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)

Classifying in scikit-learn, Keras, and PyTorch

In this section, we'll be looking at data exploration, and modeling in three of the most important libraries. Therefore, we'll break things down into the following sub-recipes:

  • Visualizing data in Seaborn
  • Modeling in scikit-learn
  • Modeling in Keras
  • Modeling in PyTorch

Throughout these recipes and several subsequent ones, we'll focus on covering first the basics of the three most important libraries for AI in Python: scikit-learn, Keras, and PyTorch. Through this, we will introduce basic and intermediate techniques in supervised machine learning with deep neural networks and other algorithms. This recipe will cover the basics of these three main libraries in machine learning and deep learning.

We'll go through a simple classification task using scikit-learn, Keras, and PyTorch in turn. We'll run both of the deep learning frameworks in offline mode.

These recipes are for introducing the basics of the three libraries...