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Applied Social Psychology Understanding and Addressing Social and Practical Problems Third Edition
This textbook serves as an introduction to applied social psychology, which is the branch of the field of social psychology that focuses on understanding social and practical problems and on developing intervention strategies directed at the amelioration of such problems. As editors of this third edition, as with the first two editions, we have two main objectives
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Hands-On Ensemble Learning with Python Build highly optimized ensemble machine learning models using scikit-learn and Keras
Ensembling is a technique for combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.
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Applied Social Psychology
The book “Applied Social Psychology” represents a landmark for the inter-disciplinary and trans-disciplinary approach of social sciences. Authors, researchers and practitioners have joined through studies approached in the field of applicative research and formulated solutions and proposals to the problems approached.
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Mastering Machine Learning with scikit-learn Second Edition
In this book, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and we will learn to measure the performance of machine learning systems.
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Applied Psychology Putting Theory into Practice SECOND EDITION
Th e second edition sees us make some important additions, particularly in terms of updating materials already presented and providing two new chapters, ‘Th e teaching room’ and Th e nature room’. We felt that it was essential that the book refl ected the ever expanding knowledge base from which applied psychologists now draw their inspiration and guidance. To this end, we have included new information and references in every chapter
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OpenCV 3.x with Python By Example Second Edition
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors nor Packt Publishing or its dealers and distributors will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
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Artificial Intelligence for Robotics
The book introduces a novel way to navigate without a map using a literal divide and conquer program that uses the upper part of the room to remember paths, and the lower part of avoid obstacles. The book demonstrates how path planning, decision trees, object classification, and navigation are all part of the same problem set. We finish by giving the robot an artificial personality. The final chapter concludes with thoughts on the future of robots and gives advice on robotics as a career.
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USE OF RISK ANALYSIS IN COMPUTER-AIDED PERSUASION
The book’s papers fall into three major categories. The first group of papers considers fraud and financial systems. Topics such as fraud in insurance claims, and in credit cards are considered, and effective approaches based on genetic algorithms, neural networks, and social network analytics are developed. Approaches for the prevention of money laundering and the mitigation of credit risk are presented.
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UNIVERSAL SMART GRID AGENT FOR DISTRIBUTED POWER GENERATION MANAGEMENT
Renewable energy sources provide an ever increasing amount of the global electric power generation. However, for many regions, even whole countries, the go-to primary renewable energy sources that are available in large quantities are wind and solar radiation, which are highly volatile since they depend on a factor that is not human-controllable: the weather.
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R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
This book will help you test your knowledge and skills, guiding you on how to build easy through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine to perform sentiment analysis on Amazon reviews.
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R Deep Learning Cookbook
This chapter will get you started with deep learning and help you set up your systems to develop deep learning models. The chapter is more focused on giving the audience a heads- up on what is expected from the book and the prerequisites required to go through the book. The current book is intended for students or professionals who want to quickly build a background in the applications of deep learning.
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Deep Learning for Beginners A beginner's guide to getting up and running with deep learning from scratch using Python
This book will serve you well in your learning journey by providing you with several examples, best practices, and fully working code snippets that will give you the understanding you need to apply deep learning in several disciplines that include computer vision, natural language processing, learning representations and more.
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Python Artificial Intelligence Projects for Beginners
This book begins with building your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using effective machine learning techniques: random forest and decision trees.
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Brain Arousal and Information Theory Neural and Genetic Mechanisms
This book is dedicated to Fred Plum, M.D., University Professor Emeritus of Neurology and former Chair of Neurology at Cornell University School of Medicine. Heroically he devoted his efforts to the understanding and amelioration of disorders of human CNS arousal.
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Hands-On Computer Vision with Julia
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
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Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.