Book Image

Artificial Intelligence with Python Cookbook

By : Ben Auffarth
Book Image

Artificial Intelligence with Python Cookbook

By: Ben Auffarth

Overview of this book

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.
Table of Contents (13 chapters)

Generating images

Adversarial learning with GANs, introduced by Ian Goodfellow and others in 2014, is a framework for fitting the distributions of a dataset by pairing two networks against each other in a way that one model generates examples and the others discriminate, whether they are real or not. This can help us to extend our dataset with new training examples. Semi-supervised training with GANs can help achieve higher performance in supervised tasks while using only small amounts of labeled training examples.

The focus of this recipe is implementing a Deep Convolutional Generative Adversarial Network (DCGAN) and a discriminator on the MNIST dataset, one of the best-known datasets, consisting of 60,000 digits between 0 and 9. We'll explain the terminology and background in the How it works... section.

Getting ready

We don't need any special libraries for this recipe. We'll use TensorFlow with Keras, NumPy, and Matplotlib, all of which we've seen earlier. For saving...