Mastering Image Generation With Gans Using Python And Keras

Hands-On Image Generation with Generative Adversarial Networks (GANs) using Python, TensorFlow, & Keras in Google Colab

What you'll learn

Understand the fundamentals of Generative Adversarial Networks (GANs) and their applications in image generation.

Gain a comprehensive understanding of the architecture and components of GANs.

Learn how to implement GANs using Python and Keras, a popular deep learning framework.

Acquire the knowledge and skills to train and evaluate GAN models for image generation tasks.

Gain hands-on experience through practical project.

Apply learned concepts and techniques to real-world image generation problems and datasets.

Requirements

Some experience with Python programming will be helpful as the course extensively uses Python for implementing GANs.

Description

In this comprehensive course, you will dive into the fascinating world of image generation using Generative Adversarial Networks (GANs) and gain hands-on experience in implementing these powerful models using Python, TensorFlow, and Keras. GANs have revolutionised the field of artificial intelligence and are widely used in various domains such as computer vision, art, entertainment, and more.Throughout the course, you will learn the fundamental concepts and principles behind GANs, including how they work, their components, and their training process. You will explore DCGAN architecture to generate high-quality and realistic images from random noise. You will also understand the challenges and considerations involved in training GANs effectively.Through practical coding exercises and projects, you will gain proficiency in Python programming, TensorFlow, and Keras libraries. You will develop a deep understanding of how to build, train, and evaluate GAN models for image generation tasks. Additionally, you will learn how to leverage Google Colab, a powerful cloud-based development environment, to harness the capabilities of GPUs for accelerated training.By the end of this course, you will have a strong foundation in GANs and image generation techniques, enabling you to embark on exciting projects and explore various applications in fields such as computer graphics, creative arts, advertising, and even research. The skills and knowledge you acquire throughout the course will equip you with a valuable asset sought after by industries that rely on computer vision and artificial intelligence, increasing your job prospects in roles related to machine learning, computer vision, data science, and image synthesis.Join us on this immersive learning journey to unlock your creativity and become proficient in image generation with GANs, empowering you to stand out in the competitive job market and opening doors to exciting career opportunities.

Overview

Section 1: Fundamentals

Lecture 1 Introduction

Lecture 2 About this Project

Lecture 3 Why should we learn?

Lecture 4 Applications

Lecture 5 Why Python and Keras?

Lecture 6 Why Google Colab?

Section 2: Model Building and Training

Lecture 7 Download Dataset

Lecture 8 Python Code

Lecture 9 Activate GPU

Lecture 10 Current Status of GPU

Lecture 11 Mounts Google Drive to a Google Colab notebook

Lecture 12 Importing libraries and Modules

Lecture 13 Enabling NumPy-like Behavior in TensorFlow

Lecture 14 Setting the Random Seed

Lecture 15 Setting up a Directory

Lecture 16 Visualizing Large Datasets of Images

Lecture 17 Settings for the Training Process

Lecture 18 Sets the Number of Training Samples

Lecture 19 Training Images are Loaded and Preprocessed

Lecture 20 List of Preprocessed Images Converted into a Numpy Array

Lecture 21 Normalizing the pixel values

Lecture 22 Creating a Shuffled and Batched TensorFlow Dataset

Lecture 23 Define Discriminator Neural Network Model

Lecture 24 Define Generator Neural Network Model

Lecture 25 Generator's loss

Lecture 26 Loss of the Discriminator

Lecture 27 Define and Summarize the Generator and Discriminator Networks

Lecture 28 Define Optimizers

Lecture 29 Define a Loss Function

Lecture 30 Visualize the Generated Images

Lecture 31 Create and Save Checkpoints During Training

Lecture 32 Value of the latent_dim Hyperparameter

Lecture 33 Generating a Tensor of Random Noise

Lecture 34 Define a TensorFlow training step

Lecture 35 Plot Training Metrics During the GAN Training

Lecture 36 Define Training Function

Lecture 37 Function Generates Images Using the Generator Model

Lecture 38 Training

Lecture 39 Creates an Animated GIF

Lecture 40 Generate and Visualize Images from the Generator

Lecture 41 Showing Examples of Images Generated by the GAN Model

Those who have a keen interest in machine learning and want to expand their knowledge and skills in generative models, specifically GANs.,Professionals who work in the field of data science, artificial intelligence, or related domains and want to gain expertise in generating realistic images using GANs.,Students pursuing computer science or related fields who want to enhance their understanding of advanced machine learning techniques and apply them to image generation tasks.,Software developers or programmers who want to delve into the exciting field of generative models and explore how GANs can be used to create novel and realistic images.,Individuals engaged in research or innovation, particularly in the areas of computer vision, image processing, or generative models, who want to leverage GANs for generating new visual content.