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Dorogovtsev S: The Nature of Complex
Networks

The Nature of Complex Networks

Series in Computer Vision

This compendium provides a hands-on description of Random Forests. It starts with a consistent introduction of general methods to create, train, and fuse ensembles of decision trees. Instead of limiting the discussion to the general-purpose layout of traditional Random Forests, this book outlines specifications during tree creation and training that are especially well suited to analyze structured data such as images. The theoretical foundations are explained as well as practical and implementation issues. The many possible variations of the Random Forest model are discussed together with their implications on the prediction results to provide insights into the influence of these parameters and their possible side-effects. Last but not least, this unique title provides specific examples of the usage of Random Forests for analysing remote sensing imagery.

Handbook Of Random Forests: Theory And Applications For Remote Sensing

Chapman & Hall/CRC Studies in Informatics Series

Presenting first-order and propositional methods as well as implementation issues, this book provides a state-of-the-art introduction to the automation of logic. It focuses on superposition calculus and related rewriting techniques. It also covers decision procedures for certain theories and includes real-world applications. Along with the relevant theory, each chapter contains implementation methods, historical background, suggestions for further reading, and software. Supplemental materials and resources are provided on the book's website and a solutions manual is available for qualifying instructors.

Automation of Logic

Efficient Signal Processing Solutions

Deep Learning Meets FPGA

Handbook of Optical Ceramics

Using Computational Thinking in Everyday Life

Problem Solving with Python

This practical introduction to the field focuses on the design, architecture, and operation of technologies based on organic compounds and conjugating molecules. It shows the reader how material structure relates to semiconducting properties, and then explores in detail the electronic devices that harness their unique properties. The author also extends the discussion to printed electronics, covering large-area, low-cost film deposition, patterning, processing, and encapsulation. The reader will gain a deep understanding of how the devices work and how the applications are designed and operate.

Science, Technology, Devices and Applications

Organic and Hybrid Electronics

Software Studies

On Software and Sovereignty

The Stack, 10th Anniversary Edition

A Practical Introduction

Membrane Protein Bioinformatics

Written by a data scientist with decades of practical experience on some of the most challenging datasets, this book caters to both novices and professionals working in the fields of data science and machine learning. It enables novices to enter these fields with confidence and ease. There are no formal prerequisites. An introduction to Python is included. The book then proceeds to cover some of the language's most advanced features. The mathematical foundations that follow are meticulously explained. The discussion then proceeds to a practical consideration of data science and machine learning. The material is delivered through case studies developed on real datasets. The reader is invited to follow these case studies, and so develop experience in working with real data. Numerous solved exercises make this work suitable for self-study. The contents of this book have been taught by the author to students of different age groups and levels of experience at Imperial College London and at the Thalesians Intensive Summer School in Artificial Intelligence over the years.

Python, Data Science And Machine Learning: From Scratch To Productivity

Sustainable Computing and Optimization

V2X, ITS & Telematics

Mobile Intelligence for Autonomous Driving Systems

The Emerging Learning Landscape

Adaptive Learning Opportunities provides an introduction to adaptive learning technologies as a way to create personal educational opportunities for students at a variety of stages across a range of areas of study.

Toward a More Personal Education

Adaptive Learning Opportunities

Quantum Computing and Machine Learning for 6G

The Nature of Complex Networks

Informations bibliographiques

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