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[Manning] Machine learning bookcamp (hevc) (2021) [EN]

  • 收录时间:2022-06-02 16:37:22
  • 文件大小:296MB
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  • 最近下载:2022-06-02 16:37:22
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文件列表

  1. Manning.Machine.learning.bookcamp.2021.pdf 10MB
  2. 25 - Ch4 ROC curve and AUC score.m4v 7MB
  3. 51 - Ch8 Preparing the Docker image.m4v 7MB
  4. 10 - Ch2 Validating the model.m4v 7MB
  5. 1 - Ch1 Introduction to machine learning.m4v 7MB
  6. 50 - Ch8 Serverless deep learning.m4v 7MB
  7. 53 - Ch9 Running TensorFlow Serving locally.m4v 7MB
  8. 2 - Ch1 When machine learning isn’t helpful.m4v 6MB
  9. 20 - Ch3 Model interpretation.m4v 6MB
  10. 54 - Ch9 Model deployment with Kubernetes.m4v 6MB
  11. 13 - Ch3 Machine learning for classification.m4v 6MB
  12. 40 - Ch6 Parameter tuning for XGBoost.m4v 6MB
  13. 21 - Ch3 Using the model.m4v 6MB
  14. 3 - Ch1 Evaluation.m4v 6MB
  15. 26 - Ch4 ROC Curve.m4v 6MB
  16. 42 - Ch7 Neural networks and deep learning.m4v 6MB
  17. 23 - Ch4 Confusion table.m4v 6MB
  18. 49 - Ch7 Using the model.m4v 6MB
  19. 28 - Ch4 Next steps.m4v 6MB
  20. 22 - Ch4 Evaluation metrics for classification.m4v 6MB
  21. 52 - Ch9 Serving models with Kubernetes and Kubeflow.m4v 6MB
  22. 55 - Ch9 Deploying to Kubernetes.m4v 6MB
  23. 15 - Ch3 Feature importance, Part 1.m4v 5MB
  24. 35 - Ch6 Data cleaning.m4v 5MB
  25. 30 - Ch5 Model serving.m4v 5MB
  26. 47 - Ch7 Saving the model and checkpointing.m4v 5MB
  27. 36 - Ch6 Decision trees.m4v 5MB
  28. 7 - Ch2 Machine learning for regression - again.m4v 5MB
  29. 14 - Ch3 Initial data preparation.m4v 5MB
  30. 9 - Ch2 Predicting the price.m4v 5MB
  31. 19 - Ch3 Training logistic regression.m4v 5MB
  32. 33 - Ch5 Deployment.m4v 5MB
  33. 6 - Ch2 Target variable analysis.m4v 5MB
  34. 8 - Ch2 Linear regression.m4v 5MB
  35. 57 - Ch9 KFServing transformers.m4v 5MB
  36. 48 - Ch7 Data augmentation.m4v 5MB
  37. 37 - Ch6 Decision tree learning algorithm.m4v 5MB
  38. 46 - Ch7 Training the model - again.m4v 5MB
  39. 29 - Ch 5 Deploying machine learning models.m4v 5MB
  40. 5 - Ch2 Exploratory data analysis.m4v 5MB
  41. 38 - Ch6 Random forest.m4v 4MB
  42. 17 - Ch3 Feature engineering.m4v 4MB
  43. 31 - Ch5 Managing dependencies.m4v 4MB
  44. 11 - Ch2 Regularization.m4v 4MB
  45. 16 - Ch3 Feature importance, Part 2.m4v 4MB
  46. 41 - Ch6 Next steps.m4v 4MB
  47. 4 - Ch2 Machine learning for regression.m4v 4MB
  48. 56 - Ch9 Model deployment with Kubeflow.m4v 4MB
  49. 45 - Ch7 Training the model.m4v 4MB
  50. 12 - Ch2 Using the model.m4v 4MB
  51. 32 - Ch5 Docker.m4v 4MB
  52. 27 - Ch4 Parameter tuning.m4v 4MB
  53. 39 - Ch6 Gradient boosting.m4v 4MB
  54. 44 - Ch7 Internals of the model.m4v 3MB
  55. 18 - Ch3 Machine learning for classification.m4v 3MB
  56. 43 - Ch7 Convolutional neural networks.m4v 3MB
  57. 24 - Ch4 Precision and recall.m4v 3MB
  58. 34 - Ch6 Decision trees and ensemble learning.m4v 3MB