Matt Cole - Böcker

Richard Wainwright, the Liberals and Liberal Democrats: Unfinished Business now available in paperback, offers new research on familiar themes involving loyalties of politics, faith and locality. Richard Wainwright was a Liberal MP for seventeen years during the Party’s recovery, but his life tells us about much more than this. Wainwright grew up in prosperity, but learned from voluntary work about poverty; he refused to fight in World War Two, but saw war at its cruellest; he joined the Liberal Party when most had given up on it, but gave his fortune to it; lost a by-election but caused the only Labour loss in Harold Wilson’s landslide of 1966. He then played a key role in the fall of Jeremy Thorpe, the Lib-Lab Pact and the formation of the SDP-Liberal Alliance and the Liberal Democrats; he represented a unique Yorkshire constituency which reflected his pride and hope for society; and though he gave his life to the battle to be in the Commons, he refused a seat in the Lords.Richard Wainwright's story is central to the story of the Liberal Party and sheds light on the reasons for its survival and the state of its prospects. At the same time this book is a parable of politics for anyone who wants to represent an apparently lost cause, who wants to motivate people who have been neglected, and who wants to follow their convictions at the highest level.

This textbook brings together an introduction to the political theory of democracy since Ancient times and a critical picture of its place in Britain today.The author examines the work of Plato and Aristotle, Rousseau and Mill, Marx and Weber, and locates them and others in the debate about what democracy means. He then scrutinises Britain’s claim to be a developing democracy, from the power of the Prime Minister and the role of political parties to the influence of pressure groups and the media, as well as recent constitutional changes.In the context of declining public trust in political institutions and increasing reluctance to vote, crucial questions are tackled: do we have a democracy, and why does it matter? Key Features:*A wide-ranging, accessible introduction to the place of Democracy in Britain today*Divided into two halves: on democratic theory (reflecting its history, development, and key concepts) and democratic practice (examining political institutions)*Offers examples of documentary material to illustrate the ideas presented*Up-to-date: includes material written after the 2005 General Election

Looks at parties' organisation, policy, support and impact, from the major parties to the localThis introductory textbook examines the factors contributing to parties' fortunes and identities, and the causes of recent changes in both. Parties studied include: The main parties: Conservatives and Labour The minor parties: the Liberal Democrats, the Scottish National Party, Plaid Cymru and the GreensThe peripheral parties: the BNP, UKIP, SSP and SLPLocal parties: Kidderminster Health and Hospital Concern, the Morecambe Bay Independents and Mebyon KernowHelpful student features include: A short, boxed paragraph of introduction and list of key issues covered in each chapter A brief conclusion summarising what you should have learnt from reading each chapter A glossary of key terms Sample examination questions Helpful websites and suggestions for further reading

Looks at parties' organisation, policy, support and impact, from the major parties to the localThis introductory textbook examines the factors contributing to parties' fortunes and identities, and the causes of recent changes in both. Parties studied include: The main parties: Conservatives and Labour The minor parties: the Liberal Democrats, the Scottish National Party, Plaid Cymru and the GreensThe peripheral parties: the BNP, UKIP, SSP and SLPLocal parties: Kidderminster Health and Hospital Concern, the Morecambe Bay Independents and Mebyon KernowHelpful student features include: A short, boxed paragraph of introduction and list of key issues covered in each chapter A brief conclusion summarising what you should have learnt from reading each chapter A glossary of key terms Sample examination questions Helpful websites and suggestions for further reading

Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorchKey FeaturesBuild practical, real-world AI projects on Android and iOSImplement tasks such as recognizing handwritten digits, sentiment analysis, and moreExplore the core functions of machine learning, deep learning, and mobile visionBook DescriptionWe’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision.This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms.By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.What you will learnExplore the concepts and fundamentals of AI, deep learning, and neural networksImplement use cases for machine vision and natural language processingBuild an ML model to predict car damage using TensorFlowDeploy TensorFlow on mobile to convert speech to textImplement GAN to recognize hand-written digitsDevelop end-to-end mobile applications that use AI principlesWork with popular libraries, such as TensorFlow Lite, CoreML, and PyTorchWho this book is forMobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.

Create and unleash the power of neural networks by implementing C# and .Net codeKey FeaturesGet a strong foundation of neural networks with access to various machine learning and deep learning librariesReal-world case studies illustrating various neural network techniques and architectures used by practitionersCutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many moreBook DescriptionNeural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks.This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search.Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.What you will learnUnderstand perceptrons and how to implement them in C#Learn how to train and visualize a neural network using cognitive servicesPerform image recognition for detecting and labeling objects using C# and TensorFlowSharpDetect specific image characteristics such as a face using Accord.NetDemonstrate particle swarm optimization using a simple XOR problem and EncogTrain convolutional neural networks using ConvNetSharpFind optimal parameters for your neural network functions using numeric and heuristic optimization techniques.Who this book is forThis book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book