Hamilton is confused and frightened. Heading he knows not where. The nightmare from which he is trying to escape will follow him wherever he goes. Salvation will come but not before tribulation...

A young Englishman is posted to an infantry battalion in France late in the winter of 1917-1918. After some months in the trenches he is attached to the Royal Flying Corps. He is wounded and falls in love with his nurse. Once fit again he returns to France where he briefly encounters a German pilot - a prisoner. By chance they meet again in the 1920s, at a time of desperate chaos for Germany. In 1940 their paths cross once more. The Battle of Britain is over but the Nazis still entertain hopes of invading and defeating Britain. The Englishman is now a civilian police inspector. He stumbles on a German plan to prepare the way for invasion. Its organisation and execution has been entrusted to his former acquaintance - the prisoner of 1918.

Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine learning and statistics tools in a practical fashion, using black-box solutions and case studies instead of a traditional math-heavy presentation. By exploring each problem in this book in depth - including both viable and hopeless approaches - you'll learn to recognize when your situation closely matches traditional problems. Then you'll discover how to apply classical statistics tools to your problem. Machine Learning for Hackers is ideal for programmers from private, public, and academic sectors.

Leading scholars in the field examine the highly topical issue of the future the welfare state in Europe. They argue that welfare states need to adjust, and examine which kind of welfare architecture will further Europe's stated goal of maximum social inclusion and justice. The volume concentrates on four principal social policy domains; the aged and transition to retirement; the welfare issues related to profound changes in working life; the new risks and needs that arise in households and, especially, in child families; and the challenges of creating gender equality.

If you're an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. You'll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation.

This book also includes a short tutorial on using the popular R language to manipulate and analyze data. You'll get clear examples for analyzing sample data and writing machine learning programs with R.Mine email content with R functions, using a collection of sample filesAnalyze the data and use the results to write a Bayesian spam classifierRank email by importance, using factors such as thread activityUse your email ranking analysis to write a priority inbox programTest your classifier and priority inbox with a separate email sample set

This book shows you how to run experiments on your website using A/B testing - and then takes you a huge step further by introducing you to bandit algorithms for website optimization. Author John Myles White shows you how this family of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which have previously only been described in research papers. You'll learn about several simple algorithms you can deploy on your own websites to improve your business including the epsilon-greedy algorithm, the UCB algorithm and a contextual bandit algorithm. All of these algorithms are implemented in easy-to-follow Python code and be quickly adapted to your business's specific needs. You'll also learn about a framework for testing and debugging bandit algorithms using Monte Carlo simulations, a technique originally developed by nuclear physicists during World War II. Monte Carlo techniques allow you to decide whether A/B testing will work for your business needs or whether you need to deploy a more sophisticated bandits algorithm.

The redistributive state is fading in Canada. Government programs are no longer offsetting the growth in inequality generated by the market. In this book, leading political scientists, sociologists, and economists point to the failure of public policy to contain surging income inequality. A complex mix of forces has reshaped the politics of social policy, including global economic pressures, ideological change, shifts in the influence of business and labour, changes in the party system, and the decline of equality-seeking civil society organizations. This volume demonstrates that action and inaction - policy change and policy drift - are at the heart of growing inequality in Canada.