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Five years ago, Don Snyder was teaching English at Colgate University. He was forty years old and had a wife, three children, a new baby on the way, and what seemed like a secure middle-class future. But then Snyder lost his chance at tenure -- and, all of a sudden, he was out of a job. The Cliff Walk is a moving, clear-eyed account of Snyder's agonizing loss and what it feels like to fall, rung by rung, down the socio-economic ladder. Snyder chronicles the denial and disbelief he went through as his hopes of finding another teaching job faded after being rejected for ninety positions. He explains how each painful change -- selling his house, buying groceries with food stamps -- reminded him how much he and his family had taken for granted in their previous life. And he describes how he finally found new hope in a job on a home construction crew in Maine. Working outside for ten hours a day through a vicious winter taught Snyder about his own cowardice and the lies he had come to believe about what a professional life of hard work entitled him to. Written with precision and elegance, The Cliff Walk captures the depth of one family's love and speaks to anyone who has ever wondered what it would be like to be out of a job and out in the cold.
A revealing tour of the opulent Newport Mansions where the Astors, Vanderbilts, and other Gilded Age families spent their summers. At the turn of the twentieth century, the wealthy families of New York would vacation at their summer homes in Newport, Rhode Island. Where the salty air once mingled with the laughter of society women in ball gowns, the houses of the Newport Cliff Walk still preside in grandeur over the crashing waves below. From the grand majesty of the Breakers to the beautiful proportions of Rosecliff, these houses are enduring reminders of the architectural flowering of the Gilded Age. Walking along the paved trail, it's easy to imagine the faintest hint of a waltz coming from the windows of Beechwood, or to envision the Duchess of Windsor’s carriage arriving for a visit at Fairholme. Ed Morris takes you on a tour of twenty-four historic mansions and landmarks, entertaining along the way with tales of splendor and style, social maneuvering and matchmaking.
Liam Mulligan, an old-school investigative reporter, finds himself drawn into Rhode Island's thriving sex business that involves legal prostitution and some very illegal pornography, pedophilia, and government corruption.
God only knows what possessed Bill Bryson, a reluctant adventurer if ever there was one, to undertake a gruelling hike along the world's longest continuous footpath—The Appalachian Trail. The 2,000-plus-mile trail winds through 14 states, stretching along the east coast of the United States, from Georgia to Maine. It snakes through some of the wildest and most spectacular landscapes in North America, as well as through some of its most poverty-stricken and primitive backwoods areas. With his offbeat sensibility, his eye for the absurd, and his laugh-out-loud sense of humour, Bryson recounts his confrontations with nature at its most uncompromising over his five-month journey. An instant classic, riotously funny, A Walk in the Woods will add a whole new audience to the legions of Bill Bryson fans.
Ex-con ruins racist gang! He pranks the hell out of them. Comic, yet touching. (This is subtle literary fiction.) Members of an all-white prison gang threaten Patrick, a white ex-con who deals meth in San Diego and who likes all races. Patrick quits crime and befriends the police. Then he and a con artist legally prank and harass the racists. When a law-abiding Asian American woman informs Patrick that he got her pregnant, he immediately marries her. Asians now in his family, Patrick destroys the racist gang, pranking them with a vengeance, until they get hurt, go straight, or get arrested. Review: Watch out! Eric Hertz is a master of satire. Pranked Straight is a scathing lampoon, and Eric's wit is extra dry (so bitterly dry you might not notice it). But the story is rolling, fluid, and exciting. This is an interracial adventure. It's like George Orwell meets Joseph Wambaugh (with a dash of Harper Lee). But this story is written in a modern style, set in San Diego. I especially admire the endearing way Mr. Hertz portrays Mexican gangsters in this novel. Juan Neptune - Mexico City Literary Club
An indefatigable walker, David Bathurst has unlaced his boots to produce this invaluable companion to the fifteen best-loved long-distance footpaths of Great Britain. His appreciation of the British countryside and light-hearted style will appeal to novice and experienced walkers alike.
Describes major tourist attractions in Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont, providing expanded coverage of Hartford, Boston, and Cape Cod.
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.