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

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视频 2021-8-25 02:14 2024-11-23 19:33 253 5.49 GB 76
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FreeCourseSite com Udemy - Deep Learning using Keras - Complete Compact Dummies Guide

文件列表
  1. 01 Course Introduction and Table of Contents/001 Course Introduction and Table of Contents.mp4255.18MB
  2. 02 Introduction to AI and Machine Learning/001 Introduction to AI and Machine Learning.mp447.45MB
  3. 03 Introduction to Deep learning and Neural Networks/001 Introduction to Deep learning and Neural Networks.mp487.53MB
  4. 04 Setting up Computer - Installing Anaconda/001 Setting up Computer - Installing Anaconda.mp485.57MB
  5. 05 Python Basics/001 Python Basics - Assignment.mp463.43MB
  6. 05 Python Basics/002 Python Basics - Flow Control - Part 1.mp446.83MB
  7. 05 Python Basics/003 Python Basics - Flow Control - Part 2.mp436.43MB
  8. 05 Python Basics/004 Python Basics - List and Tuples.mp446.08MB
  9. 05 Python Basics/005 Python Basics - Dictionary and Functions - part 1.mp453.6MB
  10. 05 Python Basics/006 Python Basics - Dictionary and Functions - part 2.mp433.93MB
  11. 06 Numpy Basics/001 Numpy Basics - Part 1.mp441.01MB
  12. 06 Numpy Basics/002 Numpy Basics - Part 2.mp452.78MB
  13. 07 Matplotlib Basics/001 Matplotlib Basics - part 1.mp451.23MB
  14. 07 Matplotlib Basics/002 Matplotlib Basics - part 2.mp437.99MB
  15. 08 Pandas Basics/001 Pandas Basics - Part 1.mp458.6MB
  16. 08 Pandas Basics/002 Pandas Basics - Part 2.mp433.57MB
  17. 09 Installing Deep Learning Libraries/001 Installing Deep Learning Libraries.mp452.79MB
  18. 10 Basic Structure of Artificial Neuron and Neural Network/001 Basic Structure of Artificial Neuron and Neural Network.mp463MB
  19. 11 Activation Functions Introduction/001 Activation Functions Introduction.mp449.3MB
  20. 12 Popular Types of Activation Functions/001 Popular Types of Activation Functions.mp479.19MB
  21. 13 Popular Types of Loss Functions/001 Popular Types of Loss Functions.mp486.75MB
  22. 14 Popular Optimizers/001 Popular Optimizers.mp488.35MB
  23. 15 Popular Neural Network Types/001 Popular Neural Network Types.mp489.15MB
  24. 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.mp499.73MB
  25. 17 Step 2 and 3 EDA and Data Preparation/001 Step 2 and 3 EDA and Data Preparation - Part 1.mp4149.77MB
  26. 17 Step 2 and 3 EDA and Data Preparation/002 Step 2 and 3 EDA and Data Preparation - Part 2.mp4120.41MB
  27. 18 Step 4 Defining the Keras Model/001 Step 4 Defining the Keras Model - Part 1.mp458.17MB
  28. 18 Step 4 Defining the Keras Model/002 Step 4 Defining the Keras Model - Part 2.mp464.54MB
  29. 19 Step 5 and 6 Compile and Fit Model/001 Step 5 and 6 Compile and Fit Model.mp4110.25MB
  30. 20 Step 7 Visualize Training and Metrics/001 Step 7 Visualize Training and Metrics.mp483.53MB
  31. 21 Step 8 Prediction Using the Model/001 Step 8 Prediction Using the Model.mp448.13MB
  32. 22 Heart Disease Binary Classification Model - Introduction/001 Heart Disease Binary Classification Model - Introduction.mp453.05MB
  33. 23 Step 1 - Fetch and Load Data/001 Step 1 - Fetch and Load Data.mp485.89MB
  34. 24 Step 2 and 3 - EDA and Data Preparation/001 Step 2 and 3 - EDA and Data Preparation - Part 1.mp469.1MB
  35. 24 Step 2 and 3 - EDA and Data Preparation/002 Step 2 and 3 - EDA and Data Preparation - Part 2.mp476.19MB
  36. 25 Step 4 - Defining the model/001 Step 4 - Defining the model.mp465.42MB
  37. 26 Step 5 - Compile Fit and Plot the Model/001 Step 5 - Compile Fit and Plot the Model.mp474.42MB
  38. 27 Step 5 - Predicting Heart Disease using Model/001 Step 5 - Predicting Heart Disease using Model.mp450.06MB
  39. 28 Redwine Quality MultiClass Classification Model - Introduction/001 Redwine Quality MultiClass Classification Model - Introduction.mp437.11MB
  40. 29 Step1 - Fetch and Load Data/001 Step1 - Fetch and Load Data.mp446.01MB
  41. 30 Step 2 - EDA and Data Visualization/001 Step 2 - EDA and Data Visualization.mp4101.08MB
