James Baird Or The Basket Maker S Son Classic Reprint

Excerpt from James Baird, or the Basket-Maker's Son The village - Wickedness abounds - Sunday School formed - Story of the unfortunate widow - Death of a widow's daughter - Sanc. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.

Ambition will fuel him. Competition will drive him. But power has its price. It is the morning of the reaping that will kick off the tenth annual Hunger Games. In the Capitol, eighteen-year-old Coriolanus Snow is preparing for his one shot at glory as a mentor in the Games. The once-mighty house of Snow has fallen on hard times, its fate hanging on the slender chance that Coriolanus will be able to outcharm, outwit, and outmaneuver his fellow students to mentor the winning tribute. The odds are against him. He's been given the humiliating assignment of mentoring the female tribute from District 12, the lowest of the low. Their fates are now completely intertwined - every choice Coriolanus makes could lead to favor or failure, triumph or ruin. Inside the arena, it will be a fight to the death. Outside the arena, Coriolanus starts to feel for his doomed tribute . . . and must weigh his need to follow the rules against his desire to survive no matter what it takes.

A NEW YORK TIMES BESTSELLER 'Inspirational and essential' Bret Easton Ellis, author of American Psycho 'Poignant and tragic' The Spectator 'Easily the most remarkable non-fiction book about drugs and drug taking since Hunter S Thompson's Fear and Loathing in Las Vegas' Observer James Frey wakes up on a plane, with no memory of the preceding two weeks. His face is cut and his body is covered with bruises. He has no wallet and no idea of his destination. He has abused alcohol and every drug he can lay his hands on for a decade - and he is aged only twenty-three. What happens next is one of the most powerful and extreme stories ever told. His family takes him to a rehabilitation centre. And James Frey starts his perilous journey back to the world of the drug and alcohol-free living. His lack of self-pity is unflinching and searing. A Million Little Pieces is a dazzling account of a life destroyed and a life reconstructed. It is also the introduction of a bold and talented literary voice.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

A famous puppeteer gives a fascinating introduction to his art.