Keras Deep Learning & Generative Adversarial Networks (GAN)

سرفصل های دوره

Learn From the Scratch to Expert Level: Deep Learning & Generative Adversarial Networks (GAN) using Python with Keras

1. Introduction

1. Course Introduction and Table of Contents

2. Introduction to AI and Machine Learning

1. Introduction to AI and Machine Learning

3. Introduction to Deep learning and Neural Networks

1. Introduction to Deep learning and Neural Networks

4. Setting up Computer - Installing Anaconda

1. Setting up Computer - Installing Anaconda

5. Python Basics - Flow Control

1. Python Basics - Flow Control - Part 1

2. Python Basics - Flow Control - Part 2

6. Python Basics - List and Tuples

1. Python Basics - List and Tuples

7. Python Basics - Dictionary and Functions

1. Python Basics - Dictionary and Functions - part 1

2. Python Basics - Dictionary and Functions - part 2

8. Numpy Basics

1. Numpy Basics - Part 1

2. Numpy Basics - Part 2

9. Matplotlib Basics

1. Matplotlib Basics - part 1

2. Matplotlib Basics - part 2

10. Pandas Basics

1. Pandas Basics - Part 1

2. Pandas Basics - Part 2

11. Installing Deep Learning Libraries

1. Installing Deep Learning Libraries

12. Basic Structure of Artificial Neuron and Neural Network

1. Basic Structure of Artificial Neuron and Neural Network

13. Activation Functions Introduction

1. Activation Functions Introduction

14. Popular Types of Activation Functions

1. Popular Types of Activation Functions

15. Popular Types of Loss Functions

1. Popular Types of Loss Functions

16. Popular Optimizers

1. Popular Optimizers

17. Popular Neural Network Types

1. Popular Neural Network Types

18. King County House Sales Regression Model - Step 1 Fetch and Load Dataset

1. King County House Sales Regression Model - Step 1 Fetch and Load Dataset

19. Step 2 and 3 EDA and Data Prepration

1. Step 2 and 3 EDA and Data Prepration - Part 1

2. Step 2 and 3 EDA and Data Prepration - Part 2

20. Step 4 Defining the Keras Model

1. Step 4 Defining the Keras Model - Part 1

2. Step 4 Defining the Keras Model - Part 2

21. Step 5 and 6 Compile and Fit Model

1. Step 5 and 6 Compile and Fit Model

22. Step 7 Visualize Training and Metrics

1. Step 7 Visualize Training and Metrics

23. Step 8 Prediction Using the Model

1. Step 8 Prediction Using the Model

24. Heart Disease Binary Classification Model - Introduction

1. Heart Disease Binary Classification Model - Introduction

25. Step 1 - Fetch and Load Data

1. Step 1 - Fetch and Load Data

26. Step 2 and 3 - EDA and Data Preparation

1. Step 2 and 3 - EDA and Data Preparation - Part 1

2. Step 2 and 3 - EDA and Data Preparation - Part 2

27. Step 4 - Defining the model

1. Step 4 - Defining the model

28. Step 5 - Compile Fit and Plot the Model

1. Step 5 - Compile Fit and Plot the Model

29. Step 5 - Predicting Heart Disease using Model

1. Step 5 - Predicting Heart Disease using Model

30. Step 6 - Testing and Evaluating Heart Disease Model

1. Step 6 - Testing and Evaluating Heart Disease Model - Part 1

2. Step 6 - Testing and Evaluating Heart Disease Model - Part 2

31. Redwine Quality MultiClass Classification Model - Introduction

1. Redwine Quality MultiClass Classification Model - Introduction

32. Step1 - Fetch and Load Data

1. Step1 - Fetch and Load Data

33. Keras Single Image Augmentation

1. Keras Single Image Augmentation - Part 1

2. Keras Single Image Augmentation - Part 2

34. Keras Directory Image Augmentation

1. Keras Directory Image Augmentation

35. Keras Data Frame Augmentation

1. Keras Data Frame Augmentation

36. CNN Basics

1. CNN Basics

37. Stride Padding and Flattening Concepts of CNN

1. Stride Padding and Flattening Concepts of CNN

38. Flowers CNN Image Classification Model - Fetch Load and Prepare Data

1. Flowers CNN Image Classification Model - Fetch Load and Prepare Data

39. Flowers Classification CNN - Create Test and Train Folders

1. Flowers Classification CNN - Create Test and Train Folders

40. Flowers Classification CNN - Defining the Model

1. Flowers Classification CNN - Defining the Model - Part 1

2. Flowers Classification CNN - Defining the Model - Part 2

3. Flowers Classification CNN - Defining the Model - Part 3

41. Flowers Classification CNN - Training and Visualization

1. Flowers Classification CNN - Training and Visualization

42. Flowers Classification CNN - Save Model for Later Use

1. Flowers Classification CNN - Save Model for Later Use

43. Flowers Classification CNN - Load Saved Model and Predict

1. Flowers Classification CNN - Load Saved Model and Predict

44. Flowers Classification CNN - Optimization Techniques - Introduction

1. Flowers Classification CNN - Optimization Techniques - Introduction

45. Flowers Classification CNN - Dropout Regularization

1. Flowers Classification CNN - Dropout Regularization

46. Flowers Classification CNN - Padding and Filter Optimization

