Deep Equality In An Era Of Religious Diversity

Download Deep Equality In An Era Of Religious Diversity

While religious conflict receives plenty of attention, the everyday negotiation of religious diversity does not. Questions of how to accommodate religious minorities and of the limits of tolerance resonate in a variety of contexts and have become central preoccupations for many Western democracies. What might we see if we turned our attention to the positive narratives and success stories of the everyday working out of religious difference? Rather than 'tolerance' and 'accommodation', and through the stories of ordinary people, this book traces deep equality, which is found in the respect, humour, and friendship of seemingly mundane interactions. Deep Equality in an Era of Religious Diversity shows that the telling of such stories can create an alternative narrative to that of diversity as a problem to be solved. It explores the non-event, or micro-processes of interaction that constitute the foundation for deep equality and the conditions under which deep equality emerges, exists, and sometimes flourishes.

Through a systematic search for and examination of such narratives, Lori G. Beaman demonstrates the possibility of uncovering, revealing, and recovering deep equality―a recovery that is vital to living in an increasingly diverse society. In achieving deep equality, identities are fluid, shifting in importance and structure as social interaction unfolds. Rigid identity imaginings, especially religious identities, block our vision to the complexities of social life and press us into corners that trap us in identities that we often ourselves do not recognize, want, or know how to escape. Although the focus of this study is deep equality and its existence and persistence in relation to religious difference, deep equality is located beyond the realm of religion. Beaman draws from the work of those whose primary focus is not in fact religion, and who are doing their own 'deep equality' work in other domains, illustrating especially why equality matters. By retelling and exploring stories of negotiation it is possible to reshape our social imaginary to better facilitate what works, which varies from place to place and time to time.

Deep Learning With Python: A Hands-on Introduction

Download Deep Learning With Python: A Hands-on Introduction

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.

This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.

Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.

What You Will Learn

Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe

Gain the fundamentals of deep learning with mathematical prerequisites

Discover the practical considerations of large scale experiments

Take deep learning models to production

Who This Book Is For

Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Deep Learning With Hadoop

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Key Features Get to grips with the deep learning concepts and set up Hadoop to put them to use. Implement and parallelize deep learning models on Hadoop's YARN framework. A comprehensive tutorial to distributed deep learning with Hadoop Book Description Deep Learning involves extracting features and insights from multiple layers of the data. This book will teach you how to deploy the deep learning networks with Hadoop. Starting with understanding what deep learning is and what the various models associated with deep learning are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with Hadoop. The book will also show you how you can implement and parallelize Deep Belief Networks, CNN, RNN, RBM and much more using the popular deep learning library deeplearning4j. Get in depth mathematical explanations, visual representations to understand the implementation of Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you how you can implement image classification, audio processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy deep learning in distributed systems using Hadoop What you will learn Explore Deep Learning and various models associated with it. Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it Implement Convolutional Neural Network (CNN) with deeplearning4j Delve into the implementation of Restricted Boltzmann Machines (RBM) Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN) Get hands on practice of deep learning and their implementation with Hadoop.

Fundamentals Of Deep Learning: Designing Next-generation Machine Intelligence Algorithms

Download Fundamentals Of Deep Learning: Designing Next-generation Machine Intelligence Algorithms

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field.

Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. For the rest of us however, deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started.