Mastering GANs: Image Generation with Python and GauGAN
Mastering GANs: Image Generation with Python and GauGAN, available at $19.99, with 41 lectures, and has 100 subscribers.
You will learn about Develop the technical skills to build and train the GauGAN model. Learn techniques for preparing and managing image datasets for training GANs. Understand and apply techniques to optimize and fine-tune the performance of GAN models. Use various tools and methods to monitor and visualize the training process. Gain practical experience in deploying and using trained models for image generation tasks. Utilize Google Colab effectively for running and training deep learning models using GPU acceleration. This course is ideal for individuals who are Those looking to expand their knowledge in deep learning and GANs. or Professionals aiming to enhance their skills in advanced image generation techniques. or Students in computer science or related fields who want hands-on experience with state-of-the-art models. or Individuals focused on generative models and image synthesis research. or Developers interested in applying their Python skills to machine learning and deep learning projects. or Hobbyists and enthusiasts passionate about learning the intricacies of GANs and their applications in image generation. It is particularly useful for Those looking to expand their knowledge in deep learning and GANs. or Professionals aiming to enhance their skills in advanced image generation techniques. or Students in computer science or related fields who want hands-on experience with state-of-the-art models. or Individuals focused on generative models and image synthesis research. or Developers interested in applying their Python skills to machine learning and deep learning projects. or Hobbyists and enthusiasts passionate about learning the intricacies of GANs and their applications in image generation.
Enroll now: Mastering GANs: Image Generation with Python and GauGAN
Summary
Title: Mastering GANs: Image Generation with Python and GauGAN
Price: $19.99
Number of Lectures: 41
Number of Published Lectures: 41
Number of Curriculum Items: 41
Number of Published Curriculum Objects: 41
Original Price: ₹799
Quality Status: approved
Status: Live
What You Will Learn
- Develop the technical skills to build and train the GauGAN model.
- Learn techniques for preparing and managing image datasets for training GANs.
- Understand and apply techniques to optimize and fine-tune the performance of GAN models.
- Use various tools and methods to monitor and visualize the training process.
- Gain practical experience in deploying and using trained models for image generation tasks.
- Utilize Google Colab effectively for running and training deep learning models using GPU acceleration.
Who Should Attend
- Those looking to expand their knowledge in deep learning and GANs.
- Professionals aiming to enhance their skills in advanced image generation techniques.
- Students in computer science or related fields who want hands-on experience with state-of-the-art models.
- Individuals focused on generative models and image synthesis research.
- Developers interested in applying their Python skills to machine learning and deep learning projects.
- Hobbyists and enthusiasts passionate about learning the intricacies of GANs and their applications in image generation.
Target Audiences
- Those looking to expand their knowledge in deep learning and GANs.
- Professionals aiming to enhance their skills in advanced image generation techniques.
- Students in computer science or related fields who want hands-on experience with state-of-the-art models.
- Individuals focused on generative models and image synthesis research.
- Developers interested in applying their Python skills to machine learning and deep learning projects.
- Hobbyists and enthusiasts passionate about learning the intricacies of GANs and their applications in image generation.
Welcome to “Mastering GANs: Image Generation with Python and GauGAN,” a comprehensive course designed to equip you with the knowledge and skills to master Generative Adversarial Networks (GANs) for creating high-quality images. Throughout this course, you will delve into the intricacies of GAN architectures, with a special focus on the GauGAN model, which excels in generating realistic images from semantic layouts.
The course begins with an introduction to the fundamental concepts of GANs, followed by hands-on sessions where you’ll implement and train your own GAN models using Python and Keras. You will learn how to leverage Google Colab for efficient model training, taking advantage of its powerful GPU acceleration to speed up your development process.
A significant portion of the course is dedicated to understanding and implementing various loss functions, including Feature Matching Loss and VGG Feature Matching Loss, which are crucial for enhancing the quality of generated images. You will also explore techniques for optimizing GAN performance and generating visually stunning results.
In addition to technical skills, the course emphasizes practical applications. You’ll work on real-world projects, generating images from semantic layouts and evaluating the results. By the end of the course, you’ll have a portfolio of impressive projects that showcase your expertise in advanced image generation techniques.
This course is ideal for aspiring data scientists, machine learning engineers, and AI enthusiasts who are looking to deepen their understanding of GANs and their applications. Whether you’re aiming to enhance your current skill set or transition into a new career in AI and deep learning, this course will provide you with the tools and knowledge to succeed.
Upon successful completion, you’ll be well-equipped to pursue advanced roles in the field of AI and deep learning. The hands-on experience and practical knowledge gained from this course will significantly improve your job prospects, making you a valuable asset to any organization looking to leverage cutting-edge image generation technologies.
Enroll now and take the first step towards mastering GANs and advancing your career in the exciting world of AI and image generation!
Course Curriculum
Chapter 1: Fundamentals
Lecture 1: Introduction
Lecture 2: About this Project
Lecture 3: Applications
Lecture 4: Job Opportunities
Lecture 5: Why Python, Keras, and Google Colab?
Chapter 2: Model Development, Training and Inference
Lecture 1: Set up the working directory
Lecture 2: What is inside facades_data?
Lecture 3: What is inside code.ipynb?
Lecture 4: Launch the code
Lecture 5: Enable the GPU
Lecture 6: Mount Google Drive
Lecture 7: Installing Libraries
Lecture 8: Configures environment
Lecture 9: Importing libraries
Lecture 10: Dataset Path and Data Split Ratio
Lecture 11: List of Image Files and Shuffling
Lecture 12: Splitting the dataset
Lecture 13: Information about our dataset
Lecture 14: Setting parameters
Lecture 15: Comprehensive preprocessing
Lecture 16: Loading and preparing both the training and validation datasets
Lecture 17: Examining the shapes
Lecture 18: Visualizing sample segmentation maps and real images
Lecture 19: SPADE layer
Lecture 20: Residual Block with SPADE layers
Lecture 21: Implementation of the Gaussian Sampler
Lecture 22: Creating a downsampling block
Lecture 23: Creates an encoder model
Lecture 24: Generator model
Lecture 25: Discriminator model
Lecture 26: Generator loss function
Lecture 27: KL Divergence loss function
Lecture 28: Define Feature Matching Loss
Lecture 29: Define VGG Feature Matching Loss
Lecture 30: Loss function for the discriminator
Lecture 31: Custom Keras callback
Lecture 32: Defines the GauGAN model
Lecture 33: Instantiating and training the GauGAN model
Lecture 34: Function to visualize the training progress
Lecture 35: Visualize the training progress
Lecture 36: Visual assessment
Instructors
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Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML
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