Practical Artificial Intelligence 免积分下载

实用AI:从理论到实践
本书深入浅出地介绍了人工智能的基本概念和技术,包括神经网络、机器学习、多代理系统等,通过具体案例展示了如何将AI应用于现实世界的项目中,适合各水平读者。

图书说明:

了解所有级别的人工智能(AI)如何出现在最难以想象的普通生活场景中。本书探讨了诸如神经网络,代理,多代理系统,监督学习和无监督学习等主题。这些和其他主题将通过实际示例进行解决,因此您可以使用AI解决方案学习基本概念并将其应用于您自己的项目。

人们倾向于将人工智能视为神秘而与他们日常生活无关的东西。实用人工智能提供简单的解释和实施指示。本书不是专注于理论和过于科学的语言,而是使各级实践者不仅能够学习人工智能,还能实现其实际用途。

你将学到什么

  • 了解代理商和多代理商以及它们的合并方式
  • 机器学习如何与现实世界的问题及其对您的意义有关
  • 在现实世界中应用有监督和无监督的学习技巧和方法
  • 实施强化学习,游戏编程,模拟和神经网络

本书适用于谁

对AI及其应用感兴趣的计算机科学专业学生,专业人士和业余爱好者。

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下载地址:Practical Artificial Intelligence

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Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this book, we will explore various real-world scenarios. We will understand what algorithms to use in a given context and write functional code using this exciting book. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques. You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue! What this book covers Chapter 1, Introduction to Artificial Intelligence, teaches you various introductory concepts in artificial intelligence. It talks about applications, branches, and modeling of Artificial Intelligence. It walks the reader through the installation of necessary Python packages. Chapter 2, Classification and Regression Using Supervised Learning, covers various supervised learning techniques for classification and regression. You will learn how to analyze income data and predict housing prices. Chapter 3, Predictive Analytics with Ensemble Learning, explains predictive modeling techniques using Ensemble Learning, particularly focused on Random Forests. We will learn how to apply these techniques to predict traffic on the roads near sports stadiums. Chapter 4, Detecting Patterns with Unsupervised Learning, covers unsupervised learning algorithms including K-means and Mean Shift Clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.
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