We've been told this since the beginning of civilisation. But what if we stopped to question our antiquated system? Is it fair? And is it capable of serving the needs of our rapidly-changing, modern society? In Daylight Robbery, Dominic Frisby traces the origins of taxation, from its roots in the ancient world, through to today. He explores the role of tax in the formation of our global religions, the part tax played in wars and revolutions throughout the ages, why, at one stage, we paid tax for daylight or for growing a beard. Ranging from the despotic to the absurd, the tax laws of the past reveal so much about how we got to where we are today and what we can do to build a system fit for the future.'This entertaining, surprising, contrarian book is a tour de force!' - Matt Ridley, author of The Evolution of Everything'In this spectacular gallop through history, Frisby shows how taxation has warped, stunted and thwarted human progress' - Mark Littlewood, Director General, Institute of Economic Affairs'Against all expectations, Dominic's book on tax is a real page-turner. His historical interpretation and utopian ideas will outrage Left and Right. Both should read the book' - Steve Baker, MP for Wycombe and Member of the House of Commons Treasury Committee 'Fascinating book which exposes the political and economic basis of tax. A must read for those of us who believe in simpler, lower taxes' - Rt Hon Liz Truss, MP for South West Norfolk, Secretary of State for International Trade and President of the Board of Trade'Both amusing and informative, it's a romp' - Bill Bonner, author of Empire of Debt

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    A series of formal models are used to present and analyze important macroeconomic theories. The theories are supplemented by examples of relevant empirical work, which illustrate the ways that theories can be applied and tested. This well-respected and well-known text is unique in the marketplace.