Hands-on Machine Learning with Scikit-Learn,Keras TensorFlow
读书记录(缓慢更新)
目录
Part 1. The Fundamentals of Machine Learning
The Content of The Machine Learning Landscape
The Machine Learning Landscape
Part 1. The Fundamentals of Machine Learning
The Content of The Machine Learning Landscape
Part 1. The Fundamentals(fundament n.基础;臀部) of Machine Learning 机器学习的基础
1.The Machine Learning Landscape(n.景色;形势 v.对……做景观美化) 机器学习的前景
What Is Machine Learning? 什么是机器学习
Why Use Machine Learning? 为什么使用机器学习
Types of Machine Learning Systems 机器学习系统的类型
Supervised/Unsupervised(supervise v.监督) Learning 监督/无监督学习
Batch(n.一批 v.分批处理) and Online Learning 批处理和在线学习
Instance-Based Versus(与) Model-Based Learning 基于实例与基于模型的学习
Main Challenges of Machine Learning 机器学习的主要挑战
Insufficient(sufficient a.充足的) Quantity(n.数目;大量) of Training Data 训练数据不足
Nonrepresentative(represent v.代表) Training Data 非代表性训练数据
Poor-Quality Data 低质量数据
Irrelevant(relevant a.相关的;正确的;适宜的;有价值的) Features 无关的特征
Overfitting(overfit n.过拟合) the Training Data 过拟合训练数据
Underfitting(underfit n.欠拟合) the Training Data 欠拟合训练数据
Stepping(step n.迈步;脚步;梯级;台阶;步骤;措施;阶段;进程 v.跨步走;(短距离)移动;行走) Back 退一步?
Testing and Validating(validate v.批准;证实;确认……有效) 测试和验证
Hyperparameter(parameter n.界限;范围;参数;变量) Tuning(tune n.曲调;歌曲 v.调整;校音) and Model Selection 超参数调优和模型选择
Data Mismatch(match n.比赛;对手;配偶;婚姻 v.比得上;使相配) 数据不匹配
Exercises
The Machine Learning Landscape
With Early Release ebooks(n. 电子书), you get books in their earliest form-the author's raw and unedited content as he or she writes--so you can take advantage of(take advantage of... 利用...) these technologies long before the official release of these titles. The following will be Chapter 1 in the final release of the book.
When most people hear "Machine Learning," they picture(n. 图片;绘画;照片;肖像 v.想象;绘画;拍摄) a robot: a dependable butler(n. 管家) or a deadly Terminator(终结者) depending on who you ask. But Machine Learning is not just a futuristic(a. 未来主义的) fantasy, it's already here. In fact, it has been around for decades in some specialized applications(n. 申请书;应用;程序), such as Optical Character Recognition(OCR)(光学字符识别). But the first ML application that really became mainstream(n.主流 a.主流的 v.使主流化), improving the lives of hundredsof millions of people, took over(take over 接管;控制) the world back in the 1990s: it was the spam filter(垃圾邮件过滤器 spam n.垃圾邮件 v.向..群发垃圾邮件 filter n.过滤器;滤光器;滤声器;滤波器;过滤程序 v. 过滤;渗入;透过).Not exactly a self-aware(a. 有自我意识的) Skynet(天网 ？框架), but it does technically qualify as Machine Learning(it has actually learned so well that you seldom need to flag an email as spam anymore)(但从技术上讲，它在技术上符合机器学习(它实际上已经学得很好了，你几乎不需要把电子邮件标记为垃圾邮件了). It was followed by(followed by 后面有;接着是) hundreds of ML applications that now quietly power(驱动) hundreds of products and features that you use regularly(regular a. 常规的 n. 常客), from better recommendations(recommend v. 建议;劝告;推荐;介绍) to voice search(接着是数百个机器学习应用程序，这些应用程序现在悄悄地为您经常使用的数百种产品和功能提供支持，从更好的推荐到语音搜索).
Where does Machine Learning start and where does it end? What exactly does itmean for a machine to learn something? If I download a copy of Wikipedia, has mycomputer really “learned” something? Is it suddenly smarter? In this chapter we willstart by clarifying what Machine Learning is and why you may want to use it.
Then, before we set out to explore the Machine Learning continent, we will take alook at the map and learn about the main regions and the most notable landmarks:supervised versus unsupervised learning, online versus batch learning, instancebased versus model-based learning. Then we will look at the workflow of a typical Mlproject, discuss the main challenges you may face, and cover how to evaluate ancfine-tune a Machine Learning system.