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The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop

By : Anthony So , William So , Nagy
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The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop

5 (1)
By: Anthony So , William So , Nagy

Overview of this book

You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You will then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you’ll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you’ll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you’ll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.
Table of Contents (8 chapters)
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Preface

Python for Game AI

An AI game player is nothing but an intelligent agent with a clear goal: to win the game and defeat all the other players. AI experiments have achieved surprising results when it comes to games. Today, no human can defeat an AI in the game of chess.

The game Go was the last game where human players could consistently defeat a computer player. However, in 2017, Google's game-playing AI called AlphaGo defeated the world number 1 ranked Go player.

Intelligent Agents in Games

An intelligent agent plays according to the rules of the game. The agent can sense the current state of the game through its sensors and can evaluate the potential steps. Once the agent finds the best possible step, it performs the action using its actuators. The agent finds the best possible action to reach the goal based on the information it has. Actions are either rewarded or punished. The carrot and stick are excellent examples of rewards and punishment. Imagine a donkey in front...

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