Generating a New Reality

About the Book

Chapter 1: Deep Learning PerceptronChapter Goal: In this chapter we introduce the basics of deep learning from the perceptron to multi-layer perceptron.No of pages: 30Sub -Topics1. 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 NetworksChapter 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: 30Sub - 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 SpaceChapter 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 GANsChapter 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: 30Sub - 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 DeepFakesChapter Goal: DeepFakes are taking the world by storm and in this chapter, we explore how to use a DeepFakes project. No of pages: 301. 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 AutoencodersChapter 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.
About the Author: 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.