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  1. Урок 1. 00:01:49
    Machine Learning with Hugging Face Bootcamp: Zero to Mastery
  2. Урок 2. 00:05:03
    Overview
  3. Урок 3. 00:05:44
    Introduction to Text Classification
  4. Урок 4. 00:07:22
    What We're Going To Build!
  5. Урок 5. 00:05:53
    Getting Setup: Adding Hugging Face Tokens to Google Colab
  6. Урок 6. 00:09:36
    Getting Setup: Importing Necessary Libraries to Google Colab
  7. Урок 7. 00:16:01
    Downloading a Text Classification Dataset from Hugging Face Datasets
  8. Урок 8. 00:12:49
    Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers
  9. Урок 9. 00:06:19
    Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets
  10. Урок 10. 00:12:54
    Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer
  11. Урок 11. 00:10:27
    Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer
  12. Урок 12. 00:17:58
    Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data
  13. Урок 13. 00:08:54
    Setting Up an Evaluation Metric (to measure how well our model performs)
  14. Урок 14. 00:07:11
    Introduction to Transfer Learning (a powerful technique to get good results quickly)
  15. Урок 15. 00:12:20
    Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub
  16. Урок 16. 00:12:27
    Model Training - Part 2: Counting the Parameters in Our Model
  17. Урок 17. 00:03:54
    Model Training - Part 3: Creating a Folder to Save Our Model
  18. Урок 18. 00:15:00
    Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments
  19. Урок 19. 00:05:06
    Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers
  20. Урок 20. 00:13:35
    Model Training - Part 6: Training Our Model and Fixing Errors Along the Way
  21. Урок 21. 00:14:40
    Model Training - Part 7: Inspecting Our Models Loss Curves
  22. Урок 22. 00:08:02
    Model Training - Part 8: Uploading Our Model to the Hugging Face Hub
  23. Урок 23. 00:05:59
    Making Predictions on the Test Data with Our Trained Model
  24. Урок 24. 00:12:49
    Turning Our Predictions into Prediction Probabilities with PyTorch
  25. Урок 25. 00:05:11
    Sorting Our Model's Predictions by Their Probability
  26. Урок 26. 00:09:41
    Performing Inference - Part 1: Discussing Our Options
  27. Урок 27. 00:10:02
    Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time)
  28. Урок 28. 00:06:39
    Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching)
  29. Урок 29. 00:10:34
    Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions
  30. Урок 30. 00:12:07
    Performing Inference - Part 5: Performing Inference with PyTorch
  31. Урок 31. 00:34:29
    OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data
  32. Урок 32. 00:03:48
    Turning Our Model into a Demo - Part 1: Gradio Overview
  33. Урок 33. 00:07:08
    Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs
  34. Урок 34. 00:06:47
    Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally
  35. Урок 35. 00:08:02
    Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory
  36. Урок 36. 00:12:15
    Making Our Demo Publicly Accessible - Part 2: Creating an App File
  37. Урок 37. 00:07:08
    Making Our Demo Publicly Accessible - Part 3: Creating a README File
  38. Урок 38. 00:03:34
    Making Our Demo Publicly Accessible - Part 4: Making a Requirements File
  39. Урок 39. 00:18:44
    Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available
  40. Урок 40. 00:05:56
    Summary Exercises and Extensions
  41. Урок 41. 00:10:04
    Introduction
  42. Урок 42. 00:05:52
    Setting Up Google Colab with Hugging Face Tokens
  43. Урок 43. 00:03:44
    Installing Necessary Dependencies
  44. Урок 44. 00:07:38
    Getting an Object Detection Dataset
  45. Урок 45. 00:06:24
    Inspecting the Features of Our Dataset
  46. Урок 46. 00:09:36
    Creating a Colour Palette to Visualize Our Classes
  47. Урок 47. 00:04:25
    Creating a Helper Function to Halve Our Image Sizes
  48. Урок 48. 00:06:02
    Creating a Helper Function to Halve Our Box Sizes
  49. Урок 49. 00:04:33
    Testing our Helper Functions
  50. Урок 50. 00:06:27
    Outlining the Steps to Draw Boxes on an Image
  51. Урок 51. 00:19:05
    Plotting Bounding Boxes on a Single Image Step by Step
  52. Урок 52. 00:08:18
    Different Bounding Box Formats
  53. Урок 53. 00:06:16
    Getting an Object Detection Model
  54. Урок 54. 00:06:09
    Transfer Learning Overview
  55. Урок 55. 00:09:27
    Downloading our Model from the Hugging Face Hub and Trying it Out
  56. Урок 56. 00:06:54
    Inspecting the Layers of Our Model
  57. Урок 57. 00:10:55
    Counting the Number of Parameters in Our Model
  58. Урок 58. 00:13:16
    Creating a Function to Build Our Custom Model
