1. Introduction FREE CHAPTER

• Course Introduction and Table of Contents

2. Introduction to AI and Machine Learning

• Introduction to AI and Machine Learning

3. Introduction to Deep learning and Neural Networks

• Introduction to Deep learning and Neural Networks

4. Setting Up Computer - Installing Anaconda

• Setting Up Computer - Installing Anaconda

5. Python Basics - Flow Control

• Python Basics - Flow Control - Part 1
• Python Basics - Flow Control - Part 2

6. Python Basics - Lists and Tuples

• Python Basics - Lists and Tuples

7. Python Basics - Dictionaries and Functions

• Python Basics - Dictionaries and Functions - part 1
• Python Basics - Dictionary and Functions - part 2

8. NumPy Basics

• NumPy Basics - Part 1
• NumPy Basics - Part 2

9. Matplotlib Basics

• Matplotlib Basics - part 1
• Matplotlib Basics - part 2

10. Pandas Basics

• Pandas Basics - Part 1
• Pandas Basics - Part 2

11. Installing Deep Learning Libraries

• Installing Deep Learning Libraries

12. Basic Structure of Artificial Neuron and Neural Network

• Basic Structure of Artificial Neuron and Neural Network

13. Activation Functions Introduction

• Activation Functions Introduction

14. Popular Types of Activation Functions

• Popular Types of Activation Functions

15. Popular Types of Loss Functions

• Popular Types of Loss Functions

16. Popular Optimizers

• Popular Optimizers

17. Popular Neural Network Types

• Popular Neural Network Types

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

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

19. Steps 2 and 3 - EDA and Data Preparation

• Steps 2 and 3 - EDA and Data Preparation - Part 1
• Steps 2 and 3 - EDA and Data Preparation - Part 2

20. Step 4 - Defining the Keras Model

• Step 4 Defining the Keras Model - Part 1
• Step 4 Defining the Keras Model - Part 2

21. Steps 5 and 6 - Compile and Fit Model

• Steps 5 and 6 Compile and Fit Model

22. Step 7 Visualize Training and Metrics

• Step 7 Visualize Training and Metrics

23. Step 8 Prediction Using the Model

• Step 8 Prediction Using the Model

24. Heart Disease Binary Classification Model - Introduction

• Heart Disease Binary Classification Model - Introduction

25. Step 1 - Fetch and Load Data

• Step 1 - Fetch and Load Data

26. Steps 2 and 3 - EDA and Data Preparation

• Steps 2 and 3 - EDA and Data Preparation - Part 1
• Steps 2 and 3 - EDA and Data Preparation - Part 2

27. Step 4 - Defining the Model

• Step 4 - Defining the Model

28. Step 5 – Compile, Fit, and Plot the Model

• Step 5 – Compile, Fit, and Plot the Model

29. Step 5 - Predicting Heart Disease Using Model

• Step 5 - Predicting Heart Disease Using Model

30. Step 6 - Testing and Evaluating Heart Disease Model

• Step 6 - Testing and Evaluating Heart Disease Model - Part 1
• Step 6 - Testing and Evaluating Heart Disease Model - Part 2

31. Redwine Quality Multiclass Classification Model - Introduction

• Redwine Quality Multiclass Classification Model - Introduction

32. Step1 - Fetch and Load Data

• Step1 - Fetch and Load Data

33. Step 2 - EDA and Data Visualization

• Step 2 - EDA and Data Visualization

34. Step 3 - Defining the Model

• Step 3 - Defining the Model

35. Step 4 – Compile, Fit, and Plot the Model

• Step 4 – Compile, Fit, and Plot the Model

36. Step 5 - Predicting Wine Quality Using Model

• Step 5 - Predicting Wine Quality Using Model

37. Serialize and Save Trained Model for Later Usage

• Serialize and Save Trained Model for Later Usage

38. Digital Image Basics

• Digital Image Basics

39. Basic Image Processing Using Keras Functions

• Basic Image Processing Using Keras Functions - Part 1
• Basic Image Processing Using Keras Functions - Part 2
• Basic Image Processing using Keras Functions - Part 3

