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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

Description

The course begins with the fundamentals of Python, encompassing concepts such as assignment, flow control, lists, tuples, dictionaries, and functions. We then move on to the Python NumPy library, which supports large arrays and matrices. Before embarking on the journey of deep learning, a comprehensive theoretical session awaits, expounding upon the essential structure of an artificial neuron and its amalgamation to form an artificial neural network. The exploration then delves into the realm of CNNs, text-based models, binary and multi-class classification, and the intricate world of image processing. The transformation continues with an in-depth exploration of the GAN paradigm, spanning from fundamental principles to advanced strategies. Attendees will have the opportunity to construct models, harness transfer learning techniques, and venture into the realm of conditional GANs. Once we complete the fully connected GAN, we will then proceed with a more advanced Deep Convoluted GAN, or DCGAN. We will discuss what a DCGAN is and see the difference between a DCGAN and a fully connected GAN. Then we will try to implement the DCGAN. We will define the Generator function and define the Discriminator function. By the end of the course, you will wield the skills to create, fine-tune, and deploy cutting-edge AI solutions, setting you apart in this evolving landscape.

Who is this book for?

This course is designed for newcomers aiming to excel in deep learning and Generative Adversarial Networks (GANs) starting from the basics. Progress from novice to advanced through immersive learning. Suitable for roles like machine learning engineer, deep learning specialist, AI researcher, data scientist, and GAN developer.

What you will learn

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

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105 Chapters

Introduction

Course Introduction and Table of Contents

Introduction to AI and Machine Learning

Introduction to Deep learning and Neural Networks

Setting Up Computer - Installing Anaconda

Python Basics - Flow Control

Python Basics - Flow Control - Part 1

Python Basics - Flow Control - Part 2

Python Basics - Lists and Tuples

Python Basics - Dictionaries and Functions

Python Basics - Dictionaries and Functions - part 1

Python Basics - Dictionary and Functions - part 2

NumPy Basics

NumPy Basics - Part 1

NumPy Basics - Part 2

Matplotlib Basics

Matplotlib Basics - part 1

Matplotlib Basics - part 2

Pandas Basics

Pandas Basics - Part 1

Pandas Basics - Part 2

Installing Deep Learning Libraries

Basic Structure of Artificial Neuron and Neural Network

Activation Functions Introduction

Popular Types of Activation Functions

Popular Types of Loss Functions

Popular Optimizers

Popular Neural Network Types

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

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

Step 4 - Defining the Keras Model

Step 4 Defining the Keras Model - Part 1

Step 4 Defining the Keras Model - Part 2

Steps 5 and 6 - Compile and Fit Model

Steps 5 and 6 Compile and Fit Model

Step 7 Visualize Training and Metrics

Step 8 Prediction Using the Model

Heart Disease Binary Classification Model - Introduction

Step 1 - Fetch and Load Data

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

Step 4 - Defining the Model

Step 5 – Compile, Fit, and Plot the Model

Step 5 - Predicting Heart Disease Using Model

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

Redwine Quality Multiclass Classification Model - Introduction

Step1 - Fetch and Load Data

Step 2 - EDA and Data Visualization

Step 3 - Defining the Model

Step 4 – Compile, Fit, and Plot the Model

Step 5 - Predicting Wine Quality Using Model

Serialize and Save Trained Model for Later Usage

Digital Image Basics

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

Keras Single Image Augmentation

Keras Single Image Augmentation - Part 1

Keras Single Image Augmentation - Part 2

Keras Directory Image Augmentation

Keras Data Frame Augmentation

CNN Basics

Stride, Padding, and Flattening Concepts of CNN

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

Flowers Classification CNN - Create Test and Train Folders

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

Flowers Classification CNN - Training and Visualization

Flowers Classification CNN - Save Model for Later Use

Flowers Classification CNN - Load Saved Model and Predict

Flowers Classification CNN - Optimization Techniques - Introduction

Flowers Classification CNN - Dropout Regularization

Flowers Classification CNN - Padding and Filter Optimization

Flowers Classification CNN - Augmentation Optimization

Hyperparameter Tuning

Hyperparameter Tuning - Part 1

Hyperparameter Tuning - Part 2

Transfer Learning Using Pre-Trained Models - VGG Introduction

VGG16 and VGG19 Prediction

VGG16 and VGG19 Prediction- Part 1

VGG16 and VGG19 Prediction- Part 2

ResNet50 Prediction

VGG16 Transfer Learning Training Flowers Dataset

VGG16 Transfer Learning Training Flowers Dataset - part 1

VGG16 Transfer Learning Training Flowers Dataset - Part 2

VGG16 Transfer Learning Flower Prediction

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

VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction

VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction

ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction

Popular Neural Network Types

Generative Adversarial Networks GAN Introduction

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

Generator and Discriminator Mechanism Explained

A fully Connected Simple GAN Using MNIST Dataset - Introduction

A Fully Connected Simple GAN Using MNIST Dataset - Introduction

Fully Connected GAN - Loading the Dataset

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

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

Fully Connected GAN - Combining Generator and Discriminator Models

Fully Connected GAN - Compiling Discriminator and Combined GAN Models

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

Fully Connected GAN - Generator Training

Fully Connected GAN - Saving Log at Each Interval

Fully Connected GAN - Plot the Log at Intervals

Fully Connected GAN - Display Generated Images

Fully Connected GAN - Display Generated Images - Part 1

Fully Connected GAN - Display Generated Images - Part 2

Saving the Trained Generator for Later Use

Generating Fake Images Using the Saved GAN Model

Fully Connected GAN Versus Deep Convoluted GAN

Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset

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

Deep Convolutional GAN - Defining the Discriminator Function

Deep Convolutional GAN - Combining and Compiling the Model

Deep Convolutional GAN - Training the Model

Deep Convolutional GAN - Training the Model Using Google Colab GPU

Deep Convolutional GAN - Loading the Fashion MNIST Dataset

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

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

Deep Convolutional GAN - Defining the Discriminator

Deep Convolutional GAN CIFAR-10 - Training the Model

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

Vanilla GAN Versus Conditional GAN

Conditional GAN - Defining the Basic Generator Function

Conditional GAN - Label Embedding for Generator

Conditional GAN - Label Embedding for Generator - Part 1

Conditional GAN - Label Embedding for Generator - Part 2

Conditional GAN - Defining the Basic Discriminator Function

Conditional GAN - Label Embedding for Discriminator

Conditional GAN - Combining and Compiling the Model

Conditional GAN - Training the Model

Conditional GAN - Training the Model - Part 1

Conditional GAN - Training the Model - Part 2

Conditional GAN - Display Generated Images

Conditional GAN - Training the MNIST Model Using Google Colab GPU

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

Other Popular GANs - Further Reference and Source Code Link

Tapiwa Jun 19, 2024

Pandas: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` print(myseries[3])

Subscriber review

Andreas Dec 02, 2023

both video and code unzipped, will check and give a new rating

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

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