  59. Урок 59. 00:15:47
    Passing a Single Image Sample Through Our Model - Part 1
  60. Урок 60. 00:08:46
    OPTIONAL: Data Preprocessor Model Workflow
  61. Урок 61. 00:20:11
    Loading Our Models Image Preprocessor and Customizing it for Our Use Case
  62. Урок 62. 00:02:57
    Exercise: Imposter Syndrome
  63. Урок 63. 00:06:18
    Discussing the Format Our Model Expects Our Annotations In (COCO)
  64. Урок 64. 00:09:55
    Creating Dataclasses to Hold the COCO Format
  65. Урок 65. 00:12:06
    Creating a Function to Turn Our Annotations into COCO Format
  66. Урок 66. 00:07:27
    Preprocessing a Single Image Sample and COCO Formatted Annotations
  67. Урок 67. 00:12:03
    Post Processing a Single Output
  68. Урок 68. 00:12:45
    Plotting a Single Post Processed Sample onto an Image
  69. Урок 69. 00:10:45
    OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 1: Overview
  70. Урок 70. 00:28:33
    OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 2: Replicating Scores by Hand
  71. Урок 71. 00:12:33
    OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 3: Replicating Labels by Hand
  72. Урок 72. 00:10:24
    OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 4: Replicating Boxes by Hand Overview
  73. Урок 73. 00:17:41
    OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 5: Replicating Boxes by Hand Implementation
  74. Урок 74. 00:06:44
    OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 6: Plotting Our Manual Post Processed Outputs on an Image
  75. Урок 75. 00:09:22
    Preparing Our Data at Scale - Part 1: Concept Overview
  76. Урок 76. 00:12:14
    Preparing Our Data at Scale - Part 2: Creating Train Validation and Test Splits
  77. Урок 77. 00:08:17
    Preparing Our Data at Scale - Part 3: Preprocessing Multiple Samples at a Time Overview
  78. Урок 78. 00:21:38
    Preparing our Data at Scale - Part 4: Making a Function to Preprocess Multiple Samples at a Time
  79. Урок 79. 00:09:38
    Preparing our Data at Scale - Part 5: Applying Our Preprocessing Function to Our Datasets
  80. Урок 80. 00:12:20
    Preparing Our Data at Scale - Part 6: Creating a Data Collation Function
  81. Урок 81. 00:07:43
    Training a Custom Model - Part 1: Overview
  82. Урок 82. 00:04:12
    Training a Custom Model - Part 2: Creating a Model and Folder to Save Our Model to
  83. Урок 83. 00:12:54
    Training a Custom Model - Part 3: Creating TrainingArguments for Our Model Overview
  84. Урок 84. 00:11:12
    Training a Custom Model - Part 4: Creating our First TrainingArguments
  85. Урок 85. 00:12:40
    Training a Custom Model - Part 5: Finishing Off the TrainingArguments
  86. Урок 86. 00:16:06
    Training a Custom Model - Part 6: OPTIONAL - Creating a Custom Optimizer for Different Learning Rates
  87. Урок 87. 00:13:09
    Training a Custom Model - Part 7: Creating an Evaluation Function for Our Model Overview
  88. Урок 88. 00:22:50
    Training a Custom Model - Part 8: Creating an Evaluation Function for Our Model Targets Processing
  89. Урок 89. 00:13:53
    Training a Custom Model - Part 9: Creating an Evaluation Function for Our Model Predictions Processing
  90. Урок 90. 00:12:54
    Training a Custom Model - Part 10: Training Our Model with Trainer
  91. Урок 91. 00:08:36
    Training a Custom Model - Part 11: Plotting Our Models Loss Curves
  92. Урок 92. 00:11:14
    Evaluating Our Model on the Test Dataset
  93. Урок 93. 00:24:21
    Making Predictions on Test Data and Visualizing Them
  94. Урок 94. 00:12:01
    Plotting Our Models Predictions vs. the Ground Truth Images
  95. Урок 95. 00:09:50
    Trying Our Model on Images from the Wild
  96. Урок 96. 00:10:47
    Uploading Our Trained Model to the Hugging Face Hub
  97. Урок 97. 00:10:11
    Turning Our Model into a Demo - Part 1: Gradio and Hugging Face Spaces Overview
  98. Урок 98. 00:07:10
    Turning Our Model into a Demo - Part 2: Creating an App File Overview
  99. Урок 99. 00:27:33
    Turning Our Model into a Demo - Part 3: Building the Main Function of Our App File
  100. Урок 100. 00:09:57
    Turning Our Model into a Demo - Part 4: Finishing Off Our App File and Testing Our Demo
  101. Урок 101. 00:03:32
    Turning Our Model into a Demo - Part 5: Creating a Readme and Requirements File
  102. Урок 102. 00:08:20
    Turning Our Model into a Demo - Part 6: Getting Example Images for Our Demo
  103. Урок 103. 00:17:19
    Turning Our Model into a Demo - Part 7: Uploading Our Demo to the Hugging Face Hub
  104. Урок 104. 00:03:45
    Turning Our Model into a Demo - Part 8: Embedding Our Demo into Our Notebook
  105. Урок 105. 00:06:16
    Summary, Extensions and Extra-Curriculum
  106. Урок 106. 00:01:18
    Thank You!