In November 2008, John Hattie's ground-breaking book Visible Learning synthesised the results of more than fifteen years research involving millions of students and represented the biggest ever collection of evidence-based research into what actually works in schools to improve learning. Visible Learning for Teachers takes the next step and brings those ground breaking concepts...(展开全部)

In November 2008, John Hattie's ground-breaking book Visible Learning synthesised the results of more than fifteen years research involving millions of students and represented the biggest ever collection of evidence-based research into what actually works in schools to improve learning. Visible Learning for Teachers takes the next step and brings those ground breaking concepts...(展开全部)

For over two decades, Skills for Preschool Teachers has prepared teachers, teaching assistants, college students, classroom volunteers, and CDA candidates to work with children three-to-five years of age in preschools, center-based child care, Head Starts, and pre-kindergartens. The skills for working with young children are presented in 13 easily-readable, fact-filled chapte...(展开全部)

An invaluable resource helping teachers at all levels of experience to develop their understanding of English grammar. Grammar for English Language Teachers is designed to help practising and trainee teachers to develop their knowledge of English grammar systems. It encourages teachers to appreciate factors that affect grammatical choices, and evaluates the 'rules of thumb' pr...(展开全部)

雷夫•艾斯奎斯（Rafe Esquith） 总统国家艺术奖 全美最佳教师奖 美国著名杂志《父母》年度“成长奖” 美国媒体天后欧普拉“善待生命奖” 英国女王颁发的不列颠帝国勋章 1981年毕业于加州大学洛杉矶分校，现为美国洛杉矶市霍伯特小学的五年级教师。 30年来，雷夫•艾斯奎斯老师一直在霍伯特小学担任五年级的老师，该校高达九成的学生家庭贫困，且多出自非英语系的移民家庭。可是，就是在这样恶劣的环境下，雷夫老师创造了轰动全美的教育奇迹，被《纽约时报》尊称为“天才与圣徒”。 在第56号教室里，雷夫老师独创的阅读、数学等基础课程深受孩子们喜欢，他们如着迷般每天提前2小时到校，放学后数小时内仍不愿离去。 学生不仅取得高居全美标准化测试前5%的好成绩，而且雷夫还引导他们的人格成长，以“成功无捷径”作为学生的指导思想；他所提倡的“有自己的行为准则并奉行不悖”甚至与...(展开全部)

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.