1. Flowers Classification CNN - Padding and Filter Optimization

47. Flowers Classification CNN - Augmentation Optimization

1. Flowers Classification CNN - Augmentation Optimization

48. Hyper Parameter Tuning

1. Hyper Parameter Tuning - Part 1

2. Hyper Parameter Tuning - Part 2

49. Transfer Learning using Pretrained Models - VGG Introduction

1. Transfer Learning using Pretrained Models - VGG Introduction

50. VGG16 and VGG19 prediction

1. VGG16 and VGG19 prediction- Part 1

2. VGG16 and VGG19 prediction- Part 2

51. ResNet50 Prediction

1. ResNet50 Prediction

52. VGG16 Transfer Learning Training Flowers Dataset

1. VGG16 Transfer Learning Training Flowers Dataset - part 1

2. VGG16 Transfer Learning Training Flowers Dataset - part 2

53. VGG16 Transfer Learning Flower Prediction

1. VGG16 Transfer Learning Flower Prediction

54. VGG16 Transfer Learning using Google Colab GPU - Preparing and Uploading Dataset

1. VGG16 Transfer Learning using Google Colab GPU - Preparing and Uploading Dataset

55. VGG16 Transfer Learning using Google Colab GPU - Training and Prediction

1. VGG16 Transfer Learning using Google Colab GPU - Training and Prediction

56. VGG19 Transfer Learning using Google Colab GPU - Training and Prediction

1. VGG19 Transfer Learning using Google Colab GPU - Training and Prediction

57. ResNet50 Transfer Learning using Google Colab GPU - Training and Prediction

1. ResNet50 Transfer Learning using Google Colab GPU - Training and Prediction

58. Popular Neural Network Types

1. Popular Neural Network Types

59. Generative Adversarial Networks GAN Introduction

1. Generative Adversarial Networks GAN Introduction

60. Simple Transpose Convolution using a grayscale image

1. Simple Transpose Convolution using a grayscale image - Part 1

2. Simple Transpose Convolution using a grayscale image - Part 2

3. Simple Transpose Convolution using a grayscale image - Part 3

61. Generator and Discriminator Mechanism Explained

1. Generator and Discriminator Mechanism Explained

62. A fully Connected Simple GAN using MNIST DataSet - Introduction

1. A fully Connected Simple GAN using MNIST DataSet - Introduction

63. Fully Connected GAN - Loading the Dataset

1. Fully Connected GAN - Loading the Dataset

64. Fully Connected GAN - Defining the Generator Function

1. Fully Connected GAN - Defining the Generator Function - Part 1

2. Fully Connected GAN - Defining the Generator Function - Part 2

65. Fully Connected GAN - Defining the Discriminator Function

1. Fully Connected GAN - Defining the Discriminator Function - Part 1

2. Fully Connected GAN - Defining the Discriminator Function - Part 2

66. Fully Connected GAN - Combining Generator and Discriminator Models

1. Fully Connected GAN - Combining Generator and Discriminator Models

67. Fully Connected GAN - Compiling Discriminator and Combined GAN Models

1. Fully Connected GAN - Compiling Discriminator and Combined GAN Models

68. Fully Connected GAN - Discriminator Training

1. Fully Connected GAN - Discriminator Training - Part 1

2. Fully Connected GAN - Discriminator Training - Part 2

3. Fully Connected GAN - Discriminator Training - Part 3

69. Fully Connected GAN - Generator Training

1. Fully Connected GAN - Generator Training

70. Fully Connected GAN - Saving Log at Each Interval

1. Fully Connected GAN - Saving Log at Each Interval

71. Fully Connected GAN - Plot the log at intervals

1. Fully Connected GAN - Plot the log at intervals

72. Fully Connected GAN - Display Generated Images

1. Fully Connected GAN - Display Generated Images - Part 1

2. Fully Connected GAN - Display Generated Images - Part 2

73. Saving the Trained Generator for Later Use

1. Saving the Trained Generator for Later Use

74. Generating fake images using the saved GAN Model

1. Generating fake images using the saved GAN Model

75. Fully Connected GAN vs Deep Convoluted GAN

1. Fully Connected GAN vs Deep Convoluted GAN

76. Deep Convolutional GAN - Loading the MNIST Hand Written Digits Dataset

1. Deep Convolutional GAN - Loading the MNIST Hand Written Digits Dataset

77. Deep Convolutional GAN - Defining the Generator Function

1. Deep Convolutional GAN - Defining the Generator Function - Part 1

2. Deep Convolutional GAN - Defining the Generator Function - Part 2

78. Deep Convolutional GAN - Defining the Discriminator Function

1. Deep Convolutional GAN - Defining the Discriminator Function

79. Deep Convolutional GAN - Combining and Compiling the Model

1. Deep Convolutional GAN - Combining and Compiling the Model

80. Deep Convolutional GAN - Training the Model

1. Deep Convolutional GAN - Training the Model

81. Deep Convolutional GAN - Training the Model using Google Colab GPU

1. Deep Convolutional GAN - Training the Model using Google Colab GPU

82. Deep Convolutional GAN - Loading the Fashion MNIST Dataset

1. Deep Convolutional GAN - Loading the Fashion MNIST Dataset

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