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Cover: 9781484270912 | Generating a New Reality | Micheal Lanham | Taschenbuch | Paperback
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Generating a New Reality
From Autoencoders and Adversarial Networks to Deepfakes
Taschenbuch von Micheal Lanham
Sprache: Englisch

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Beschreibung
The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality.
In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects.
By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new.
What You Will Learn
Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)
Explore variations of GAN
Understand the basics of other forms of content generation
Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2

Who This Book Is For
Machine learning developers and AI enthusiasts who want to understand AI content generation techniques
The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality.
In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects.
By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new.
What You Will Learn
Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)
Explore variations of GAN
Understand the basics of other forms of content generation
Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2

Who This Book Is For
Machine learning developers and AI enthusiasts who want to understand AI content generation techniques
Über den Autor
Micheal Lanham is a proven software and tech innovator with more than 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence (AI), GIS, and machine learning (ML) applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He is an avid educator, has written more than eight books covering game development, extended reality, and AI, and teaches at meetups and other events. Micheal also likes to cook for his large family in his hometown of Calgary, Canada.
Zusammenfassung

Explores variations of content generation AI, not just GANs

Uses free online resources (such as Google Collaboratory) that allow users to train AI with GPUs on the cloud

Is developer-focused, with lots of hands-on exercises (readers are encouraged to open the examples and run them while reading through the book)

Inhaltsverzeichnis
Chapter 1: Deep Learning Perceptron
Chapter Goal: In this chapter we introduce the basics of deep learning from the perceptron to multi-layer perceptron.
No of pages: 30
Sub -Topics
1. Understanding deep learning and supervised learning.
1. Using the perceptron for supervised learning.
2. Constructing a multilayer perceptron.
3. Discover the basics of activation, loss, optimization and back propagation for problems of regression and classification.
Chapter 2: Unleashing Autoencoders and Generative Adversarial Networks
Chapter Goal: This chapter introduces the autoencoder and GAN for simple content generation. Along the way we also learn about using convolutional network layers for better feature extraction.
No of pages: 30
Sub - Topics
1. Why we need autoencoders and how they function.
2. Improving on the autoencoder with convolutional network layers.
3. Generating content with the GAN.
4. Explore methods for improving on the vanilla GAN.
Chapter 3: Exploring the Latent Space
Chapter Goal: In this chapter we discover the latent space in AI. What it means to move through the AI latent space using variational autoencoders and conditional GANs.
No of pages : 30
Sub - Topics:
1. Understanding variation and the variational autoencoder.
2. Exploring the latent space with a VAE.
3. Extending a GAN to be conditional.
4. Generate interesting foods using a conditional GAN.
Chapter 4: GANs, GANs and More GANs
Chapter Goal: In this chapter we begin uncovering the vast variations in GANs and their applications. We start with basics like the double convolution GAN and work up to the Stack and Progressive GANs.
No of pages: 30
Sub - Topics:
1. Look at samples from the many variations of GANs.
2. Setup and use a DCGAN.
3. Understand how a StackGAN works.
4. Work with and use a ProGAN.

Chapter 5: Image to Image Translation with GANs

Covers: Pix2Pix and DualGAN, side projects for understanding with ResNET and UNET, advanced network architectures for image classification/generation



Chapter 6: Translating Images with Cycle Consistency

Covers: Cycle consistency loss and the CycleGAN, BiCycleGAN and StarGAN

Chapter 7: Styling with GANs

Covers: StyleGAN, Attention and the Self-attention GAN with a look at DeOldify

Chapter 8: Developing DeepFakes
Chapter Goal: DeepFakes are taking the world by storm and in this chapter, we explore how to use a DeepFakes project.
No of pages: 30
1. Learn how to isolate faces or other points of interest in images or video.
2. Extract and replace faces from images or video.
3. Use DeepFakes GAN to generate facial images based on input image.
4. Put it all together and allow the user to generate their own DeepFake video.

Chapter 9: Uncovering Adversarial Latent Autoencoders
Chapter Goal: GANs are not the only technique that allows for content manipulation and generations. In this chapter we look at the ALAE method for generating content.
No of pages:
1. Look at how to extend autoencoders for adversarial learning.
2. Understanding how AE can be used to explore the latent space in data.
3. Use ALAE to generate conditional content.
4. Revisit our previous foods example and see what new foods we can generate.
Chapter 10: Video Content with First Order Model Motion
Chapter Goal: In this chapter we explore a new technique for animating static images called First Order Model Motion. At the end of this chapter we will use this technique to create avatars for Skype or Zoom.
No of pages: 30
1. Discover the basic of First Order Model Motion, what it is and how it works.
2. Be able to apply FOMM to a number of static image datasets for various applications.
3. Use the project Avatarify for generating real-time avatars from static avatars.
4. Use Avatarify real-time in applications like Zoom or Skype.
Details
Erscheinungsjahr: 2021
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 340
Inhalt: xvii
321 S.
120 s/w Illustr.
321 p. 120 illus.
ISBN-13: 9781484270912
ISBN-10: 1484270916
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Lanham, Micheal
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 19 mm
Von/Mit: Micheal Lanham
Erscheinungsdatum: 16.07.2021
Gewicht: 0,641 kg
preigu-id: 119748358
Über den Autor
Micheal Lanham is a proven software and tech innovator with more than 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence (AI), GIS, and machine learning (ML) applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He is an avid educator, has written more than eight books covering game development, extended reality, and AI, and teaches at meetups and other events. Micheal also likes to cook for his large family in his hometown of Calgary, Canada.
Zusammenfassung

