The Medieval Python

In To Win or Lose a Medieval Battle, Andrew Villalon and Donald Kagay provide a full treatment of one of the major battles of the Hundred Years War. The authors have investigated the background to Náj

Natasha O'Hear considers seven different visualisations of all or part of the Book of Revelation across a range of different media, from illuminated manuscripts, to tapestries, to altarpieces to pain

The 15 papers of this beautifully produced volume were first presented at a conference called New Perspectives on the Chantilly Codex, held in September 2001 in Tours, France. Centering on the rich co

Reflecting the focus but also range of their honorand's work in medieval canon law in the era before Gratian, the essays in this volume explore the creation and transmission of canonical texts and the

Explores the historical development of rifles through the centuries and an A-Z visual directory with key specifications for each weapon.

As a convenient source of wide-ranging critical opinion on early literature, this series contains excerpts from criticism through the ages on the works of philosophers, poets and playwrights, politica

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientis

(FAQ). Monty Python FAQ burrows into the pulsing heart of the Python's corpus of work and emerges, bloody but unbowed, with a book that tells you everything you positively, absolutely need to know a

This book introduces numerical programming using Python and C/C++, emphasizing methods used in physics and engineering. Its helps readers develop the ability to navigate relevant algorithms, knowledge

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning