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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
615.57KB |
| 1 |
229.48KB |
| 10 |
796.61KB |
| 10 - Create Ratings Model with Generic Foreign Keys.mp4 |
101.82MB |
| 11 |
329.96KB |
| 11 - Calculate Average Ratings.mp4 |
119.37MB |
| 12 |
672.80KB |
| 12 - Generate Movie Ratings.mp4 |
127.43MB |
| 13 |
907.53KB |
| 13 - Handling Duplicate Ratings with Signals.mp4 |
129.12MB |
| 14 |
343.55KB |
| 14 - Calculate Movie Average Rating Task.mp4 |
122.82MB |
| 15 |
923.52KB |
| 15 - Setup Celery for Offloading Tasks.mp4 |
126.58MB |
| 16 |
183.60KB |
| 16 - Converting Functions into Celery Tasks.mp4 |
166.22MB |
| 17 |
337.31KB |
| 17 - Movie List Detail View URLs and Templates.mp4 |
161.68MB |
| 18 |
401.27KB |
| 18 - Django AllAuth.mp4 |
92.65MB |
| 19 |
899.98KB |
| 19 - Update the Movie Ratings Task.mp4 |
175.60MB |
| 1 - Welcome to Recommender.mp4 |
53.62MB |
| 2 |
578.68KB |
| 20 |
933.28KB |
| 20 - Rendering Rating Choices.mp4 |
71.26MB |
| 21 |
588.43KB |
| 21 - Display a Users Ratings.mp4 |
179.79MB |
| 22 |
120.53KB |
| 22 - Dynamic Requests with HTMX.mp4 |
155.66MB |
| 23 |
425.34KB |
| 23 - Rate Movies Dynamically with HTMX.mp4 |
160.34MB |
| 24 |
187.23KB |
| 24 - Infinite Rating Flow with Django HTMX.mp4 |
126.88MB |
| 25 |
303.79KB |
| 25 - Rating Dataset Exports Model Task.mp4 |
250.78MB |
| 26 |
38.81KB |
| 26 - Using Jupyter with Django.mp4 |
68.46MB |
| 27 |
643.70KB |
| 27 - Load Real Ratings to Fake Users.mp4 |
140.67MB |
| 28 |
75.83KB |
| 28 - Update Movie Data.mp4 |
218.38MB |
| 29 |
189.38KB |
| 29 - Recommendations by Popularity.mp4 |
203.08MB |
| 2 - Celery with Django Blog Post.txt |
64B |
| 2 - Course Code on Github.txt |
53B |
| 2 - justinmitchel on Twitter.txt |
33B |
| 2 - Live demo limited features.txt |
32B |
| 2 - Public Discussion Forum.txt |
65B |
| 2 - Requirements InDepth Walkthrough.mp4 |
194.80MB |
| 2 - YouTube Channel.txt |
43B |
| 3 |
632.04KB |
| 30 |
3.06KB |
| 30 - What is Collaborative Filtering.mp4 |
134.61MB |
| 31 |
356.98KB |
| 31 - Collaborative Filtering with Surprise ML.mp4 |
67.35MB |
| 32 |
785.66KB |
| 32 - Surprise ML Utils Celery Task For Surprise Model Training.mp4 |
266.40MB |
| 33 |
756.47KB |
| 33 - Batch User Prediction Task.mp4 |
141.82MB |
| 34 |
84.51KB |
| 34 - Storing Predictions in our Suggestion Model.mp4 |
156.11MB |
| 35 |
550.67KB |
| 35 - Updating Batch Predictions Based on Previous Suggestions.mp4 |
146.10MB |
| 36 |
663.49KB |
| 36 - MLBased Movies Recommendations View.mp4 |
178.58MB |
| 37 |
950.31KB |
| 37 - Trigger ML Predictions Per User Activity.mp4 |
104.93MB |
| 38 |
386.95KB |
| 38 - Position Ranking for Movie Querysets.mp4 |
86.23MB |
| 39 |
599.83KB |
| 39 - Movie Embedding Idx Field and Task.mp4 |
129.09MB |
| 3 - Where to get help.mp4 |
28.39MB |
| 4 |
39.98KB |
| 40 |
448.00KB |
| 40 - Movie Dataset Exports.mp4 |
210.96MB |
| 41 |
625.24KB |
| 41 - Schedule for ML Training ML Inference Movie IDX Updates and Exports.mp4 |
121.70MB |
| 42 - Overview of a Neural Network Colab Filtering Model.mp4 |
235.43MB |
| 43 - Thank you and next steps.mp4 |
44.56MB |
| 4 - Setup Project.mp4 |
69.92MB |
| 5 |
946.22KB |
| 5 - Django as a ML Pipeline Orchestration Tool.mp4 |
11.12MB |
| 6 |
201.45KB |
| 6 - Generate Fake User Data.mp4 |
50.41MB |
| 7 |
211.61KB |
| 7 - Django Management Command to add Fake User Data.mp4 |
95.00MB |
| 8 |
429.97KB |
| 8 - Our Collaborative Filtering Dataset.mp4 |
56.07MB |
| 9 |
411.92KB |
| 9 - Load The Movies Dataset into the Movie Django Model.mp4 |
120.96MB |
| TutsNode.net.txt |
63B |