通过实践学习人工智能（感知器、神经网络和贝叶斯人工智能）
我即将参加人工智能课程，我想先练习一下。我正在使用一本书来学习理论，但是任何语言的资源和具体示例都可以帮助实践，这将是令人惊奇的。谁能给我推荐一些有大量示例和教程的好网站或书籍？
谢谢 ！
编辑：我的课程将涉及感知器、神经网络和贝叶斯人工智能。
I'm about to take a course in AI and I want to practice before. I'm using a book to learn the theory, but resources and concrete examples in any language to help with the practice would be amazing. Can anyone recommend me good sites or books with plenty of examples and tutorials ?
Thanks !
Edit: My course will deal with Perceptrons, Neural networks and Bayesian AI.
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实际上取决于您想专注于哪个领域。有启动-资源：是
此处。我从他们的例子中了解了神经网络。您能详细说明一下它应该是什么样的人工智能吗？
啊，我忘了：此链接是一个非常好的论坛您可以在其中查看其他人遇到的问题并从中学习。
干杯。
Really depends on what area you want to specialize on. There is the startup - resource : is
here. I learned about neural nets from their example. Can you elaborate what kind of AI it should be?
Ah and i forgot: this link is a very nice forum where you can look at problems other people have and learn from that.
Cheers.
我的建议是通过尝试自己实现各种类型的学习器来学习它。看看您是否可以找到与您的某些兴趣（体育、游戏、健康等）相关的数据集，然后尝试创建一个学习器来进行某种分类（预测体育比赛中的获胜者，学习如何分类双陆棋位置、根据患者数据检测癌症等）使用不同的方法。如果决策树是您未来课程作业的一部分，那么从决策树开始，因为它们相对简单，然后继续学习神经网络。
My advice would be to learn it by trying to implement the various types of learners yourself. See if you can find yourself a dataset related to some interest you have (sports, games, health, etc.) and then try and create a learner to do some kind of classification (predicting a winner in a sports game, learning how to classify backgammon positions, detecting cancer based on patient data, etc.) using different methods. Start with Decision Trees if that's part of your future course work since they're relatively simple, then move on to neural networks.
这里有一组来源，我强烈推荐其中的每一个——因为解释的质量、代码的质量以及算法演示的“完整性”。
最小二乘回归< /a>
(Python)
k 均值聚类 (Python)
多层感知器 (Python)
Hopfield 网络 (Python)
决策树（ID3 和 C4.5）
此外，Hastie 的优秀教科书 Elements of Statistical Learning ，等人。实际上是免费下载的。作者有一个随本教科书附带的 R 包，其中包含所有代码。本书详细讨论了大多数（如果不是全部）主要的 ML 算法类别，并提供了涉及工作代码和“现实世界”数据的具体示例。
Here is a set of sources, each one of which i recommend highly--for the quality of the explanation, for the quality of the code, and for the 'completeness' of the algorithm demo.
Least-Squares Regression
(Python)
k-means clustering (Python)
Multi-Layer Perceptron (Python)
Hopfield Network (Python)
Decision Tree (ID3 & C4.5)
In addition, the excellent textbook Elements of Statistical Learning by Hastie, et al. is actually free to download. The authors have an R package that accompanies this textbook which contains all of the code. This book includes detailed discussion of most (if not all) of the major classes of ML algorithms, with specific examples that refer to working code and 'real-world' data.
就我个人而言，我会推荐这本M.Tim.Jones关于人工智能的书。
关于 AI 的主题很多，几乎每种类型的 AI 讨论都后面有 C 示例代码。确实是一本关于人工智能的非常实用的书！
Personally I would recommend this M.Tim.Jones book on AI.
Has many many topics on AI and almost every type of AI discussion is followed by C example code. Very pragmatic book on AI indeed !!
罗素和Norvig 对广阔的领域进行了很好的调查。
Russel & Norvig have a good survey of the broad field.