Chapter 1: Getting Started with Image Generation Using TensorFlow
This book focuses on generating images and videos using unsupervised learning with TensorFlow 2. We assume that you have prior experience in using modern machine learning frameworks, such as TensorFlow 1, to build image classifiers with Convolutional Neural Networks (CNNs). Therefore, we will not be covering the basics of deep learning and CNNs. In this book, we will mainly use high level Keras APIs in TensorFlow 2, which is easy to learn. Nevertheless, we assume that you have no prior knowledge of image generation, and we will go through all that is needed to help you get started with it. The first aspect that you need to know about is probability distribution.
Probability distribution is fundamental in machine learning and it is especially important in generative models. Don't worry, I assure you that there aren't any complex mathematical equations in this chapter. We will first learn what probability is and how to use it to generate faces without using any neural networks or complex algorithms.
That's right: with the help of only basic math and NumPy code, you'll learn how to create a probabilistic generative model. Following that, you will learn how to use TensorFlow 2 to build a PixelCNN model in order to generate handwritten digits. This chapter is packed with useful information; you will need to read this chapter before jumping to any other chapters.
In this chapter, we are going to cover the following main topics:
- Understanding probabilities
- Generating faces with a probabilistic model
- Building a PixelCNN model from scratch
