If white boys can't jump, can Asian kids play cricket? Lan's fed up with being called a nip. He wants to be a true-blue Aussie. What better way than by playing the greatest Anglo game of all? Lan gathers a team together and defiantly gives it a name: NIPS XI. Now all they have to do is get some equipment, find a coach, get themselves a sponsor and learn the rules of the game. Then it's time to challenge the best cricket team in the district. A funny, empowering story of cricket and curry, spinners and leggies, that is about overcoming cultural barriers, in sport and in life. Also by Ruth Starke, NIPS GO NATIONAL is the sequel to the bestselling NIPS XI.

If white boys can't jump, can Asian kids play cricket? Lan's fed up with being called a nip. He wants to be a true-blue Aussie. What better way than by playing the greatest Anglo game of all? Lan gathers a team together and defiantly gives it a name: NIPS XI. Now all they have to do is get some equipment, find a coach, get themselves a sponsor and learn the rules of the game. Then it's time to challenge the best cricket team in the district. A funny, empowering story of cricket and curry, spinners and leggies, that is about overcoming cultural barriers, in sport and in life. Also by Ruth Starke, NIPS GO NATIONAL is the sequel to the bestselling NIPS XI.

In this sequel to the bestselling NIPS XI, the cricket team, together with coach Spinner McGinty, are in Melbourne to compete for the Harmony Cup, and Lan has a lot on his mind: a new star batsman with something to hide, a mystery from Spinner?s past, conflict with his best mate, Izzy, and his fast bowler laid low just before their biggest match ever. Will Lan resolve his problems? Will the Nips get it together in time? Is the Pop Catholic?

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

In this guide, two experienced school librarians provide a selection of books for librarians, teachers and parents. The Fiction Gateway is an essential resource that supports individual, group and social reading program and provides an instant guide to matching children's interests with suitable reading material.

Many students coming to grips with Kant's philosophy are understandably daunted not only by the complexity and sheer difficulty of the man's writings, but almost equally by the amount of secondary literature available. A great deal of this seems to be - and not only on first reading - just about as difficult as the work it is meant to make more accessible. Any writer deliberately setting out to provide an authentically introductory text thus faces a double problem: how to provide an exegesis which would capture some of the spirit of the original, without gross and misleading over-simplification; and secondly, how to anchor the argument in the best and most imaginative secondary literature, yet avoid the whole project appearing so fragmented as to make the average book of chess openings seem positively austere. Until fairly recently, matters were made even more difficul t, in that commentaries on Kant were very often of a whole work, say, The Critique of Pure Reason, with the result that students would have to struggle through a very great deal of material indeed in order to feel any confidence at all that they had begun to understand the original writings. Recently, things have changed somewhat. There are now excellent commentaries on "Kant's Analytic", "Kant's Analogies" etc. . We have also seen, (at least as reflected in book titles), a resurgence of interest in what is perhaps the most controversial and far-reaching Kantian claim, viz.