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)

Recognizing clothing items

A popular example of image classification is the MNIST dataset, which contains digits from 0 to 9 in different styles. Here, we'll use a drop-in replacement, called Fashion-MNIST, consisting of different pieces of clothing.

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples:

Here are a few examples from the dataset:

In this recipe, we'll recognize clothing items with different models – we'll start with generic image features (Difference of Gaussians, or DoG) and a support vector machine; then we'll move on to a feedforward Multilayer Perceptron (MLP); then we'll use a Convolutional Neural Network (ConvNet); and finally, we'll look at transfer learning with MobileNet.

Getting ready

Before we can start, we have to install a library. In this recipe, we'll use scikit-image, a library...