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)

Diagnosing a disease

For probabilistic modeling, experimental libraries abound. Running probabilistic networks can be much slower than algorithmic (non-algorithmic) approaches, which until not long ago rendered them impractical for anything but very small datasets. In fact, most of the tutorials and examples relate to toy datasets.

However, this has changed in recent years due to faster hardware and variational inference. With TensorFlow Probability, it is often straightforward to define architectures, losses, and layers, even with probabilistic sampling with full GPU support, and state-of-the-art implementations that support fast training.

In this recipe, we'll implement an application in healthcare – we'll diagnose a disease.

Getting ready

We already have scikit-learn and TensorFlow installed from previous chapters.

For this recipe, we'll need tensorflow-probability as well:

pip install tensorflow-probability

Now that tensorflow-probability is installed, we&apos...