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

FreeCourseSiteUdemyDeepLearningusingKerasCompleteCompactDummiesGuide

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收录时间：2024-07-11

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文件列表：

  1. 01 Course Introduction and Table of Contents/001 Course Introduction and Table of Contents.mp4255.18 Mb
  2. 17 Step 2 and 3 EDA and Data Preparation/001 Step 2 and 3 EDA and Data Preparation - Part 1.mp4149.77 Mb
  3. 52 Hyper Parameter Tuning/002 Hyper Parameter Tuning - Part 2.mp4125.61 Mb
  4. 40 CNN Basics/001 CNN Basics.mp4125.52 Mb
  5. 17 Step 2 and 3 EDA and Data Preparation/002 Step 2 and 3 EDA and Data Preparation - Part 2.mp4120.41 Mb
  6. 19 Step 5 and 6 Compile and Fit Model/001 Step 5 and 6 Compile and Fit Model.mp4110.25 Mb
  7. 45 Flowers Classification CNN - Training and Visualization/001 Flowers Classification CNN - Training and Visualization.mp4106.53 Mb
  8. 56 VGG16 Transfer Learning Training Flowers Dataset/002 VGG16 Transfer Learning Training Flowers Dataset - part 2.mp4106.31 Mb
  9. 38 Keras Directory Image Augmentation/001 Keras Directory Image Augmentation.mp4105.63 Mb
  10. 37 Keras Single Image Augmentation/001 Keras Single Image Augmentation - Part 1.mp4104.04 Mb
  11. 30 Step 2 - EDA and Data Visualization/001 Step 2 - EDA and Data Visualization.mp4101.08 Mb
  12. 54 VGG16 and VGG19 prediction/001 VGG16 and VGG19 prediction - Part 1.mp4100.73 Mb
  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.mp499.73 Mb
  14. 39 Keras Data Frame Augmentation/001 Keras Data Frame Augmentation.mp499.1 Mb
  15. 52 Hyper Parameter Tuning/001 Hyper Parameter Tuning - Part 1.mp497.98 Mb
  16. 41 Stride Padding and Flattening Concepts of CNN/001 Stride Padding and Flattening Concepts of CNN.mp496.13 Mb
  17. 53 Transfer Learning using Pretrained Models - VGG Introduction/001 Transfer Learning using Pretrained Models - VGG Introduction.mp495.91 Mb
  18. 37 Keras Single Image Augmentation/002 Keras Single Image Augmentation - Part 2.mp495.03 Mb
  19. 55 ResNet50 Prediction/001 ResNet50 Prediction.mp494.23 Mb
  20. 42 Flowers CNN Image Classification Model - Fetch Load and Prepare Data/001 Flowers CNN Image Classification Model - Fetch Load and Prepare Data.mp492.3 Mb
  21. 15 Popular Neural Network Types/001 Popular Neural Network Types.mp489.15 Mb
  22. 44 Flowers Classification CNN - Defining the Model/002 Flowers Classification CNN - Defining the Model - Part 2.mp489.03 Mb
  23. 14 Popular Optimizers/001 Popular Optimizers.mp488.35 Mb
  24. 03 Introduction to Deep learning and Neural Networks/001 Introduction to Deep learning and Neural Networks.mp487.53 Mb
  25. 13 Popular Types of Loss Functions/001 Popular Types of Loss Functions.mp486.75 Mb
  26. 23 Step 1 - Fetch and Load Data/001 Step 1 - Fetch and Load Data.mp485.89 Mb
  27. 04 Setting up Computer - Installing Anaconda/001 Setting up Computer - Installing Anaconda.mp485.57 Mb
  28. 35 Digital Image Basics/001 Digital Image Basics.mp483.91 Mb
  29. 20 Step 7 Visualize Training and Metrics/001 Step 7 Visualize Training and Metrics.mp483.53 Mb
  30. 50 Flowers Classification CNN - Padding and Filter Optimization/001 Flowers Classification CNN - Padding and Filter Optimization.mp482.87 Mb
  31. 12 Popular Types of Activation Functions/001 Popular Types of Activation Functions.mp479.19 Mb
  32. 32 Step 4 - Compile Fit and Plot the Model/001 Step 4 - Compile Fit and Plot the Model.mp478.17 Mb
  33. 56 VGG16 Transfer Learning Training Flowers Dataset/001 VGG16 Transfer Learning Training Flowers Dataset - part 1.mp476.67 Mb
  34. 24 Step 2 and 3 - EDA and Data Preparation/002 Step 2 and 3 - EDA and Data Preparation - Part 2.mp476.19 Mb
  35. 26 Step 5 - Compile Fit and Plot the Model/001 Step 5 - Compile Fit and Plot the Model.mp474.42 Mb
  36. 31 Step 3 - Defining the Model/001 Step 3 - Defining the Model.mp472.82 Mb
  37. 47 Flowers Classification CNN - Load Saved Model and Predict/001 Flowers Classification CNN - Load Saved Model and Predict.mp469.87 Mb
  38. 49 Flowers Classification CNN - Dropout Regularization/001 Flowers Classification CNN - Dropout Regularization.mp469.36 Mb
