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

Generative Adversarial Networks 101

As shown in the following diagram, the Generative Adversarial Networks, popularly known as GANs, have two models working in sync to learn and train on complex data such as images, videos or audio files:

Intuitively, the generator model generates data starting from random noise but slowly learns how to generate more realistic data. The generator output and the real data is fed into the discriminator that learns how to differentiate fake data from real data.


Thus, both generator and discriminator play an adversarial game where the generator tries to fool the discriminator by generating as real data as possible, and the discriminator tries not to be fooled by identifying fake data from real data, thus the discriminator tries to minimize the classification loss. Both the models are trained in a lockstep fashion.

Mathematically, the generative model 

learns the probability distribution 

 such that the discriminator

 is unable to identify between the probability...