  42. 31 Step 3 - Defining the Model/001 Step 3 - Defining the Model.mp472.82MB
  43. 32 Step 4 - Compile Fit and Plot the Model/001 Step 4 - Compile Fit and Plot the Model.mp478.17MB
  44. 33 Step 5 - Predicting Wine Quality using Model/001 Step 5 - Predicting Wine Quality using Model.mp442.02MB
  45. 34 Serialize and Save Trained Model for Later Use/001 Serialize and Save Trained Model for Later Use.mp449.14MB
  46. 35 Digital Image Basics/001 Digital Image Basics.mp483.91MB
  47. 36 Basic Image Processing using Keras Functions/001 Basic Image Processing using Keras Functions - Part 1.mp462.65MB
  48. 36 Basic Image Processing using Keras Functions/002 Basic Image Processing using Keras Functions - Part 2.mp465.45MB
  49. 36 Basic Image Processing using Keras Functions/003 Basic Image Processing using Keras Functions - Part 3.mp446.44MB
  50. 37 Keras Single Image Augmentation/001 Keras Single Image Augmentation - Part 1.mp4104.04MB
  51. 37 Keras Single Image Augmentation/002 Keras Single Image Augmentation - Part 2.mp495.03MB
  52. 38 Keras Directory Image Augmentation/001 Keras Directory Image Augmentation.mp4105.63MB
  53. 39 Keras Data Frame Augmentation/001 Keras Data Frame Augmentation.mp499.1MB
  54. 40 CNN Basics/001 CNN Basics.mp4125.52MB
  55. 41 Stride Padding and Flattening Concepts of CNN/001 Stride Padding and Flattening Concepts of CNN.mp496.13MB
  56. 42 Flowers CNN Image Classification Model - Fetch Load and Prepare Data/001 Flowers CNN Image Classification Model - Fetch Load and Prepare Data.mp492.3MB
  57. 43 Flowers Classification CNN - Create Test and Train Folders/001 Flowers Classification CNN - Create Test and Train Folders.mp463.93MB
  58. 44 Flowers Classification CNN - Defining the Model/001 Flowers Classification CNN - Defining the Model - Part 1.mp453.57MB
  59. 44 Flowers Classification CNN - Defining the Model/002 Flowers Classification CNN - Defining the Model - Part 2.mp489.03MB
  60. 44 Flowers Classification CNN - Defining the Model/003 Flowers Classification CNN - Defining the Model - Part 3.mp436.79MB
  61. 45 Flowers Classification CNN - Training and Visualization/001 Flowers Classification CNN - Training and Visualization.mp4106.53MB
  62. 46 Flowers Classification CNN - Save Model for Later Use/001 Flowers Classification CNN - Save Model for Later Use.mp426.35MB
  63. 47 Flowers Classification CNN - Load Saved Model and Predict/001 Flowers Classification CNN - Load Saved Model and Predict.mp469.87MB
  64. 48 Flowers Classification CNN - Optimization Techniques - Introduction/001 Flowers Classification CNN - Optimization Techniques - Introduction.mp440.54MB
  65. 49 Flowers Classification CNN - Dropout Regularization/001 Flowers Classification CNN - Dropout Regularization.mp469.36MB
  66. 50 Flowers Classification CNN - Padding and Filter Optimization/001 Flowers Classification CNN - Padding and Filter Optimization.mp482.87MB
  67. 51 Flowers Classification CNN - Augmentation Optimization/001 Flowers Classification CNN - Augmentation Optimization.mp458.59MB
  68. 52 Hyper Parameter Tuning/001 Hyper Parameter Tuning - Part 1.mp497.98MB
  69. 52 Hyper Parameter Tuning/002 Hyper Parameter Tuning - Part 2.mp4125.61MB
  70. 53 Transfer Learning using Pretrained Models - VGG Introduction/001 Transfer Learning using Pretrained Models - VGG Introduction.mp495.91MB
  71. 54 VGG16 and VGG19 prediction/001 VGG16 and VGG19 prediction - Part 1.mp4100.73MB
  72. 54 VGG16 and VGG19 prediction/002 VGG16 and VGG19 prediction - Part 2.mp446.51MB
  73. 55 ResNet50 Prediction/001 ResNet50 Prediction.mp494.23MB
  74. 56 VGG16 Transfer Learning Training Flowers Dataset/001 VGG16 Transfer Learning Training Flowers Dataset - part 1.mp476.67MB
  75. 56 VGG16 Transfer Learning Training Flowers Dataset/002 VGG16 Transfer Learning Training Flowers Dataset - part 2.mp4106.31MB
  76. 57 VGG16 Transfer Learning Flower Prediction/001 VGG16 Transfer Learning Flower Prediction.mp427.48MB
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