip

889.97 KB

   50 - 01 Load Data For Machine Learning.mp4

105.86 MB

   51 - 02 Process Data For Machine Learning.mp4

77.91 MB

   52 - 03 Build Categories.mp4

55.79 MB

  12 - 09 Build Models for User Recommendations

   62 - Source Files.html

0.03 KB

   55 - 04B What Is Regression.mp4

85.08 MB

   56 - 04C Build A Ridge Regression Model.mp4

72.64 MB

   58 - 06 Visualize Top Features Affecting Rating.mp4

58.42 MB

   59 - 07 Build A Lasso Regression Model.mp4

55.79 MB

   57 - 05 Evaluate Model Error.mp4

48.88 MB

   60 - 08 Visualize Top Features From Lasso Regression.mp4

42.21 MB

   54 - 04A Regression Introduction.mp4

35.86 MB

   61 - 09 Determine Which Model Is Best.mp4

17.02 MB

   62 - SourceFiles.zip

1.84 MB

  13 - 10 Build a Model to Predict Ratings

   66 - Source Files.html

0.03 KB

   64 - 02 Build A Singular Value Decomposition Algorithm.mp4

73.02 MB

   63 - 01 Load Data For A Neural Network.mp4

62.97 MB

   65 - 03 Calculate Model Error.mp4

62.54 MB

   66 - SourceFIles.zip

1.33 MB

  16 - 13 Data Analysis with Pandas Numpy and Scikit Learn

   81 - Source Files.html

0.03 KB

   80 - 07 Build An Average Ratings Column.mp4

91.39 MB

   79 - 06 Find Rating Averages For Every Movie In The Slice.mp4

54.13 MB

   74 - 01 Load Data Into Dataframes.mp4

44.69 MB

   77 - 04 Calculate Average Rating Of A Movie.mp4

41.88 MB

   78 - 05 Find Ratings For A Movie In Every Slice.mp4

40.46 MB

   76 - 03 Build A Rating Pivot Table.mp4

30.85 MB

   75 - 02 Explore Data In Our Dataset.mp4

20.12 MB

   73 - 00 Project Preview.mp4

14.03 MB

   81 - SourceFiles.zip

1.65 MB

  2 - 00b Mammoth Interactive Courses Introduction

   7 - 01 How To Learn Online Effectively.mp4

74.00 MB

   6 - 00 About Mammoth Interactive.mp4

8.64 MB

  6 - 03 Machine Learning Fundamentals

   34 - 00C What Is Supervised Learning.mp4

59.03 MB

   33 - 00B Types Of Machine Learning Models.mp4

52.53 MB

   32 - 00A What Is Machine Learning.mp4

27.70 MB

  14 - 11 Deep Learning Fundamentals

   69 - 03 What Is Unsupervised Learning.mp4

40.80 MB

   68 - 02 What Is A Neural Network.mp4

39.61 MB

   67 - 01 What Is Deep Learning.mp4

34.88 MB

  5 - 02 Projects 2 and 3 Preview Machine Learning Movie Recommender

   31 - 00 Project Preview.mp4

19.30 MB

  10 - 07 Project 4 Preview Complex Machine Learning Recommender

   49 - 00 Project Preview.mp4

14.23 MB

 TutsNode.net.txt

0.06 KB

 [TGx]Downloaded from torrentgalaxy.to .txt

0.57 KB

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