Explores variations of content generation AI, not just GANs

Uses free online resources (such as Google Collaboratory) that allow users to train AI with GPUs on the cloud

Is developer-focused, with lots of hands-on exercises (readers are encouraged to open the examples and run them while reading through the book)

Inhaltsverzeichnis
Chapter 1: Deep Learning Perceptron
Chapter Goal: In this chapter we introduce the basics of deep learning from the perceptron to multi-layer perceptron.
No of pages: 30
Sub -Topics
1. Understanding deep learning and supervised learning.
1. Using the perceptron for supervised learning.
2. Constructing a multilayer perceptron.
3. Discover the basics of activation, loss, optimization and back propagation for problems of regression and classification.
Chapter 2: Unleashing Autoencoders and Generative Adversarial Networks
Chapter Goal: This chapter introduces the autoencoder and GAN for simple content generation. Along the way we also learn about using convolutional network layers for better feature extraction.
No of pages: 30
Sub - Topics
1. Why we need autoencoders and how they function.
2. Improving on the autoencoder with convolutional network layers.
3. Generating content with the GAN.
4. Explore methods for improving on the vanilla GAN.
Chapter 3: Exploring the Latent Space
Chapter Goal: In this chapter we discover the latent space in AI. What it means to move through the AI latent space using variational autoencoders and conditional GANs.
No of pages : 30
Sub - Topics:
1. Understanding variation and the variational autoencoder.
2. Exploring the latent space with a VAE.
3. Extending a GAN to be conditional.
4. Generate interesting foods using a conditional GAN.
Chapter 4: GANs, GANs and More GANs
Chapter Goal: In this chapter we begin uncovering the vast variations in GANs and their applications. We start with basics like the double convolution GAN and work up to the Stack and Progressive GANs.
No of pages: 30
Sub - Topics:
1. Look at samples from the many variations of GANs.
2. Setup and use a DCGAN.
3. Understand how a StackGAN works.
4. Work with and use a ProGAN.

Chapter 5: Image to Image Translation with GANs

Covers: Pix2Pix and DualGAN, side projects for understanding with ResNET and UNET, advanced network architectures for image classification/generation



Chapter 6: Translating Images with Cycle Consistency

Covers: Cycle consistency loss and the CycleGAN, BiCycleGAN and StarGAN

Chapter 7: Styling with GANs

Covers: StyleGAN, Attention and the Self-attention GAN with a look at DeOldify

Chapter 8: Developing DeepFakes
Chapter Goal: DeepFakes are taking the world by storm and in this chapter, we explore how to use a DeepFakes project.
No of pages: 30
1. Learn how to isolate faces or other points of interest in images or video.
2. Extract and replace faces from images or video.
3. Use DeepFakes GAN to generate facial images based on input image.
4. Put it all together and allow the user to generate their own DeepFake video.

Chapter 9: Uncovering Adversarial Latent Autoencoders
Chapter Goal: GANs are not the only technique that allows for content manipulation and generations. In this chapter we look at the ALAE method for generating content.
No of pages:
1. Look at how to extend autoencoders for adversarial learning.
2. Understanding how AE can be used to explore the latent space in data.
3. Use ALAE to generate conditional content.
4. Revisit our previous foods example and see what new foods we can generate.
Chapter 10: Video Content with First Order Model Motion
Chapter Goal: In this chapter we explore a new technique for animating static images called First Order Model Motion. At the end of this chapter we will use this technique to create avatars for Skype or Zoom.
No of pages: 30
1. Discover the basic of First Order Model Motion, what it is and how it works.
2. Be able to apply FOMM to a number of static image datasets for various applications.
3. Use the project Avatarify for generating real-time avatars from static avatars.
4. Use Avatarify real-time in applications like Zoom or Skype.
Details
Erscheinungsjahr: 2021
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 340
Inhalt: xvii
321 S.
120 s/w Illustr.
321 p. 120 illus.
ISBN-13: 9781484270912
ISBN-10: 1484270916
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Lanham, Micheal
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 19 mm
Von/Mit: Micheal Lanham
Erscheinungsdatum: 16.07.2021
Gewicht: 0,641 kg
preigu-id: 119748358
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