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)

Localizing objects

Object detection refers to identifying objects of particular classes in images and videos. For example, in self-driving cars, pedestrians and trees have to be identified in order to be avoided.

In this recipe, we'll implement an object detection algorithm in Keras. We'll apply it to a single image and then to our laptop camera. In the How it works... section, we'll discuss the theory and more algorithms for object detection.

Getting ready

For this recipe, we'll need the Python bindings for the Open Computer Vision Library (OpenCV) and scikit-image:

!pip install -U opencv-python scikit-image

As our example image, we'll download an image from an object detection toolbox:

def download_file(url: str, filename='demo.jpg'):
import requests
response = requests.get(url)
with open(filename, 'wb') as f: