Keras Deep Learning and Generative Adversarial Networks (GAN)

Learn deep learning and Generative Adversarial Networks (GAN) using Python with Keras

Created by Abhilash Nelson using Keras and Python

VideoAssessment

Published: September 26th, 2023

ISBN-13: 9781805125495

Included in ExpertEdge Essentials

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Understand Generative Adversarial Networks (GAN) using Python with Keras
Learn deep learning from scratch to expert level
Python and deep learning using real-world examples

Learn about Artificial Intelligence (AI) and machine learning
Understand deep learning and neural networks
Learn about lists, tuples, dictionaries, and functions in Python
Learn Pandas, NumPy, and Matplotlib basics
Explore the basic structure of artificial neurons and neural network
Understand Stride, Padding, and Flattening concepts of CNNs

Table of Contents

105 Chapters

1
Introduction
2
Introduction to AI and Machine Learning
3
Introduction to Deep learning and Neural Networks
4
Setting Up Computer - Installing Anaconda
5
Python Basics - Flow Control
6
Python Basics - Lists and Tuples
7
Python Basics - Dictionaries and Functions
8
NumPy Basics
9
Matplotlib Basics
10
Pandas Basics
11
Installing Deep Learning Libraries
12
Basic Structure of Artificial Neuron and Neural Network
13
Activation Functions Introduction
14
Popular Types of Activation Functions
15
Popular Types of Loss Functions
16
Popular Optimizers
17
Popular Neural Network Types
18
King County House Sales Regression Model - Step 1 Fetch and Load Dataset
19
Steps 2 and 3 - EDA and Data Preparation
20
Step 4 - Defining the Keras Model
21
Steps 5 and 6 - Compile and Fit Model
22
Step 7 Visualize Training and Metrics
23
Step 8 Prediction Using the Model
24
Heart Disease Binary Classification Model - Introduction
25
Step 1 - Fetch and Load Data
26
Steps 2 and 3 - EDA and Data Preparation
27
Step 4 - Defining the Model
28
Step 5 - Compile, Fit, and Plot the Model
29
Step 5 - Predicting Heart Disease Using Model
30
Step 6 - Testing and Evaluating Heart Disease Model
31
Redwine Quality Multiclass Classification Model - Introduction
32
Step1 - Fetch and Load Data
33
Step 2 - EDA and Data Visualization
34
Step 3 - Defining the Model
35
Step 4 - Compile, Fit, and Plot the Model
36
Step 5 - Predicting Wine Quality Using Model
37
Serialize and Save Trained Model for Later Usage
38
Digital Image Basics
39
Basic Image Processing Using Keras Functions
40
Keras Single Image Augmentation
41
Keras Directory Image Augmentation
42
Keras Data Frame Augmentation
43
CNN Basics
44
Stride, Padding, and Flattening Concepts of CNN
45
Flowers CNN Image Classification Model - Fetch, Load, and Prepare Data
46
Flowers Classification CNN - Create Test and Train Folders
47
Flowers Classification CNN - Defining the Model
48
Flowers Classification CNN - Training and Visualization
49
Flowers Classification CNN - Save Model for Later Use
50
Flowers Classification CNN - Load Saved Model and Predict
51
Flowers Classification CNN - Optimization Techniques - Introduction
52
Flowers Classification CNN - Dropout Regularization
53
Flowers Classification CNN - Padding and Filter Optimization
54
Flowers Classification CNN - Augmentation Optimization
55
Hyperparameter Tuning
56
Transfer Learning Using Pre-Trained Models - VGG Introduction
57
VGG16 and VGG19 Prediction
58
ResNet50 Prediction
59
VGG16 Transfer Learning Training Flowers Dataset
60
VGG16 Transfer Learning Flower Prediction
61
VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading Dataset
62
VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction
63
VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction
64
ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction
65
Popular Neural Network Types
66
Generative Adversarial Networks GAN Introduction
67
Simple Transpose Convolution Using a Grayscale Image
68
Generator and Discriminator Mechanism Explained
69
A fully Connected Simple GAN Using MNIST Dataset - Introduction
70
Fully Connected GAN - Loading the Dataset
71
Fully Connected GAN - Defining the Generator Function
72
Fully Connected GAN - Defining the Discriminator Function
73
Fully Connected GAN - Combining Generator and Discriminator Models
74
Fully Connected GAN - Compiling Discriminator and Combined GAN Models
75
Fully Connected GAN - Discriminator Training
76
Fully Connected GAN - Generator Training
77
Fully Connected GAN - Saving Log at Each Interval
78
Fully Connected GAN - Plot the Log at Intervals
79
Fully Connected GAN - Display Generated Images
80
Saving the Trained Generator for Later Use
81
Generating Fake Images Using the Saved GAN Model
82
Fully Connected GAN Versus Deep Convoluted GAN
83
Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset
84
Deep Convolutional GAN - Defining the Generator Function
85
Deep Convolutional GAN - Defining the Discriminator Function
86
Deep Convolutional GAN - Combining and Compiling the Model
87
Deep Convolutional GAN - Training the Model
88
Deep Convolutional GAN - Training the Model Using Google Colab GPU
89
Deep Convolutional GAN - Loading the Fashion MNIST Dataset
90
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
92
Deep Convolutional GAN - Defining the Discriminator
93
Deep Convolutional GAN CIFAR-10 - Training the Model
94
Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPU
95
Vanilla GAN Versus Conditional GAN
96
Conditional GAN - Defining the Basic Generator Function
97
Conditional GAN - Label Embedding for Generator
98
Conditional GAN - Defining the Basic Discriminator Function
99
Conditional GAN - Label Embedding for Discriminator
100
Conditional GAN - Combining and Compiling the Model
101
Conditional GAN - Training the Model
102
Conditional GAN - Display Generated Images
103
Conditional GAN - Training the MNIST Model Using Google Colab GPU
104
Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPU
105
Other Popular GANs - Further Reference and Source Code Link