AlphaGo Simplified: Rule-Based AI and Deep Learning in Everyday Games - Image 1

£115.00

Chapman and Hall/CRC AlphaGo Simplified: Rule-Based AI and Deep Learning in Everyday Games

Name: AlphaGo Simplified: Rule-Based AI and Deep Learning in Everyday Games
Brand: Chapman and Hall/CRC
Price: 115.0 GBP
Availability: InStock

AI & Machine Learning

This is the most expensive it has ever been. Walk away.

£115 today · previous high £115 · all-time low £110

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 243 days • 243 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 243 days • 4 price levels

Days at Price

Current Price

Price Analysis

Most common price: £112 (154 days, 63.4%)

Price range: £110 - £115

Price levels: 4 different prices over 243 days

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 Specifications

Brand: Chapman and Hall/CRC
Format: Hardcover
ASIN: 1032722215
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 27 August 2024
Listed Since: 22 February 2024

Barcode

No barcode data available

Chapman and Hall/CRC AlphaGo Simplified: Rule-Based AI and Deep Learning in Everyday Games

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions: 156 (Intelligent Systems Reference Library, 156)

Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain

Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI, Volume 2: 956 (Studies in Computational Intelligence, 956)

Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications

The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python

Meta-Learning in Decision Tree Induction: 498 (Studies in Computational Intelligence, 498)

Basic Mathematical Foundations of AI: Hands on with Python (Mastering Machine Learning)

EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V: 288 (Advances in Intelligent Systems and Computing, 288)

AI-Based Services for Smart Cities and Urban Infrastructure

Algorithms for Decision Making

Recent Advances in Soft Computing and Cybernetics: 403 (Studies in Fuzziness and Soft Computing, 403)

Biologically Inspired Techniques in Many-Criteria Decision Making: International Conference on Biologically Inspired Techniques in Many-Criteria ... and Analytics in Intelligent Systems, 10)

Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 3: 296 (Lecture Notes in Networks and Systems, 296)

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Pairwise Comparison Matrices and their Fuzzy Extension: Multi-criteria Decision Making with a New Fuzzy Approach: 366 (Studies in Fuzziness and Soft Computing, 366)

AI: All About the AI World

Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers

Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, ... in Intelligent Systems and Computing, 1043)

New Trends in Computational Collective Intelligence: 572 (Studies in Computational Intelligence, 572)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Intelligent Systems: 10th Brazilian Conference, BRACIS 2021, Virtual Event, November 29 – December 3, 2021, Proceedings, Part II: 13074 (Lecture Notes in Computer Science, 13074)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches