Linkedin Full Stack Deep Learning with Python FreeCourseWeb

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FreeCourseWeb
Source
T Torrent Galaxy
Downloads
16
Language
English
Category
Other
Size
431.1 MB
Added
2024-02-15 16:23 (2 years, 8 months ago)
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2 seeders, 0 leechers (checked 3 days, 21 hours ago)
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Files
  1. Get Bonus Downloads Here.url183 bytes
  2. 01 - Full-stack deep learning, MLOps, and MLflow.mp49.8 MB
  3. 01 - Full-stack deep learning, MLOps, and MLflow.srt12.5 KB
  4. 02 - Prerequisites.mp4898.8 KB
  5. 02 - Prerequisites.srt1.1 KB
  6. 01 - Introducing full-stack deep learning.mp47.8 MB
  7. 01 - Introducing full-stack deep learning.srt11.4 KB
  8. 02 - Introducing MLOps.mp46.6 MB
  9. 02 - Introducing MLOps.srt7.6 KB
  10. 03 - Introducing MLflow.mp46.3 MB
  11. 03 - Introducing MLflow.srt7.9 KB
  12. 04 - Setting up the environment on Google Colab.mp413.0 MB
  13. 04 - Setting up the environment on Google Colab.srt9.2 KB
  14. 05 - Running MLflow and using ngrok to access the MLflow UI.mp410.3 MB
  15. 05 - Running MLflow and using ngrok to access the MLflow UI.srt9.7 KB
  16. 01 - Loading and exploring the EMNIST dataset.mp49.9 MB
  17. 01 - Loading and exploring the EMNIST dataset.srt8.8 KB
  18. 02 - Logging metrics, parameters, and artifacts in MLflow.mp411.0 MB
  19. 02 - Logging metrics, parameters, and artifacts in MLflow.srt11.0 KB
  20. 03 - Set up the dataset and data loader.mp46.9 MB
  21. 03 - Set up the dataset and data loader.srt6.4 KB
  22. 04 - Configuring the image classification DNN model.mp410.5 MB
  23. 04 - Configuring the image classification DNN model.srt8.7 KB
  24. 05 - Training a model within an MLflow run.mp411.1 MB
  25. 05 - Training a model within an MLflow run.srt7.0 KB
  26. 06 - Exploring parameters and metrics in MLflow.mp49.0 MB
  27. 06 - Exploring parameters and metrics in MLflow.srt7.9 KB
  28. 07 - Making predictions using MLflow artifacts.mp411.4 MB
  29. 07 - Making predictions using MLflow artifacts.srt8.8 KB
  30. 01 - Preparing data for image classification using CNN.mp49.7 MB
  31. 01 - Preparing data for image classification using CNN.srt6.9 KB
  32. 02 - Configuring and training the model using MLflow runs.mp415.5 MB
  33. 02 - Configuring and training the model using MLflow runs.srt10.9 KB
  34. 03 - Visualizing charts, metrics, and parameters on MLflow.mp415.2 MB
  35. 03 - Visualizing charts, metrics, and parameters on MLflow.srt12.0 KB
  36. 04 - Setting up the objective function for hyperparameter tuning.mp412.4 MB
  37. 04 - Setting up the objective function for hyperparameter tuning.srt9.8 KB
  38. 05 - Hyperparameter optimization with Hyperopt and MLflow.mp413.9 MB
  39. 05 - Hyperparameter optimization with Hyperopt and MLflow.srt11.7 KB
  40. 06 - Identifying the best model.mp47.8 MB
  41. 06 - Identifying the best model.srt6.0 KB
  42. 07 - Registering a model with the MLflow registry.mp45.7 MB
  43. 07 - Registering a model with the MLflow registry.srt6.0 KB
  44. 01 - Setting up MLflow on the local machine.mp48.2 MB
  45. 01 - Setting up MLflow on the local machine.srt8.4 KB
  46. 02 - Workaround to get model artifacts on the local machine.mp44.3 MB
  47. 02 - Workaround to get model artifacts on the local machine.srt3.9 KB
  48. 03 - Deploying and serving the model locally.mp413.8 MB
  49. 03 - Deploying and serving the model locally.srt10.6 KB
  50. 01 - Summary and next steps.mp42.5 MB
  51. 01 - Summary and next steps.srt3.2 KB
  52. Bonus Resources.txt386 bytes
  53. emnist-letters-test.csv27.3 MB
  54. emnist-letters-train.csv163.7 MB
  55. demo_01_EMNISTClassificationUsingDNN.ipynb1.7 MB
  56. demo_02_EMNISTClassificationUsingCNN.ipynb3.1 MB
  57. demo_03_ModelDeployment.ipynb37.7 KB
  58. demo_01_EMNISTClassificationUsingDNN-checkpoint.ipynb1.7 MB
  59. demo_03_ModelDeployment-checkpoint.ipynb46.3 KB

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