African Americans, Hispanics, and Native Americans represent 27 percent of the United States population, yet they constitute less than 11 percent of nurses and 8 percent of physicians. In Racism in Health Care: Alive and Well, author Marie Edwige Seneque discusses how this long history of racism continues to shortchange the national recruitment and retention of minority health care providers which contributes to racial and ethnic health disparities.

Racism in Health Care: Alive and Well dismantles and examines the many layers involved in the complex health care system including physician attitude, nursing in the twenty-first century, the lack of cultural competence, and the belief that the "r" word should remain unspoken.

During extensive research, Seneque, a registered nurse, compiled already existing data regarding racial and ethnic disparities. She communicates her findings in a simplified, easy-to-read format. In Racism in Health Care: Alive and Well, she exposes the glaring disparities for minorities in the health care delivery system and why racism is alive and well in the United States.

African Americans, Hispanics, and Native Americans represent 27 percent of the United States population, yet they constitute less than 11 percent of nurses and 8 percent of physicians. In Racism in Health Care: Alive and Well, author Marie Edwige Seneque discusses how this long history of racism continues to shortchange the national recruitment and retention of minority health care providers which contributes to racial and ethnic health disparities.

Racism in Health Care: Alive and Well dismantles and examines the many layers involved in the complex health care system including physician attitude, nursing in the twenty-first century, the lack of cultural competence, and the belief that the "r" word should remain unspoken.

During extensive research, Seneque, a registered nurse, compiled already existing data regarding racial and ethnic disparities. She communicates her findings in a simplified, easy-to-read format. In Racism in Health Care: Alive and Well, she exposes the glaring disparities for minorities in the health care delivery system and why racism is alive and well in the United States.

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding of deep learning using Golang Build complex neural network models using Go libraries and Gorgonia Take your deep learning model from design to deployment with this handy guide Book DescriptionGo is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems. What you will learn Explore the Go ecosystem of libraries and communities for deep learning Get to grips with Neural Networks, their history, and how they work Design and implement Deep Neural Networks in Go Get a strong foundation of concepts such as Backpropagation and Momentum Build Variational Autoencoders and Restricted Boltzmann Machines using Go Build models with CUDA and benchmark CPU and GPU models Who this book is forThis book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.