Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

Chapter 16. Generative Adversarial Networks

Generative models are trained to generate more data similar to the one they are trained on, and adversarial models are trained to distinguish the real versus fake data by providing adversarial examples.

The Generative Adversarial Networks (GAN) combine the features of both the models. The GANs have two components: 

  • A generative model that learns how to generate similar data
  • A discriminative model that learns how to distinguish between the real and generated data (from the generative model)

GANs have been successfully applied to various complex problems such as:

  • Generating photo-realistic resolution images from low-resolution images
  • Synthesizing images from the text
  • Style transfer
  • Completing the incomplete images and videos

In this chapter, we shall study the following topics for learning how to implement GANs in TensorFlow and Keras:

  • Generative Adversarial Networks
  • Simple GAN in TensorFlow
  • Simple GAN in Keras
  • Deep Convolutional GAN with TensorFlow and Keras...