This book presents and argues for a suitably articulated version of consensualism as a form of Kantian moral theory with an ability to powerfully illuminate the moral intuitions to which Kantian and utilitarian theories have traditionally appealed.

One of the principle weaknesses of the non-consequentialist moral theories is that they fail to adequately address the intuitions to which consequentialist views appeal for their strength. For this reason, those who expect a moral theory to provide insight into these intuitions continue to be drawn to consequentialism, despite well-known ways in which its theories can be deeply counter-intuitive.
This study shows how this challenge can be met by non-consequentialists. Articulating and defending a form of Scanlonian contractualism, called Consensualism, Kumar argues that consensualism also has the resources to powerfully illuminate those commonsense moral intuitions to which only consequentialism normally appeals.

Existing human beings stand in a unique relationship of asymmetrical influence over future generations. Our choices now can settle whether there are any human beings in the further future; how many will exist; what capacities and abilities they might have; and what the character of the natural world they inhabit is like. This volume, with contributions from both new voices and prominent, established figures in moral and political philosophy, examines three generally underexplored themes concerning morality and our relationship to future generations. First, would it be morally wrong to allow humanity to go extinct? Or do we have moral reasons to try and ensure that humanity continues into the indefinite future? Second, if humanity is to continue into the future, how many people should there be? And is it morally important whether they have lives that are of high quality or are just barely worth living? And third, how can we best make sense of the intuitive idea that by not taking action on climate change and preserving natural resources, we are in some way wronging future generations? This book was originally published as a special issue of the Canadian Journal of Philosophy.

Existing human beings stand in a unique relationship of asymmetrical influence over future generations. Our choices now can settle whether there are any human beings in the further future; how many will exist; what capacities and abilities they might have; and what the character of the natural world they inhabit is like. This volume, with contributions from both new voices and prominent, established figures in moral and political philosophy, examines three generally underexplored themes concerning morality and our relationship to future generations. First, would it be morally wrong to allow humanity to go extinct? Or do we have moral reasons to try and ensure that humanity continues into the indefinite future? Second, if humanity is to continue into the future, how many people should there be? And is it morally important whether they have lives that are of high quality or are just barely worth living? And third, how can we best make sense of the intuitive idea that by not taking action on climate change and preserving natural resources, we are in some way wronging future generations? This book was originally published as a special issue of the Canadian Journal of Philosophy.

Football and fascism: the politics of popular culture in Portugal tells the hidden history of football and discusses its political, social and cultural foundations, during the longest running authoritarian regime in Europe. Theoretically grounded on Bourdieu's field theory, and using a multi-scalar methodology, this award-winning research explores the political tensions between the nationalization of sports envisaged by the Portuguese "New State" and the integration of national football in a globalized urban popular culture. Mobilizing unexplored archival sources, and a wide array of primary materials, this groundbreaking work offers new insight on the administrative structures of the corporativist state, the making of an authoritarian cultural program, and the relation between state institutions and civil society. Besides broadening the scope of existing transnational histories of football, this study also puts into question the conventional geographies and political chronologies adopted in sports history.

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch Explore expert techniques and hacks for a variety of machine learning concepts Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book DescriptionMachine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation Choose the best machine learning algorithm to solve your problem Explore kernel learning, neural networks, and time-series analysis Train deep learning models and optimize them for maximum performance Briefly cover Bayesian techniques and sentiment analysis in your NLP solution Implement probabilistic graphical models and causal inferences Measure and optimize the performance of your machine learning models Who this book is forIf you're a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you're an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You'll need some exposure to machine learning to get the best out of this book.

Insightful projects to master deep learning and neural network architectures using Python and Keras Key Features Explore deep learning across computer vision, natural language processing (NLP), and image processing Discover best practices for the training of deep neural networks and their deployment Access popular deep learning models as well as widely used neural network architectures Book DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You'll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you'll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you'll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learn Set up a deep learning development environment on Amazon Web Services (AWS) Apply GPU-powered instances as well as the deep learning AMI Implement seq-to-seq networks for modeling natural language processing (NLP) Develop an end-to-end speech recognition system Build a system for pixel-wise semantic labeling of an image Create a system that generates images and their regions Who this book is forPython Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming