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[] Udemy - Deep Learning using Keras - Complete Compact Dummies Guide

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文件列表

  1. 01 Course Introduction and Table of Contents/001 Course Introduction and Table of Contents.mp4 255MB
  2. 17 Step 2 and 3 EDA and Data Preparation/001 Step 2 and 3 EDA and Data Preparation - Part 1.mp4 150MB
  3. 52 Hyper Parameter Tuning/002 Hyper Parameter Tuning - Part 2.mp4 126MB
  4. 40 CNN Basics/001 CNN Basics.mp4 126MB
  5. 17 Step 2 and 3 EDA and Data Preparation/002 Step 2 and 3 EDA and Data Preparation - Part 2.mp4 120MB
  6. 19 Step 5 and 6 Compile and Fit Model/001 Step 5 and 6 Compile and Fit Model.mp4 110MB
  7. 45 Flowers Classification CNN - Training and Visualization/001 Flowers Classification CNN - Training and Visualization.mp4 107MB
  8. 56 VGG16 Transfer Learning Training Flowers Dataset/002 VGG16 Transfer Learning Training Flowers Dataset - part 2.mp4 106MB
  9. 38 Keras Directory Image Augmentation/001 Keras Directory Image Augmentation.mp4 106MB
  10. 37 Keras Single Image Augmentation/001 Keras Single Image Augmentation - Part 1.mp4 104MB
  11. 30 Step 2 - EDA and Data Visualization/001 Step 2 - EDA and Data Visualization.mp4 101MB
  12. 54 VGG16 and VGG19 prediction/001 VGG16 and VGG19 prediction - Part 1.mp4 101MB
  13. 16 King County House Sales Regression Model - Step 1 Fetch and Load Dataset/001 King County House Sales Regression Model - Step 1 Fetch and Load Dataset.mp4 100MB
  14. 39 Keras Data Frame Augmentation/001 Keras Data Frame Augmentation.mp4 99MB
  15. 52 Hyper Parameter Tuning/001 Hyper Parameter Tuning - Part 1.mp4 98MB
  16. 41 Stride Padding and Flattening Concepts of CNN/001 Stride Padding and Flattening Concepts of CNN.mp4 96MB
  17. 53 Transfer Learning using Pretrained Models - VGG Introduction/001 Transfer Learning using Pretrained Models - VGG Introduction.mp4 96MB
  18. 37 Keras Single Image Augmentation/002 Keras Single Image Augmentation - Part 2.mp4 95MB
  19. 55 ResNet50 Prediction/001 ResNet50 Prediction.mp4 94MB
  20. 42 Flowers CNN Image Classification Model - Fetch Load and Prepare Data/001 Flowers CNN Image Classification Model - Fetch Load and Prepare Data.mp4 92MB
  21. 15 Popular Neural Network Types/001 Popular Neural Network Types.mp4 89MB
  22. 44 Flowers Classification CNN - Defining the Model/002 Flowers Classification CNN - Defining the Model - Part 2.mp4 89MB
  23. 14 Popular Optimizers/001 Popular Optimizers.mp4 88MB
  24. 03 Introduction to Deep learning and Neural Networks/001 Introduction to Deep learning and Neural Networks.mp4 88MB
  25. 13 Popular Types of Loss Functions/001 Popular Types of Loss Functions.mp4 87MB
  26. 23 Step 1 - Fetch and Load Data/001 Step 1 - Fetch and Load Data.mp4 86MB
  27. 04 Setting up Computer - Installing Anaconda/001 Setting up Computer - Installing Anaconda.mp4 86MB
  28. 35 Digital Image Basics/001 Digital Image Basics.mp4 84MB
  29. 20 Step 7 Visualize Training and Metrics/001 Step 7 Visualize Training and Metrics.mp4 84MB
  30. 50 Flowers Classification CNN - Padding and Filter Optimization/001 Flowers Classification CNN - Padding and Filter Optimization.mp4 83MB
  31. 12 Popular Types of Activation Functions/001 Popular Types of Activation Functions.mp4 79MB
  32. 32 Step 4 - Compile Fit and Plot the Model/001 Step 4 - Compile Fit and Plot the Model.mp4 78MB
  33. 56 VGG16 Transfer Learning Training Flowers Dataset/001 VGG16 Transfer Learning Training Flowers Dataset - part 1.mp4 77MB
  34. 24 Step 2 and 3 - EDA and Data Preparation/002 Step 2 and 3 - EDA and Data Preparation - Part 2.mp4 76MB
  35. 26 Step 5 - Compile Fit and Plot the Model/001 Step 5 - Compile Fit and Plot the Model.mp4 74MB
  36. 31 Step 3 - Defining the Model/001 Step 3 - Defining the Model.mp4 73MB
  37. 47 Flowers Classification CNN - Load Saved Model and Predict/001 Flowers Classification CNN - Load Saved Model and Predict.mp4 70MB
  38. 49 Flowers Classification CNN - Dropout Regularization/001 Flowers Classification CNN - Dropout Regularization.mp4 69MB
  39. 24 Step 2 and 3 - EDA and Data Preparation/001 Step 2 and 3 - EDA and Data Preparation - Part 1.mp4 69MB
  40. 36 Basic Image Processing using Keras Functions/002 Basic Image Processing using Keras Functions - Part 2.mp4 65MB
