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)
Probabilistic Modeling

This chapter is about uncertainty and probabilistic approaches. State-of-the-art machine learning systems have two significant shortcomings.

First of all, they can be overconfident (or sometimes underconfident) in their prediction. In practice, given noisy data, even if we observe the best practice of cross-validating with unseen datasets, this confidence might not be warranted. Especially in regulated or sensitive environments, such as in financial services, healthcare, security, and intelligence, we need to be very careful about our predictions and how accurate they are.

Secondly, the more complex a machine learning system is, the more data we need to fit our model, and the more severe the risk of overfitting.

Probabilistic models are models that produce probabilistic inferences using stochastic sampling techniques. By parametrizing distributions and inherent...