  39. 24 Step 2 and 3 - EDA and Data Preparation/001 Step 2 and 3 - EDA and Data Preparation - Part 1.mp469.1 Mb
  40. 36 Basic Image Processing using Keras Functions/002 Basic Image Processing using Keras Functions - Part 2.mp465.45 Mb
  41. 25 Step 4 - Defining the model/001 Step 4 - Defining the model.mp465.42 Mb
  42. 18 Step 4 Defining the Keras Model/002 Step 4 Defining the Keras Model - Part 2.mp464.54 Mb
  43. 43 Flowers Classification CNN - Create Test and Train Folders/001 Flowers Classification CNN - Create Test and Train Folders.mp463.93 Mb
  44. 05 Python Basics/001 Python Basics - Assignment.mp463.43 Mb
  45. 10 Basic Structure of Artificial Neuron and Neural Network/001 Basic Structure of Artificial Neuron and Neural Network.mp463 Mb
  46. 36 Basic Image Processing using Keras Functions/001 Basic Image Processing using Keras Functions - Part 1.mp462.65 Mb
  47. 08 Pandas Basics/001 Pandas Basics - Part 1.mp458.6 Mb
  48. 51 Flowers Classification CNN - Augmentation Optimization/001 Flowers Classification CNN - Augmentation Optimization.mp458.59 Mb
  49. 18 Step 4 Defining the Keras Model/001 Step 4 Defining the Keras Model - Part 1.mp458.17 Mb
  50. 05 Python Basics/005 Python Basics - Dictionary and Functions - part 1.mp453.6 Mb
  51. 44 Flowers Classification CNN - Defining the Model/001 Flowers Classification CNN - Defining the Model - Part 1.mp453.57 Mb
  52. 22 Heart Disease Binary Classification Model - Introduction/001 Heart Disease Binary Classification Model - Introduction.mp453.05 Mb
  53. 09 Installing Deep Learning Libraries/001 Installing Deep Learning Libraries.mp452.79 Mb
  54. 06 Numpy Basics/002 Numpy Basics - Part 2.mp452.78 Mb
  55. 07 Matplotlib Basics/001 Matplotlib Basics - part 1.mp451.23 Mb
  56. 27 Step 5 - Predicting Heart Disease using Model/001 Step 5 - Predicting Heart Disease using Model.mp450.06 Mb
  57. 11 Activation Functions Introduction/001 Activation Functions Introduction.mp449.3 Mb
  58. 34 Serialize and Save Trained Model for Later Use/001 Serialize and Save Trained Model for Later Use.mp449.14 Mb
  59. 21 Step 8 Prediction Using the Model/001 Step 8 Prediction Using the Model.mp448.13 Mb
  60. 02 Introduction to AI and Machine Learning/001 Introduction to AI and Machine Learning.mp447.45 Mb
  61. 05 Python Basics/002 Python Basics - Flow Control - Part 1.mp446.83 Mb
  62. 54 VGG16 and VGG19 prediction/002 VGG16 and VGG19 prediction - Part 2.mp446.51 Mb
  63. 36 Basic Image Processing using Keras Functions/003 Basic Image Processing using Keras Functions - Part 3.mp446.44 Mb
  64. 05 Python Basics/004 Python Basics - List and Tuples.mp446.08 Mb
  65. 29 Step1 - Fetch and Load Data/001 Step1 - Fetch and Load Data.mp446.01 Mb
  66. 33 Step 5 - Predicting Wine Quality using Model/001 Step 5 - Predicting Wine Quality using Model.mp442.02 Mb
  67. 06 Numpy Basics/001 Numpy Basics - Part 1.mp441.01 Mb
  68. 48 Flowers Classification CNN - Optimization Techniques - Introduction/001 Flowers Classification CNN - Optimization Techniques - Introduction.mp440.54 Mb
  69. 07 Matplotlib Basics/002 Matplotlib Basics - part 2.mp437.99 Mb
  70. 28 Redwine Quality MultiClass Classification Model - Introduction/001 Redwine Quality MultiClass Classification Model - Introduction.mp437.11 Mb
  71. 44 Flowers Classification CNN - Defining the Model/003 Flowers Classification CNN - Defining the Model - Part 3.mp436.79 Mb
  72. 05 Python Basics/003 Python Basics - Flow Control - Part 2.mp436.43 Mb
  73. 05 Python Basics/006 Python Basics - Dictionary and Functions - part 2.mp433.93 Mb
  74. 08 Pandas Basics/002 Pandas Basics - Part 2.mp433.57 Mb
  75. 57 VGG16 Transfer Learning Flower Prediction/001 VGG16 Transfer Learning Flower Prediction.mp427.48 Mb
  76. 46 Flowers Classification CNN - Save Model for Later Use/001 Flowers Classification CNN - Save Model for Later Use.mp426.35 Mb
  77. 58 SOURCE CODE AND FILES ATTACHED/001 SOURCE CODE AND FILES ATTACHED.html1.05 Kb
  78. 0. Websites you may like/[FCS Forum].url133 Byte
  79. 0. Websites you may like/[FreeCourseSite.com].url127 Byte
  80. 0. Websites you may like/[CourseClub.ME].url122 Byte
  81. 0. Websites you may like/[GigaCourse.Com].url49 Byte
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