Examining the historical context of healthcare whilst focusing on building a more just, equitable world, this book proposes a radical imagination for nursing and presents possibilities for speculative futures embracing queer, feminist, posthuman, and abolitionist frames. Bringing together radical and emancipatory perspectives from an international selection of authors, this book reflects on the realities created by the COVID-19 pandemic, recognizing that our situation is not new but the result of ongoing hegemonies and injustices. The authors attend to the history of nursing and related institutions, examining the assumptions, ideologies, and discourses that shape the discipline and its place within healthcare. They explore the impact of this context on contemporary nursing and look at alternative visions for the future. The final section specifically focuses on ways that we can move forward. Envisioning new possibilities for nursing, this innovative volume is a vital resource for practitioners, scholars and students keen to promote social justice within and without nursing. It is an important contribution to nursing theory, philosophy and history.

Humans learn best from feedback-we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you'll need to implement it into your own projects. Key features * Structuring problems as Markov Decision Processes * Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them * Applying reinforcement learning algorithms to real-world problems Audience You'll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that's not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.