Upscaling, Training, Commoning features a lively account of the 10-year exploration by STEALTH.unlimited into what it means to engage with the glimpses of a world that has become conceivable in the cracks of the Global Financial Crisis (2008). Ultimately, this book is about devising methods for imagining and enacting other worlds. The perspective for this is situated between spatial practice and emerging commons, between an already plundered future and the politics of possibility, between reflection and future fiction. The book traces the moves STEALTH.unlimited (Ana Dzokić and Marc Neelen) have taken to reframe practice, to rethink economy and create urban commons. Its six-part sequence moves from critique to commitment, but along the way reveals an unsettling systemic rejection - by current politics and economy - of the urgency to upscale and 'train' for a world beyond the capitalist urban condition. The book leads us thus to a crucial question: how to construct our alternatives on the ruins of the broken system? By including personal accounts from emerging alternative voices from literature, economy and urban politics from across Europe, it expands this quest beyond the field of architecture and art.

This book directly links the notion of the commons with different design praxes, and explores their social, cultural, and ecological ramifications. It draws out material conditions in four areas of design interest: social design, commons and culture, ecology and transdisciplinary design. As a collection of positions, the diversity of arguments advances the understanding of the commons as both concepts and modes of thinking, and their material translation when contextualised in the domain of design questions. In other words, it moves abstract social science concepts towards concrete design debates. This text appeals to students, researchers and practitioners working on design in architecture, architecture theory, urbanism, and ecology.

Available Open Access under CC-BY-NC licence. Reporting on the innovative, transdisciplinary research on sustainable urbanisation undertaken by Mistra Urban Futures, a highly influential research centre based in Sweden (2010-19), this book builds on the Policy Press title Rethinking Sustainable Cities to make a significant contribution to evolving theory about comparative urban research. Highlighting important methodological experiences from across a variety of diverse contexts in Africa and Europe, this book surveys key experiences and summarises lessons learned from the Mistra Urban Futures' global research platforms. It demonstrates best practice for developing and deploying different forms of transdisciplinary co-production, covering topics including neighbourhood transformation and housing justice, sustainable urban and transport development, urban food security and cultural heritage.

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

From one of the most respected names in business and leadership, a rare look at the specifics of how great leaders achieve "common purpose" and success within their organizations. What is common purpose? It is that rare, almost-palpable experience that happens when a leader coalesces a group, team or community into a creative, dynamic, brave and nearly invincible we. It happens the moment the organization's values, tools, objectives and hopes are internalized in a way that enables people to work tirelessly toward a goal. Common purpose is rarely achieved. But Kurtzman has observed that when a leader is able to bring it about, the results are outsized, measurable and inspiring. Based on Kurtzman's all-new interviews with more than 50 leaders, including Ron Sargent, Ilene Lang, Micky Arison, Simon Cooper, Joel Klein, Janet Field, Steve Wynn, Shivan Subramaniam, Michael Dell, Richard Boyatzis, Tom Kelley, Michael Milken, and Warren Bennis Contains research on leadership Kurtzman has conducted during his years at The New York Times, the Harvard Business Review, Booz & Company, as well as with PricewaterhouseCoopers, Mercer, and Korn/Ferry Based on all new interviews with some of the most dynamic, successful, and enduring leaders, Common Purpose sheds new light on the meaning of leadership, the crucial qualities of leaders, and most importantly, how to lead.

As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way. You'll quickly understand how to successfully navigate your way through typical ML interviews. This guide shows you how to: Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positions Assess your interests and skills before deciding which ML role(s) to pursue Evaluate your current skills and close any gaps that may prevent you from succeeding in the interview process Acquire the skill set necessary for each machine learning role Ace ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questions Prepare for interviews in statistics and machine learning theory by studying common interview questions