Generative Adversarial Networks (GANs) Specialization
The Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
Skills You Will Gain
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Adversarial Autoencoder (AAE)
Variational Auto Encoder (VAE)
Conditional GAN (CGAN)
Dual GAN (DGAN)
Stack GAN (StackGAN)
Cycle GAN (CycleGan)
Superresolution GAN (SRGAN)
Deep convolutional GAN (DCGAN)
This course includes
- 1 : 1 Session
- 100% Placement Assistance
- 8 Weeks
- Real TIme Project Training
Syllabus Overview
Generative Adversarial Networks (GANs) Specialization
Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.
In this course, you will: Learn about GANs and their applications; Understand the intuition behind the fundamental components of GANs; Explore and implement multiple GAN architectures; Build conditional GANs capable of generating examples from determined categories.
Week 1: Intro to GANs
- Learn about GANs and their applications, understand the intuition behind the basic components of GANs, and build your very own GAN using PyTorch.
Week 2: Deep Convolutional GAN
- Build a more sophisticated GAN using convolutional layers. Learn about useful activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images.
Week 3: Wasserstein GANs with Normalization
- Reduce instances of GANs failure due to imbalances between the generator and discriminator by learning advanced techniques such as WGANs to mitigate unstable training and mode collapse with a W-Loss and an understanding of Lipschitz Continuity.
Week 4: Conditional and Controllable GANs
- Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories.
In this course, you will: Assess the challenges of evaluating GANs and compare different generative models; Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs; Identify sources of bias and the ways to detect it in GANs; Learn and implement the techniques associated with the state-of-the-art StyleGANs.
Week 1: GAN Evaluation
- Understand the challenges of evaluating GANs, learn about the advantages and disadvantages of different GAN performance measures, and implement the Fréchet Inception Distance (FID) method using embeddings to assess the accuracy of GANs.
Week 2: GAN Disadvantages and Bias
- Find out the disadvantages of GANs when compared to other generative models, discover the pros/cons of these models — plus, learn about the many places where bias in machine learning can come from, why it’s important, and an approach to identify it in GANs.
Week 3: StyleGAN and Advancements
- Understand how StyleGAN improves upon previous models and implement the components and the techniques associated with StyleGAN, currently the most state-of-the-art GAN with powerful capabilities
In this course, you will: Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity; Leverage the image-to-image translation framework and identify applications to modalities beyond images; Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa); Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures; Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one.
Week 1: GANs for Data Augmentation and Privacy Preservation
- Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity
- Improve your downstream AI models with GAN-generated data
Week 2: Image-to-Image Translation
- Leverage the image-to-image translation framework and identify extensions, generalizations, and applications of this framework to modalities beyond images
- Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images to map routes (and vice versa) with advanced U-Net generator and PatchGAN discriminator architectures
Week 3: Image-to-Image Unpaired Translation
- Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures
- Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one
In this course you will:
– Deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure.
– Establish procedures to mitigate model decay and performance drops.
– Establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system.Model Serving Introduction
- Learn how to make your ML model available to end-users and optimize the inference process.
Model Serving Patterns and Infrastructures
- Learn how to serve models and deliver batch and real-time inference results by building scalable and reliable infrastructure.
Model Management and Delivery
- Learn how to implement ML processes, pipelines, and workflow automation that adhere to modern MLOps practices, which will allow you to manage and audit your projects during their entire lifecycle.
Model Monitoring and Logging
- Establish procedures to detect model decay and prevent reduced accuracy in a continuously operating production system.
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