40. Keras Single Image Augmentation

• Keras Single Image Augmentation - Part 1
• Keras Single Image Augmentation - Part 2

41. Keras Directory Image Augmentation

• Keras Directory Image Augmentation

42. Keras Data Frame Augmentation

• Keras Data Frame Augmentation

43. CNN Basics

• CNN Basics

44. Stride, Padding, and Flattening Concepts of CNN

• Stride, Padding, and Flattening Concepts of CNN

45. Flowers CNN Image Classification Model – Fetch, Load, and Prepare Data

• Flowers CNN Image Classification Model – Fetch, Load, and Prepare Data

46. Flowers Classification CNN - Create Test and Train Folders

• Flowers Classification CNN - Create Test and Train Folders

47. Flowers Classification CNN - Defining the Model

• Flowers Classification CNN - Defining the Model - Part 1
• Flowers Classification CNN - Defining the Model - Part 2
• Flowers Classification CNN - Defining the Model - Part 3

48. Flowers Classification CNN - Training and Visualization

• Flowers Classification CNN - Training and Visualization

49. Flowers Classification CNN - Save Model for Later Use

• Flowers Classification CNN - Save Model for Later Use

50. Flowers Classification CNN - Load Saved Model and Predict

• Flowers Classification CNN - Load Saved Model and Predict

51. Flowers Classification CNN - Optimization Techniques - Introduction

• Flowers Classification CNN - Optimization Techniques - Introduction

52. Flowers Classification CNN - Dropout Regularization

• Flowers Classification CNN - Dropout Regularization

53. Flowers Classification CNN - Padding and Filter Optimization

• Flowers Classification CNN - Padding and Filter Optimization

54. Flowers Classification CNN - Augmentation Optimization

• Flowers Classification CNN - Augmentation Optimization

55. Hyperparameter Tuning

• Hyperparameter Tuning - Part 1
• Hyperparameter Tuning - Part 2

56. Transfer Learning Using Pre-Trained Models - VGG Introduction

• Transfer Learning Using Pre-Trained Models - VGG Introduction

57. VGG16 and VGG19 Prediction

• VGG16 and VGG19 Prediction- Part 1
• VGG16 and VGG19 Prediction- Part 2

58. ResNet50 Prediction

• ResNet50 Prediction

59. VGG16 Transfer Learning Training Flowers Dataset

• VGG16 Transfer Learning Training Flowers Dataset - part 1
• VGG16 Transfer Learning Training Flowers Dataset - Part 2

60. VGG16 Transfer Learning Flower Prediction

• VGG16 Transfer Learning Flower Prediction

61. VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading Dataset

• VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading Dataset

62. VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction

• VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction

63. VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction

• VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction

64. ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction

• ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction

65. Popular Neural Network Types

• Popular Neural Network Types

66. Generative Adversarial Networks GAN Introduction

• Generative Adversarial Networks GAN Introduction

67. Simple Transpose Convolution Using a Grayscale Image

• Simple Transpose Convolution Using a Grayscale Image - Part 1
• Simple Transpose Convolution Using a Grayscale Image - Part 2
• Simple Transpose Convolution Using a Grayscale Image - Part 3

68. Generator and Discriminator Mechanism Explained

• Generator and Discriminator Mechanism Explained

69. A fully Connected Simple GAN Using MNIST Dataset - Introduction

• A Fully Connected Simple GAN Using MNIST Dataset - Introduction

70. Fully Connected GAN - Loading the Dataset

• Fully Connected GAN - Loading the Dataset

71. Fully Connected GAN - Defining the Generator Function

• Fully Connected GAN - Defining the Generator Function - Part 1
• Fully Connected GAN - Defining the Generator Function - Part 2

72. Fully Connected GAN - Defining the Discriminator Function

• Fully Connected GAN - Defining the Discriminator Function - Part 1
• Fully Connected GAN - Defining the Discriminator Function - Part 2

