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Born in Leighlinbridge in Ireland, John Tyndall (1820-93) was a brilliant nineteenth-century experimental physicist and gifted science educator. He worked initially as a draughtsman, then spent a year teaching at an English school before attending the University of Marburg to study physics and chemistry. Tyndall carried out important research on magnetism, light and bacteriology. Among his many significant achievements, he demonstrated the greenhouse effect in Earth's atmospheric gases using absorption spectroscopy. He was a skilled and entertaining educator and as Professor of Natural Philosophy at the Royal Institution he gave many public lectures and demonstrations of science. In this engaging potpourri of essays published in 1893, Tyndall's prose enlivens subjects as diverse as the life of Louis Pasteur, observing the Sabbath, the prevention of phthisis (tuberculosis), personal experiences of Alpine mountaineering, and the science of rainbows.

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Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.

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This book constitutes the refereed proceedings of the 14th International Conference on Computational Collective Intelligence, ICCCI 2022, held in Hammamet, Tunisia, in September 2022.The 56 full papers and 10 short papers were carefully reviewed and selected from 420 submissions. The papers are grouped in topical sections on collective intelligence and collective decision-making; deep learning techniques; natural language processing; data minning and machine learning; knowledge engineering and semantic web; computer vision techniques; social networks and intelligent systems; cybersecurity and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; applications for industry 4.0.

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"A iniciação científica (IC) pode ser entendida sob duas perspectivas dentro do ensino superior. Na primeira, é um processo que abarca todas as experiências vivenciadas pelo aluno durante a graduação, com o objetivo de promover o seu envolvimento com a pesquisa e, consequentemente, sua formação científica, incluindo programas de treinamento, desenvolvimento de estudos sobre a metodologia científica (dentro de uma disciplina ou não), visitas programadas a institutos de pesquisa e a indústrias etc. Na segunda, adotada neste livro, a IC é definida como o desenvolvimento de um projeto de pesquisa elaborado e realizado sob orientação de um docente da universidade, executado com ou sem bolsa para os alunos. O conjunto de textos aqui reunidos promove a socialização de diferentes teorias e práticas sobre a IC no contexto de sua ação formadora, além de relatar percursos, discutir fundamentos e oferecer contribuições que permitam uma melhor orientação aos gestores e discentes participantes e interessados nessa atividade. Os ensaios tratam de amplo espectro de temas correlatos: as implicações e perspectivas da pesquisa nas universidades brasileiras; o desenvolvimento e a abrangência dos programas nacionais de pesquisa sobre a iniciação científica; os efeitos dos fatores socioeconômicos e culturais no acesso à bolsa de iniciação científica; o trabalho do professor pesquisador no PIBIC/CNPq; a bolsa de iniciação científica e os grupos de pesquisa; e, por fim, a pesquisa na graduação e sua importância no Programa Institucional de Bolsas de IC."

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