converted pdf, Book description

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.

Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.

* Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning
* Set up and manage machine learning projects end-to-end
* Build an anomaly detection system to catch credit card fraud
* Clusters users into distinct and homogeneous groups
* Perform semisupervised learning
* Develop movie recommender systems using restricted Boltzmann machines

* Generate synthetic images using generative adversarial networks

转换为pdf, 书籍描述

许多业内专家认为无监督学习是人工智能的下一个前沿领域，它可能是通用人工智能的关键。由于世界上大多数的数据都是无标签的，所以传统的监督学习无法应用。另一方面，无监督学习可以应用于无标签的数据集，以发现埋藏在数据深处的有意义的模式，这些模式对于人类来说可能是几乎不可能发现的。

作者Ankur Patel告诉你如何使用两个简单的、可用于生产的Python框架来应用无监督学习。Scikit-learn和使用Keras的TensorFlow。通过代码和实践案例，数据科学家将识别数据中难以找到的模式，并获得更深入的业务洞察力，检测异常情况，执行自动特征工程和选择，并生成合成数据集。你只需要编程和一些机器学习经验就可以开始了。

* 比较不同的机器学习方法的优缺点：监督、无监督和强化学习

* 设置和管理机器学习项目的端到端

* 构建一个异常检测系统来捕捉信用卡欺诈行为

* 将用户聚类为不同的同质群体

* 进行半监督学习

* 使用限制性玻尔兹曼机开发电影推荐系统

* 使用生成式对抗网络生成合成图像