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  • Authors: Christina L. Davis (2020)

  • This chapter examines Japanese trade policy to explain how economic interests and domestic political institutions have supported the resilience of free trade policies in Japan. The mercantilist ideas and the reactive state model of past years have been replaced by strong support of free trade and Kantei diplomacy to lead in setting rules for the trade regime complex. Once dependent on the United States and mired in bilateral trade friction, Japan has emerged as an active supporter of engagement with China and pursuit of free trade agreements alongside continued commitment to the multilateral rules. Japanese-style trade adjustment and the slow path to liberalization served to balance economic efficiency with political stability as the government has supported narrow interests ...

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  • Authors: Chip Huyen (2022)

  • Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives

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  • Authors: Maria Schuld (2021)

  • This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data.

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  • Authors: Houneida Sakly (2022)

  • This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others.

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  • Authors: Ghita K. Mostefaoui (2022)

  • Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that the next generation healthcare services are sprouting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services would improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of ch...

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  • Authors: Miguel Morales (2021)

  • This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.