AlphaGo Simplified 1st Edition by Mark Liu PAPERBACK

Description: May 11, 1997, was a watershed moment in the history of artificial intelligence (AI): the IBM supercomputer chess engine, Deep Blue, beat the world Chess champion, Garry Kasparov. It was the first time a machine had triumphed over a human player in a Chess tournament. Fast forward 19 years to May 9, 2016, DeepMind’s AlphaGo beat the world Go champion Lee Sedol. AI again stole the spotlight and generated a media frenzy. This time, a new type of AI algorithm, namely machine learning (ML) was the driving force behind the game strategies. What exactly is ML? How is it related to AI? Why is deep learning (DL) so popular these days? This book explains how traditional rule-based AI and ML work and how they can be implemented in everyday games such as Last Coin Standing, Tic Tac Toe, or Connect Four. Game rules in these three games are easy to implement. As a result, readers will learn rule-based AI, deep reinforcement learning, and more importantly, how to combine the two to create powerful game strategies (the whole is indeed greater than the sum of its parts) without getting bogged down in complicated game rules. Implementing rule-based AI and ML in these straightforward games is quick and not computationally intensive. Consequently, game strategies can be trained in mere minutes or hours without requiring GPU training or supercomputing facilities, showcasing AI's ability to achieve superhuman performance in these games. More importantly, readers will gain a thorough understanding of the principles behind rule-based AI, such as the MiniMax algorithm, alpha-beta pruning, and Monte Carlo Tree Search (MCTS), and how to integrate them with cutting-edge ML techniques like convolutional neural networks and deep reinforcement learning to apply them in their own business fields and tackle real-world challenges. Written with clarity from the ground up, this book appeals to both general readers and industry professionals who seek to learn about rule-based AI and deep reinforcement learning, as well as students and educators in computer science and programming courses. Product details Publisher ‏ : ‎ Chapman and Hall Language ‏ : ‎ English Paperback ‏ : ‎ 408 pages ISBN-10 ‏ : ‎ 1032722126 ISBN-13 ‏ : ‎ 978-1032722122 Item Weight ‏ : ‎ 1.25 pounds Dimensions ‏ : ‎ 6.14 x 0.92 x 9.21 inches SHIPPING Shipping All items fast! The majority of orders are shipped using USPS Priority Mail and UPS Ground if requested. There is an option at checkout to add a Signature of Delivery for extra security & peace of mind. This means you need to sign for your parcel. If you are not home, the courier will take it to your local post office for collection. We also offer expedited shipments upon request, we will quote the shipment and let you know of the estimated cost. 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Any items returned to us may be subject to a 20% restocking fee. The refund price (after restocking and shipping fees are deducted) will be issued to you via the original method of payment for your purchase. FEEDBACK Our all customers are 100% satisfied. If you have any issues with your orders, please contact us to resolve it. We are happy to help you EXCHANGE CANCELLATION You need to send us an order cancellation request through eBay before the shipment made. once we have the request, we will approve it from our end and grant your full refund back to your account. CONTACT US Please send us an email via ebay. We will reply within 24 hours. Copyright © | All Rights Reserved

Price: 75.95 USD

Location: Philadelphia, Pennsylvania

End Time: 2024-11-29T16:05:33.000Z

Shipping Cost: 5.99 USD