Why do people go to museums and what do they learn there? What roles can museums serve in a learning community? How can museums facilitate more effective learning experiences? John H. Falk and Lynn D. Dierking investigate these questions in Learning from Museums. Synthesizing theories and research from a wide range of disciplines, including psychology, education, anthropology, ...(展开全部)

作者簡介 Wilfred R. Bion（畢昂） 畢昂1897年生於印度，八歲始至英國接受學校教育。第一次世界大戰期間，他於法國服役，擔任戰車指揮官，並獲頒卓越服務勳章以及榮譽勳位。在牛津的皇后學院攻讀歷史之後，他轉往倫敦的大學學院研習醫學。因為對精神分析的興趣日益增加，他開始接受瑞克曼的訓練分析，之後則由克萊因接手。在1940-1950年期間，他專注於團體過程的研究。後來他放棄這個領域而轉向精神分析的實務工作，之後擔任倫敦精神分析診所主任（1956-1962）以及英國精神分析學會會長（1962-1965）。1968年起，他到洛杉磯工作，1979年回到英國，兩個月後過世。 譯者簡介 劉時寧　精神科專科醫師 現任：培靈醫院醫師 仁愛醫院精神科合約醫師 呂旭立紀念文教基金會諮商師

这本热情洋溢的信息理论基础入门书从经典香农理论到统计学习中的现代应用，为学生进一步学习打下了独特、全面而严谨的基础。本书使用独特的有限块长度方法介绍了数据压缩、信道编码和速率失真理论等核心主题。本书通过超过 210 个课后练习和大量示例，向学生介绍了统计学、机器学习和现代通信理论的当代应用。本书介绍了信息理论方法在统计学习和计算机科学中的应用，例如 f 散度、PAC Bayes 和变分原理、Kolmogorov 度量熵、强数据处理不等式和统计估计的熵上界。本书附有教师解决方案手册和关于信息理论中更专业主题的额外独立章节，是电气工程、统计学和计算机科学专业高年级本科生和研究生的理想入门教科书。 ·系统地处理统计学习和高维统计中的信息论技术 ·为连续和离散变量开发信息论，提供与统计和机器学习应用相关的示例 ·重点关注有限块长度（非渐近）结果，让学生掌握 ...(展开全部)

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have ...(展开全部)

This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization fo...(展开全部)

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have ...(展开全部)

This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization fo...(展开全部)

This book will provide a solid foundation in how Deep Learning works so that no concept you learn or project you do in the field will seem daunting after this. We will walk the reader through how to implement multi-layer neural networks, implementing convolutional neural networks and recurrent neural networks from scratch. Using these as building blocks, readers will learn to t...(展开全部)

David Spiegelhalter is a British statistician and Chair of the Winton Centre for Risk and Evidence Communication in the Statistical Laboratory at the University of Cambridge. He was also elected as President of the Royal Statistical Society for 2017-18. In addition to presenting documentaries on BBC4, he has appeared on Infinite Monkey Cage, BBC Horizon, and the Life Scientific...(展开全部)

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform t...(展开全部)