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GANs

  • Course Code: Artificial Intelligence - GANs
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deep fakes, GANs are a huge step forward in deep learning systems.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level GANs for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deep fakes, GANs are a huge step forward in deep learning systems. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

GANs teaches you to build and train your own Generative Adversarial Networks. You’ll start by creating simple generator and discriminator networks that are the foundation of GAN architecture. Then, following numerous hands-on examples, you’ll train GANs to generate high-resolution images, image-to-image translation, and targeted data generation. Along the way, you’ll find pro tips for making your system smart, effective, and fast. 

Working in a hands-on learning environment, led by our GAN expert instructor, students will learn about and explore: 

  • you will explore incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the “real thing.” By pitting two neural networks against each other—one to generate fakes and one to spot them—GANs rapidly learn to produce photo-realistic faces and other media objects.  
  • With the potential to produce stunningly realistic animations or shocking deep fakes, GANs are a huge step forward in deep learning systems. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Building your first GAN 
  • Handling the progressive growing of GANs 
  • Practical applications of GANs 
  • Troubleshooting your system 

Audience & Pre-Requisites 

This course is geared for those who wants to create simple generator and discriminator networks that are the foundation of GAN architecture 

Pre-Requisites:  Students should have  

  • For data professionals with intermediate Python skills  
  • The basics of deep learning–based image processing.  
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to GANs free 
  • What are Generative Adversarial Networks? 
  • How do GANs work? 
  • GANs  
  • Why study GANs? 
  1. Intro to generative modeling with autoencoders 
  • Introduction to generative modeling 
  • How do autoencoders function on a high level? 
  • What are autoencoders to GANs? 
  • What is an autoencoder made of? 
  • Usage of autoencoders 
  • Unsupervised learning 
  • Code is life 
  • Why did we try a GAN? 
  1. Your first GAN: Generating handwritten digits 
  • Foundations of GANs: Adversarial training 
  • The Generator and the Discriminator 
  • GAN training algorithm 
  • Tutorial: Generating handwritten digits 
  1. Deep Convolutional GAN 
  • Convolutional neural networks 
  • Brief history of the DCGAN 
  • Batch normalization 
  • normalization 
  • 4.4. Tutorial: Generating handwritten digits with DCGAN 
  1. Training and common challenges: Ganing for success 
  • Evaluation 
  • Training challenges 
  • Summary of game setups 
  • Training hacks 
  1. Progressing with GANs 
  • Latent space interpolation 
  • They grow up so fast 
  • normalization in the generator 
  • Summary of key innovations 
  • TensorFlow Hub and hands-on 
  • Practical applications 
  1. Semi-Supervised GAN 
  • Introducing the Semi-Supervised GAN 
  • Tutorial: Implementing a Semi-Supervised GAN 
  • Comparison to a fully supervised classifier 
  1. Conditional GAN 
  • Motivation 
  • What is Conditional GAN? 
  • Tutorial: Implementing a Conditional GAN 
  1. Cycle GAN 
  • Image-to-image translation 
  • Cycle-consistency loss: There and back aGAN 
  • Adversarial loss 
  • Identity loss 
  • Architecture 
  • Object-oriented design of GANs 
  • Tutorial: Cycle GAN 
  • Expansions, augmentations, and applications 
  1. Adversarial examples 
  • Context of adversarial examples 
  • Lies, damned lies, and distributions 
  • Use and abuse of training 
  • Signal and the noise 
  • Not all hope is lost 
  • Adversaries to GANs 
  1. Practical applications of GANs 
  • GANs in medicine 
  • GANs in fashion 
  1. Looking ahead 
  • Ethics 
  • GAN innovations 
  • Further reading 
  • Looking back and closing thoughts 
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