  41. 25 Step 4 - Defining the model/001 Step 4 - Defining the model.mp4 65MB
  42. 18 Step 4 Defining the Keras Model/002 Step 4 Defining the Keras Model - Part 2.mp4 65MB
  43. 43 Flowers Classification CNN - Create Test and Train Folders/001 Flowers Classification CNN - Create Test and Train Folders.mp4 64MB
  44. 05 Python Basics/001 Python Basics - Assignment.mp4 63MB
  45. 10 Basic Structure of Artificial Neuron and Neural Network/001 Basic Structure of Artificial Neuron and Neural Network.mp4 63MB
  46. 36 Basic Image Processing using Keras Functions/001 Basic Image Processing using Keras Functions - Part 1.mp4 63MB
  47. 08 Pandas Basics/001 Pandas Basics - Part 1.mp4 59MB
  48. 51 Flowers Classification CNN - Augmentation Optimization/001 Flowers Classification CNN - Augmentation Optimization.mp4 59MB
  49. 18 Step 4 Defining the Keras Model/001 Step 4 Defining the Keras Model - Part 1.mp4 58MB
  50. 05 Python Basics/005 Python Basics - Dictionary and Functions - part 1.mp4 54MB
  51. 44 Flowers Classification CNN - Defining the Model/001 Flowers Classification CNN - Defining the Model - Part 1.mp4 54MB
  52. 22 Heart Disease Binary Classification Model - Introduction/001 Heart Disease Binary Classification Model - Introduction.mp4 53MB
  53. 09 Installing Deep Learning Libraries/001 Installing Deep Learning Libraries.mp4 53MB
  54. 06 Numpy Basics/002 Numpy Basics - Part 2.mp4 53MB
  55. 07 Matplotlib Basics/001 Matplotlib Basics - part 1.mp4 51MB
  56. 27 Step 5 - Predicting Heart Disease using Model/001 Step 5 - Predicting Heart Disease using Model.mp4 50MB
  57. 11 Activation Functions Introduction/001 Activation Functions Introduction.mp4 49MB
  58. 34 Serialize and Save Trained Model for Later Use/001 Serialize and Save Trained Model for Later Use.mp4 49MB
  59. 21 Step 8 Prediction Using the Model/001 Step 8 Prediction Using the Model.mp4 48MB
  60. 02 Introduction to AI and Machine Learning/001 Introduction to AI and Machine Learning.mp4 47MB
  61. 05 Python Basics/002 Python Basics - Flow Control - Part 1.mp4 47MB
  62. 54 VGG16 and VGG19 prediction/002 VGG16 and VGG19 prediction - Part 2.mp4 47MB
  63. 36 Basic Image Processing using Keras Functions/003 Basic Image Processing using Keras Functions - Part 3.mp4 46MB
  64. 05 Python Basics/004 Python Basics - List and Tuples.mp4 46MB
  65. 29 Step1 - Fetch and Load Data/001 Step1 - Fetch and Load Data.mp4 46MB
  66. 33 Step 5 - Predicting Wine Quality using Model/001 Step 5 - Predicting Wine Quality using Model.mp4 42MB
  67. 06 Numpy Basics/001 Numpy Basics - Part 1.mp4 41MB
  68. 48 Flowers Classification CNN - Optimization Techniques - Introduction/001 Flowers Classification CNN - Optimization Techniques - Introduction.mp4 41MB
  69. 07 Matplotlib Basics/002 Matplotlib Basics - part 2.mp4 38MB
  70. 28 Redwine Quality MultiClass Classification Model - Introduction/001 Redwine Quality MultiClass Classification Model - Introduction.mp4 37MB
  71. 44 Flowers Classification CNN - Defining the Model/003 Flowers Classification CNN - Defining the Model - Part 3.mp4 37MB
  72. 05 Python Basics/003 Python Basics - Flow Control - Part 2.mp4 36MB
  73. 05 Python Basics/006 Python Basics - Dictionary and Functions - part 2.mp4 34MB
  74. 08 Pandas Basics/002 Pandas Basics - Part 2.mp4 34MB
  75. 57 VGG16 Transfer Learning Flower Prediction/001 VGG16 Transfer Learning Flower Prediction.mp4 27MB
  76. 46 Flowers Classification CNN - Save Model for Later Use/001 Flowers Classification CNN - Save Model for Later Use.mp4 26MB
  77. 58 SOURCE CODE AND FILES ATTACHED/001 SOURCE CODE AND FILES ATTACHED.html 1KB
  78. 0. Websites you may like/[CourseClub.Me].url 122B
  79. 07 Matplotlib Basics/[CourseClub.Me].url 122B
  80. 22 Heart Disease Binary Classification Model - Introduction/[CourseClub.Me].url 122B
  81. 37 Keras Single Image Augmentation/[CourseClub.Me].url 122B
  82. 49 Flowers Classification CNN - Dropout Regularization/[CourseClub.Me].url 122B
  83. [CourseClub.Me].url 122B
  84. 0. Websites you may like/[GigaCourse.Com].url 49B
  85. 07 Matplotlib Basics/[GigaCourse.Com].url 49B
  86. 22 Heart Disease Binary Classification Model - Introduction/[GigaCourse.Com].url 49B
  87. 37 Keras Single Image Augmentation/[GigaCourse.Com].url 49B
  88. 49 Flowers Classification CNN - Dropout Regularization/[GigaCourse.Com].url 49B
  89. [GigaCourse.Com].url 49B