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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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    شناسه: 17131
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    مدت زمان: 832 دقیقه
    تاریخ انتشار: ۱۱ مرداد ۱۴۰۲
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