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 English | 2022 | ISBN: 3031791665, 978-3031791666 | 110 pages | True PDF | 8.09 MB Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning, (2) data is scarce, and (3) automation has limitations in the real-world. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist with readers gaining a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not technically proficient we include simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, the book illustrates the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.  English | 2022 | ISBN: 1032125608 | 181 pages | True PDF | 11.19 MB This book puts successful startup tools in the hands of creators: performers, artists, entertainers, creatives, and media makers seeking to launch like a business and generate more income.  English | 2021 | ISBN: 9383305533 | 230 pages | True PDF | 71.02 MB The book is a reference book useful for undergraduates, postgraduates and research scholars of biological, ecological and medical sciences. The purpose of writing this book is to provide an accessible reference book on statistical techniques whose proper use will help students in withdrawing accurate results and able to interpret them logically. The methods described in this book are, of course, the same as those used in different disciplines, but things are made so user's friendly that even general readers will find this book useful. The s of the book have been organized in such a way that suits the course curriculum of various universities. In this book enough materials are provided to cover statistical techniques with examples which are ecological and biological based.  English | 2022 | ISBN: 1032132051 | 273 pages | True PDF | 5.15 MB Applied Positive School Psychology is an essential guide to help teachers regain their own and assist the school community in rebuilding their health post-pandemic. While research in positive psychology is thriving, teachers and educational practitioners find it challenging to apply it in their daily practice. This practical book fills the gap between theory and practice and provides practitioners with an evidence-based toolkit on using the positive psychology in their school communities.
 English | 2022 | ISBN: 9811915113 | 385 pages | True PDF EPUB | 8.5 MB This book increases the accessibility of philosophical concepts to a wider audience within medical education, translating 'knowing' to 'doing.' It prompts health professions educators and researchers to consider the dynamics and structure of contemporary issues within health professions education in new, philosophical ways. Through considering the practical implications of applying philosophical concepts to contemporary issues, the book recommends avenues for further research and pedagogical change. Individual educators are considered, with practice points for teaching generated within each chapter.  English | 2022 | ISBN: 1642830895 | 341 pages | True PDF EPUB | 7.42 MB After a decades-long economic slump, the city of Flint, Michigan, struggled to address chronic issues of toxic water supply, malnutrition, and food security gaps among its residents. A community-engaged research project proposed a resilience assessment that would use panarchy theory to move the city toward a more sustainable food system. Flint is one of many examples that demonstrates how panarchy theory is being applied to understand and influence change in complex human-natural systems. Applied Panarchy , the much-anticipated successor to Lance Gunderson and C.S. Holling's seminal 2002 volume Panarchy , documents the extraordinary advances in interdisciplinary panarchy scholarship and applications over the past two decades. Panarchy theory has been applied to a broad range of fields, from economics to law to urban planning, changing the practice of environmental stewardship for the better in measurable, tangible ways.  English | 2022 | ISBN: 1119716705 | 585 pages | True PDF | 44.63 MB APPLIED MEDICAL STATISTICS  English | 2022 | ISBN: 1119722691, 978-1119722694 | 685 pages | True PDF | 22.52 MB APPLIED BIOSTATISTICS FOR THE HEALTH SCIENCES APPLIED BIOSTATISTICS FOR THE HEALTH SCIENCES  English | 2022 | ISBN: 1450395910 | 308 pages | True PDF | 96.41 MB Affective computing is a nascent field situated at the intersection of artificial intelligence with social and behavioral science. It studies how human emotions are perceived and expressed, which then informs the design of intelligent agents and systems that can either mimic this behavior to improve their intelligence or incorporate such knowledge to effectively understand and communicate with their human collaborators. Affective computing research has recently seen significant advances and is making a critical transformation from exploratory studies to real-world applications in the emerging research area known as applied affective computing.  English | 2022 | ISBN: 1799877760, 978-1799877776 | 293 pages | True PDF EPUB | 46.28 MB Modes and models of learning and instruction have shown a significant shift from yesterday's conventional learning and teaching given this era's current educational and social contexts. Learners are no longer learning and communicating with human-generated, computed, and mediated-or traditional-learning and instructional practices, paving the way for machine-facilitated communication, learning, and teaching tools. Learning and instruction, communication and information exchange, as well as gathering, coding, analyzing, and synthesizing data have proven to be in need of even more innovative technology-moderated tools. Applications of Machine Learning and Artificial Intelligence in Education focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Covering topics such as data coding and social networking technology, it is ideal for learners with an interest in the deep learning discipline, educators, educational technologists, instructional designers, and data evaluators, as well as special interest groups (SGIs) in the discipline. |
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