73. Fully Connected GAN - Combining Generator and Discriminator Models

• Fully Connected GAN - Combining Generator and Discriminator Models

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

• Fully Connected GAN - Compiling Discriminator and Combined GAN Models

75. Fully Connected GAN - Discriminator Training

• Fully Connected GAN - Discriminator Training - Part 1
• Fully Connected GAN - Discriminator Training - Part 2
• Fully Connected GAN - Discriminator Training - Part 3

76. Fully Connected GAN - Generator Training

• Fully Connected GAN - Generator Training

77. Fully Connected GAN - Saving Log at Each Interval

• Fully Connected GAN - Saving Log at Each Interval

78. Fully Connected GAN - Plot the Log at Intervals

• Fully Connected GAN - Plot the Log at Intervals

79. Fully Connected GAN - Display Generated Images

• Fully Connected GAN - Display Generated Images - Part 1
• Fully Connected GAN - Display Generated Images - Part 2

80. Saving the Trained Generator for Later Use

• Saving the Trained Generator for Later Use

81. Generating Fake Images Using the Saved GAN Model

• Generating Fake Images Using the Saved GAN Model

82. Fully Connected GAN Versus Deep Convoluted GAN

• Fully Connected GAN Versus Deep Convoluted GAN

83. Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset

• Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset

84. Deep Convolutional GAN - Defining the Generator Function

• Deep Convolutional GAN - Defining the Generator Function - Part 1
• Deep Convolutional GAN - Defining the Generator Function - Part 2

85. Deep Convolutional GAN - Defining the Discriminator Function

• Deep Convolutional GAN - Defining the Discriminator Function

86. Deep Convolutional GAN - Combining and Compiling the Model

• Deep Convolutional GAN - Combining and Compiling the Model

87. Deep Convolutional GAN - Training the Model

• Deep Convolutional GAN - Training the Model

88. Deep Convolutional GAN - Training the Model Using Google Colab GPU

• Deep Convolutional GAN - Training the Model Using Google Colab GPU

89. Deep Convolutional GAN - Loading the Fashion MNIST Dataset

• Deep Convolutional GAN - Loading the Fashion MNIST Dataset

90. Deep Convolutional GAN - Training the MNIST Fashion Model Using Google Colab GPU

• Deep Convolutional GAN - Training the MNIST Fashion Model Using Google Colab GPU

91. Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Defining the Generator

• Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Generator - Part 1
• Loading the CIFAR-10 Dataset and Defining the Generator - part 2

92. Deep Convolutional GAN - Defining the Discriminator

• Deep Convolutional GAN - Defining the Discriminator

93. Deep Convolutional GAN CIFAR-10 - Training the Model

• Deep Convolutional GAN CIFAR-10 - Training the Model

94. Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPU

• Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPU

95. Vanilla GAN Versus Conditional GAN

• Vanilla GAN Versus Conditional GAN

96. Conditional GAN - Defining the Basic Generator Function

• Conditional GAN - Defining the Basic Generator Function

97. Conditional GAN - Label Embedding for Generator

• Conditional GAN - Label Embedding for Generator - Part 1
• Conditional GAN - Label Embedding for Generator - Part 2

98. Conditional GAN - Defining the Basic Discriminator Function

• Conditional GAN - Defining the Basic Discriminator Function

99. Conditional GAN - Label Embedding for Discriminator

• Conditional GAN - Label Embedding for Discriminator

100. Conditional GAN - Combining and Compiling the Model

• Conditional GAN - Combining and Compiling the Model

101. Conditional GAN - Training the Model

• Conditional GAN - Training the Model - Part 1
• Conditional GAN - Training the Model - Part 2

102. Conditional GAN - Display Generated Images

• Conditional GAN - Display Generated Images

103. Conditional GAN - Training the MNIST Model Using Google Colab GPU

• Conditional GAN - Training the MNIST Model Using Google Colab GPU

104. Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPU

• Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPU

105. Other Popular GANs - Further Reference and Source Code Link

• Other Popular GANs - Further Reference and Source Code Link